The proliferation of multi-cloud architectures has introduced significant complexities in cyber security, necessitating advanced solutions to safeguard distributed infrastructures. Traditional security models often fall short in addressing the dynamic and heterogeneous nature of multi-cloud environments. Artificial Intelligence (AI) has emerged as a transformative force in enhancing threat detection capabilities, offering proactive and adaptive security measures. This paper explores the integration of AI - driven threat detection systems within multi-cloud frameworks, emphasizing their role in identifying and mitigating (...) security threats in real-time. AI technologies, particularly machine learning algorithms, enable the analysis of vast amounts of data across diverse cloud platforms, facilitating the detection of anomalous patterns and potential threats. By leveraging predictive analytics, AI systems can anticipate and neutralize threats before they manifest, thereby reducing the risk of data breaches and system compromises. Furthermore, AI enhances the efficiency of Security Information and Event Management (SIEM) systems by automating the correlation and analysis of security events, leading to faster incident response times. The implementation of AI - driven threat detection in multi-cloud environments also addresses challenges related to scalability and resource optimization. AI systems can dynamically adjust to varying workloads and security demands, ensuring consistent protection across all cloud platforms. This adaptability is crucial in maintaining robust security postures amidst the evolving threat landscape. In conclusion, the adoption of AI-driven threat detection systems in multi-cloud environments represents a significant advancement in cyber security practices. By providing proactive, scalable, and efficient security measures, AI contributes to the resilience and integrity of multi-cloud infrastructures, safeguarding organizations against an increasingly sophisticated array of cyber threats. (shrink)

The Real-Time Cyber Threat Detection and Response System is an intelligent security framework designed to proactively identify, analyze, and respond to cyber threats in real time. With the growing sophistication of cyberattacks targeting critical infrastructure, traditional static defense mechanisms are no longer sufficient. This system addresses that gap by leveraging machine learning algorithms and dimensionality reduction techniques such as Principal Component Analysis (PCA) to enable accurate and efficient threat detection. The system captures input data, such as (...) network traffic or log features, and processes it through a trained machine learning model to classify instances as normal or malicious. A pre-trained model, stored using Python's pickle module, evaluates the processed input, while PCA ensures that irrelevant or redundant features are minimized to boost performance and reduce computation time. The application is deployed as a web-based interface using Flask, providing functionalities like secure data upload, dataset preview, performance visualization, and threat prediction. Users can interact with the system in real time, upload network traffic datasets, and receive instant feedback on potential threats. The platform is built to support automated response mechanisms, which can be extended to alert generation, logging, or blocking malicious activities. (shrink)

Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much (...) more security resilience, decreasing possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One-Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. (shrink)

I put forward a novel cognitive architecture for pain, fear, and anxiety, according to which these three capacities are underpinned by a common threat-detection mechanism. This mechanism takes information about potential threats as input, assesses whether the threat is actual and, if it deems it is, outputs a threat representation. If this is correct, pain, fear, and anxiety turn out to be different manifestations of the same cognitive mechanism. I defend my proposal by discussing a large (...) swath of convergent evidence from different quarters of the sciences of the mind. (shrink)

The rapid evolution of cyber threats has made traditional cybersecurity methods increasingly inadequate. Artificial Intelligence (AI) has emerged as a transformative technology in the field of cybersecurity, offering enhanced capabilities for detecting and responding to cyber threats in real time. This paper explores the role of AI in revolutionizing cybersecurity, focusing on its applications in threat detection, anomaly detection, and automated response systems. Through the use of machine learning algorithms, AI can analyze vast amounts of data, identify (...) patterns, and predict potential threats, making it an essential tool in modern cybersecurity strategies. This paper also highlights the challenges and ethical considerations associated with implementing AI in cybersecurity. (shrink)

Cloud computing environments, with their vast scale and dynamic nature, are prime targets for cyber- attacks. The effectiveness of traditional security systems is often limited due to high false-positive rates, delayed response times, and resource constraints. Performance-based threat detection (PB-TD) represents an innovative approach that focuses on leveraging system performance metrics (e.g., CPU usage, memory utilization, network traffic) to identify anomalies indicative of potential security threats. This paper explores the application of performance-based metrics to enhance the accuracy and (...) efficiency of threat detection in cloud environments. By analyzing real-time system performance data, PB-TD enables faster identification of security incidents, reduces the computational overhead of conventional intrusion detection systems (IDS), and optimizes resource usage. The study investigates the effectiveness of PB-TD in cloud environments, focusing on performance gains, detection rates, and real-time scalability. The findings suggest that performance-based methods significantly enhance threat detection, especially in resource-constrained cloud settings. (shrink)

As Software-Defined Networking (SDN) gained traction in 2018, its separation of the control and data planes introduced both architectural flexibility and new security challenges. This research investigates the attack vectors specific to SDN environments—such as controller hijacking, flow rule manipulation, and DoS targeting centralized control. The study evaluates threat detection techniques including flow anomaly analysis, policy validation, and controller redundancy. It also proposes a hybrid intrusion prevention model that combines machine learning with rule-based policies for real-time mitigation. Using (...) a real-world case study of an SDN deployment in a university campus network, this paper analyzes the effectiveness of layered security mechanisms in securing programmable infrastructure. The findings contribute to a more secure deployment strategy for SDN in enterprise and carrier-grade networks. (shrink)

The increasing complexity and volume of cyber threats necessitate a more advanced and proactive approach to cybersecurity. Traditional Security Operations Centers (SOCs) rely on rule-based systems and manual analysis, which are often insufficient to counter evolving attack vectors. Artificial Intelligence (AI), particularly Machine Learning (ML) and Blockchain technology, has emerged as a game-changer in enhancing SOC operations, improving threat detection, response, and mitigation capabilities. Machine Learning algorithms can analyze vast amounts of security data in real time, identifying anomalies (...) and predicting potential threats with higher accuracy than conventional methods. ML-driven threat intelligence enables SOCs to detect zero-day attacks, automate incident response, and reduce false positives, thereby increasing operational efficiency. Additionally, AI-powered automation allows security analysts to focus on strategic threat mitigation rather than being overwhelmed by repetitive tasks. Blockchain technology further strengthens cybersecurity by providing an immutable, decentralized, and transparent framework for securing digital transactions and communications. Its application in SOC operations includes secure identity management, tamper-proof logging of security events, and decentralized threat intelligence sharing. By integrating blockchain with AI, SOCs can ensure data integrity, enhance collaboration across organizations, and reduce insider threats. This paper explores the synergy between AI, ML, and Blockchain in modern SOC environments. It discusses their combined potential to transform cybersecurity operations by enabling intelligent automation, realtime threat analysis, and secure data management. Furthermore, it highlights challenges such as computational overhead, integration complexity, and privacy concerns that must be addressed to maximize the benefits of these technologies. (shrink)

Cyber espionage campaigns pose significant challenges to global security, exploiting vulnerabilities in network infrastructures. This research paper explores advanced network traffic analysis models tailored for detecting sophisticated cyber espionage operations. The study focuses on leveraging machine learning algorithms, anomaly detection systems, and hybrid threat detection frameworks to identify subtle yet malicious activities within network traffic. Through a review of research, this paper synthesizes key findings and outlines practical applications, offering a roadmap for enhancing cybersecurity frameworks. Findings highlight (...) the efficacy of deep learning and unsupervised models in mitigating espionage-related risks, paving the way for more robust detection systems. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. -/- . (shrink)

Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the (...) detection of both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

The contribution of this paper is a comprehensive cybersecurity framework to secure cloud hosted Guidewire implementations by addressing critical security challenges such as threat detection, incident response, compliance, and system performance. Based on advanced technologies like machine learning, behavioral analytics and auto patching, the framework detects and mitigates known and unknown threats, incidentally zero-day exploit. The system does this through micro segmenting, behavioral anomaly detection, and automated patch orchestration in a way that does not render the system (...) unperforming. Key performance metrics of threat detection time (less than 3 seconds), incident response (patching within 5 minutes) and system availability (99.95% uptime) are all tested as the framework outperforms in all of these. Additionally, the framework ascertains industry standards such as GDPR and HIPAA via automated audit trails and ongoing monitoring. Compared to the currently deployed cloud security, the proposed approach, with a special focus on more secure the Guidewire application deployed in the cloud, offers a multi layered security approach to the modules specific to the Guidewire. Though powerful, the framework also suffers from complexity in its deployment, overhead in terms of performance and a reliance on cloud provider-specific features. The scalability will need to improve, and the product will need to support multiple clouds and integrate advanced technology such as quantum computing and AI-powered threat intelligence. This work sets a solid basis for the protection of cloud-based Guidewire systems against future cyber threats while maintaining operational continuity and system compliance. (shrink)

Social networking platforms play a vital role in global communication, but they are increasingly vulnerable to security threats due to the presence of fake profiles. Fraudulent accounts are often created for misinformation, cyber fraud, identity theft, cyberbullying, and unauthorized data harvesting, compromising user privacy and damaging the credibility of social media platforms. While existing security systems, such as Facebook's Immune System (FIS), attempt to detect fake accounts, they struggle against sophisticated fraudulent profiles. Traditional detection methods primarily rely on static (...) user data, making them less effective. To improve accuracy and efficiency, this study proposes an advanced machine learning (ML) and natural language processing (NLP)-based approach for fake account detection. The system analyzes both static and dynamic behavioral patterns to distinguish between real and fake accounts. NLP techniques, including tokenization, stemming, and stop-word removal, are applied to examine user-generated text, identifying inconsistencies and unnatural patterns commonly found in fake profiles. The study utilizes datasets from social media platforms like Instagram for training and evaluation. Performance is measured using metrics such as the confusion matrix, correlation heatmap, and classification reports. Results indicate that ML and NLP techniques significantly enhance fake profile detection accuracy compared to traditional methods. By leveraging AI-driven solutions, the system strengthens social media security, prevents misinformation, and protects users from fraudulent activities. Future work can focus on deep learning techniques, dataset expansion, and real-time detection to further improve accuracy and adaptability in fake profile detection. (shrink)

IoT Security Enhancement Using Machine Learning for Real-Time Threat Detection. The Internet of Things (IoT) is a network of interconnected devices that communicate over the internet, offering convenience and efficiency but also posing significant security risks. This project focuses on enhancing IoT security by detecting cyber threats such as DDoS, DoS, reconnaissance, and data theft in real-time. The main objective is to improve the accuracy and efficiency of threat detection using machine learning models like Random Forest. (...) The system analyzes key network behaviors, including data flow, traffic anomalies, and device interaction patterns. Unlike traditional rule-based intrusion detection systems, this approach dynamically monitors network activities to identify potential threats. The system has wide applications across domains such as smart homes, industrial IoT, healthcare, and autonomous systems, contributing to a more secure and resilient IoT ecosystem. (shrink)

With the exponential growth of online social networks, now boasting over 4 billion active users, maintaining data integrity and ensuring user security has become increasingly challenging. Social media platforms face numerous security threats, including the creation of fake profiles by malicious users seeking to steal sensitive information, spread misinformation, or engage in fraudulent activities. Due to the anonymity afforded by these platforms, detecting such deceptive accounts manually is both difficult and inefficient. This project focuses on the detection of fake (...) profiles using machine learning and deep learning techniques, trained on platform-specific datasets to improve accuracy. A web-based application has been developed featuring four core modules, each dedicated to a major social media platform experiencing rapid user growth. Each module integrates a trained model to assess and classify user profiles as real or fake. Currently, the system has been implemented for Instagram, with plans to expand to other platforms. This approach aims to provide a scalable and intelligent solution to enhance user trust and safety across social media environments. (shrink)

Phishing attacks are major threats to email security and pose challenges, while cyber attackers utilize increasingly sophisticated means to deceive the user and steal away important information. Well-established ways of detecting phishing attacks, such as rule-based systems or simple machine-learning models, usually cannot deal efficiently with such advanced threats. This research proposes an approach to detect phishing attacks on email systems, which deploys natural language processing and deep learning technologies. The method proposes to improve the detection accuracies and efficiencies (...) of phishing emails, which consequently enhances the protection of emails against popular cyberattack attempts and aids in securing users from such attacks. The research involves designing an active model that utilizes the strength of NLP-based text analysis for DL-oriented pattern identification. The NLP techniques used in this study include tokenization, stop-word removal, and context- based analysis to extract significant features from the email messages. Such context information greatly helps this model differentiate between actual emails and phishing ones. Deep learning algorithms exploited here are based on CNN and LSTM networks, offering sinusoidally varying parameters for optimum recognition of patterns from different perspectives in the email data's spatial and temporal domains. The experiment results validated that the hybrid model was far superior to the conventional methods in robbing phish attack detection. The model stood at an accuracy of 97.5%, much ahead of the baseline models purely having rule-based systems or traditional machine learning algorithms. The model also performed well under real-time detection conditions, with low latency and high throughput support for deployment in an active email environment. Evidence of the model's capability to sense new variants of future phishing threats also came in the automatic updating and continuous learning, thus making it applicable to newly emerging threats. The study's practical implications are numerous in providing advances in email protection. Companies can use the model to ward off phishing attacks with reduced chances of data breaches and financial loss. Due to its low cost and scalable features, this solution can be used in organizations from small- and medium-sized to large enterprise levels. The improved detection of phishing attacks brings organizations closer to compliance with data protection and cybersecurity regulations, thus minimizing their chances of noncompliance and the fines that come with it. Future research paths include exploring even newer NLP methods consisting of transformer-based models for feature extraction about contextual understanding. Exploring hybrid approaches that can merge DL with other techniques, such as reinforcement learning in ML, will create a more robust and adaptive phishing filter. Future fields for research are multimodal data, that is, a mixture of email metadata and behavior. Merely considering hybrid techniques merging artificial intelligence with machine learning approaches such as reinforcement learning can generate an even more robust and adaptive phishing filter. Coupling all these technical solutions with user awareness and education programs would greatly enhance overall security. (shrink)

This research paper investigates the transformative applications of data visualization techniques in the realm of financial crime detection, with a specific emphasis on addressing the challenges posed by credit card fraud and money laundering. The abstract explores the intricate landscape of visualizing financial data to uncover patterns, anomalies, and potential illicit activities. Through a comprehensive review of existing methodologies and case studies, the paper illuminates the pivotal role data visualization plays in enhancing the efficiency and accuracy of fraud (...) class='Hi'>detection systems. By synergizing advanced visualization tools with machine learning algorithms, the study aims to provide insights into how financial institutions can bolster their defenses against evolving threats. Ethical considerations, usability, and the real-world impact of data visualization in combating financial crime are also scrutinized. This research contributes to the evolving discourse on leveraging visualization technologies to fortify financial systems against illicit activities, fostering a proactive and responsive approach to safeguarding economic ecosystems. (shrink)

Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the (...) detection of both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the (...) detection of both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

Deepfakes and other forms of synthetic media are widely regarded as serious threats to our knowledge of the world. Various technological responses to these threats have been proposed. The reactive approach proposes to use artificial intelligence to identify synthetic media. The proactive approach proposes to use blockchain and related technologies to create immutable records of verified media content. I argue that both approaches, but especially the reactive approach, are vulnerable to a problem analogous to the ancient problem of the criterion—a (...) line of argument with skeptical implications. I argue that, while the proactive approach is relatively resistant to this objection, it faces its own serious challenges. In short, the proactive approach would place a heavy burden on users to verify their own content, a burden that is exacerbated by and is likely to exacerbate existing inequalities. (shrink)

The increasing rate and sophistication of cyber attacks pose a major risk to health data security. Traditional security systems cannot handle advanced ransomware, insider threats, and phishing attacks and hence incorporation of artificial intelligence (AI) into cybersecurity solutions becomes the need of the hour. AI-based security solutions leverage machine learning, behavior analysis, and real-time anomaly detection to identify and counter threats before they affect sensitive patient information. This study examines real-world case studies where AI successfully prevented cyberattacks in healthcare (...) settings, including a major U.S. hospital mitigating a ransomware attack, a European health system detecting insider threats, and a telemedicine platform blocking phishing attempts. The findings demonstrate AI’s superior threat detection, rapid response capabilities, and potential to enhance regulatory compliance. Even with significant advancements introduced by AI, challenges such as false positives, data privacy, and ethics exist. This article highlights the pioneering function of AI in protecting healthcare data and presents a vision on the future of AI-based cyber protection. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

The adoption of multi-cloud environments has become a strategic necessity for organizations seeking scalability, flexibility, and operational efficiency. However, distributing workloads across multiple cloud providers introduces significant security challenges, including authentication vulnerabilities, inconsistent security policies, data breaches, and compliance risks. Traditional security approaches often fail to address the complexity of multi-cloud ecosystems, requiring a more comprehensive risk mitigation strategy. This paper analyses key security risks in multi-cloud architectures and evaluates industry-standard risk assessment frameworks to prioritize effective countermeasures. Our findings indicate (...) that authentication, access control, and secure cloud networking are the most critical areas requiring immediate attention. Threats such as identity mismanagement, insecure data transfers, and lack of unified monitoring further escalate security concerns. To mitigate these risks, we propose a combination of zero-trust architecture, robust identity and access management (IAM), encryption protocols, and AI-driven threat detection. Implementing these strategies can enhance data integrity, regulatory compliance, and overall cloud security posture. By adopting a proactive approach, IT leaders can optimize cybersecurity investments and ensure the resilience of multi-cloud environments. This study provides actionable insights to strengthen security in distributed cloud architectures, enabling organizations to defend against evolving cyber threats. (shrink)

Phishing attacks pose a significant threat to online security by tricking users into revealing sensitive information through deceptive websites. These attacks are frequently used as entry points for broader cyber intrusions, risking both personal and organizational data. Traditional detection systems often rely on predefined rules or manual feature extraction, which limits their effectiveness, especially against new or zero-day phishing threats. To overcome these limitations, this project proposes an intelligent phishing website detection system utilizing an ensemble machine learning (...) approach. By combining the strengths of multiple learning algorithms, the model enhances accuracy and adaptability. The system automatically extracts meaningful features from URLs and web page content, enabling it to distinguish between legitimate and malicious websites efficiently. This approach not only improves the detection rate but also reduces false positives, making it suitable for real-world deployment. The ensemble strategy ensures the model remains resilient to evolving attack patterns, offering a more robust and scalable solution to phishing detection. (shrink)

Agricultural productivity is significantly impacted by wildlife intrusion and unfavourable environmental conditions, necessitating the integration of modern technology to enhance crop protection and growth monitoring. This project presents an intelligent IoT and sensor-based automation system designed to safeguard crops while ensuring optimal growth conditions. The system incorporates multiple sensors to monitor environmental parameters and detect potential threats in real time. A soil moisture sensor continuously assesses soil moisture levels, and when low moisture is detected, it automatically activates a water motor (...) to initiate irrigation, preventing water stress in crops. Additionally, a DHT sensor monitors temperature and humidity, providing farmers with real-time data updates through a mobile IoT platform, enabling proactive decision-making for maintaining ideal growing conditions. To mitigate the risks posed by wildlife intrusion, a Passive Infrared (PIR) sensor is deployed to detect motion in the field. Upon detecting movement, the system triggers an alarm buzzer to deter animals and simultaneously updates the IoT platform, alerting farmers about potential threats. Furthermore, the security aspect is enhanced using image processing techniques based on MATLAB and Convolutional Neural Networks (CNN). The system captures and processes images of intruding wildlife, accurately identifying animal species and automatically triggering an alert system for immediate intervention. By integrating IoT with machine learning-based image recognition, the system offers a dual-layered security mechanism, ensuring a proactive response to wildlife threats. (shrink)

: Deepfake technology enables the creation of hyper-realistic fake videos, posing significant threats in domains like politics, law enforcement, and cybersecurity. This project proposes a deepfake detection framework leveraging facial feature embeddings using FaceNet512, followed by classification through transfer learning models. Unlike traditional CNN-based detectors that analyze full video frames, this method isolates and processes facial data, ensuring higher efficiency and accuracy. Additionally, a real-time email alert system notifies users upon deepfake detection. Evaluation across benchmark datasets like DFDC (...) and FaceForensics++ demonstrates high accuracy, robustness, and suitability for real-time deployment. The proposed system is designed to detect subtle facial manipulations using efficient feature extraction and classification techniques. By focusing on key facial landmarks and minimizing the amount of data analyzed, it ensures quick and effective identification of forged content. The use of pre-trained models via transfer learning significantly reduces the training time while maintaining high generalization across various datasets. Integration with email alerts also provides timely responses for practical security applications. The solution is versatile, scalable, and offers real - world utility in environments like social media platforms, digital forensics, and content authentication. It offers a balanced approach to performance and usability, setting a foundation for future improvements like audio detection, multimodal input support, and deployment on mobile or edge device. (shrink)

: The rise of digital platforms for job recruitment has brought with it a growing threat of fraudulent job postings, undermining the trust and safety of online hiring systems. This paper proposes an advanced fraud detection system based on the ALBERT (A Lite BERT) model to identify fraudulent job postings. The system will utilize a dataset created by merging job postings from multiple sources to better capture both legitimate and fraudulent job listings. A comprehensive pre-processing pipeline, including data (...) cleaning and feature engineering, will be applied to prepare the data for model training. The system will incorporate various techniques, such as SMOTE (Synthetic Minority Over-sampling Technique), to address class imbalance. The ALBERT model will then be fine-tuned to classify job postings, and the system will be evaluated using standard performance metrics like accuracy, precision, recall, and F1-score. The goal is to provide an effective and scalable solution for detecting fraudulent job postings in real-time, enhancing the overall security and trustworthiness of online recruitment platforms. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy of the (...) proposed approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to improve (...) the accuracy and effectiveness of detecting intrusions. The proposed framework integrates multiple layers of defense, including data preprocessing, feature extraction, classification, and post-processing, to provide a robust solution to IoT security challenges. This research explores the application of EML methods such as Random Forests, AdaBoost, and Gradient Boosting, combining them into a unified model to detect and mitigate various types of attacks in IoT environments. Experimental results demonstrate the superiority of the proposed framework in terms of detection accuracy, false positive rate, and computational efficiency. (shrink)

The increasing prevalence of Distributed Denial-of-Service (DDoS) attacks in Software- Defined Networking (SDN)-based IoT environments poses a significant security challenge. Existing detection methods often suffer from limited accuracy, high false positive rates, and poor scalability, leading to delayed mitigation and network disruptions. This project proposes an ensemble learning approach combining K-Nearest Neighbors (KNN) and Light GBM to enhance real-time DDoS attack detection and mitigation. KNN efficiently classifies network traffic based on proximity, while Light GBM utilizes gradient boosting to (...) improve detection accuracy and adaptability to evolving attack patterns. The proposed model leverages IoT controllers for dynamic mitigation, ensuring minimal impact on network performance. Key advantages of this approach include reduced false positives, improved scalability, and faster response times, making it highly effective for real-world IoT security applications. The system processes network traffic features such as packet rate, flow duration, and entropy, providing robust anomaly detection. Experimental evaluation on benchmark datasets demonstrates superior accuracy, precision, and recall compared to traditional methods. By integrating lightweight and adaptive machine learning models, this solution significantly enhances IoT network security, offering a scalable and efficient defense against DDoS threats. (shrink)

With the exponential growth of the Internet of Things (IoT), ensuring the security of IoT devices and networks has become a significant challenge. Anomaly detection techniques play a pivotal role in identifying unusual behaviors that may indicate cyber threats. Traditional anomaly detection systems often struggle with scalability, privacy concerns, and the need for continuous model improvement. In this paper, we propose a collaborative anomaly detection framework for IoT systems based on Federated Deep Learning (FDL). This framework allows (...) IoT devices to collaboratively train deep learning models for anomaly detection without sharing raw data, thereby addressing privacy issues. The federated approach also improves scalability by reducing the communication overhead typically associated with centralized learning systems. Experimental results demonstrate the effectiveness of the proposed framework in detecting anomalies while preserving privacy and ensuring scalability across diverse IoT devices. (shrink)

With the rapid expansion of Android-based mobile applications, ensuring user security and privacy has become a growing concern. Android's open-source nature and widespread adoption have made it a prime target for malware developers. Traditional malware detection approaches, such as signature-based and heuristic techniques, are increasingly insufficient against sophisticated and evolving threats. This project aims to develop an intelligent Android malware detection system using machine learning techniques to identify malicious applications based on behavioral and static features extracted from APK (...) files. The proposed system utilizes a comprehensive dataset consisting of both benign and malicious Android applications. Static analysis is performed by extracting features such as permissions, API calls, intents, and hardware components, without executing the code. These features are then preprocessed and used to train multiple machine learning models including Random Forest, Support Vector Machine (SVM), and Gradient Boosting Classifier. Among these, the Random Forest classifier demonstrated superior accuracy, precision, and recall in detecting malware, making it the most suitable model for deployment. (shrink)

In 2024, the intersection of artificial intelligence (AI) and cybersecurity presents both unprecedented challenges and significant opportunities. This article explores the evolving landscape of AI-driven cyber threats, the advancements in AI-enabled security measures, and the strategic responses required to navigate these new realities. Leveraging statistics, trends, and expert insights, we delve into how organizations can enhance their cybersecurity posture in the face of sophisticated AI threats.

This study aims at analyzing and outlining anAI-based NIDS design and comparing different implementation models to improve the current state of network protection. Current NIDS do not assist organizations against modern cyber threats hence relies on machine learning and deep learning for real-time protections. The detailed plan of work includes the use of signature and anomaly detection techniques in parallel and the use of ensemble technique for increasing the detectors capabilities and decrease the false positive rates. The study employs (...) open datasets for training and benchmarking and establishes that deep learning models, including CNNs and RNNs, elicit improved results compared to more conventional machine learning models. The results show that the ensemble learning model, outperformed the other and thus emphasizes future work should consider the use of this model for detection of network intrusions. This study shows that netsec based on AI-powered NIDS can increase the opportunity to detect threats and effectively respond to incidents in realistic network contexts. Further research by designs will involve handling issues like model interpretability and updates to sustain reliability and scalability. (shrink)

This remains the case because cyberattacks are becoming more frequent and sophisticated, and as a result, the IDS must also be innovative and evolving to protect sensitive networks. Traditional intrusion detection techniques are replaced by more advanced and dynamic Methodologies based on Machine Learning (ML) algorithms. These dynamic systems capture significant data flows, search for potentially pathological patterns, and forecast threats in real time. By simulating and analyzing real-time cases, this paper discusses the architecture for ML-based IDS, how this (...) function is implemented, and its effectiveness. The primary issues of data quality, model scaling, and interpretability are presented with solutions to address them and support the reliable functioning of the system. (shrink)

This book aims to provide a comprehensive analysis of Advanced Persistent Threats (APTs), including their characteristics, origins, methods, consequences, and defense strategies, with a focus on detecting these threats. It explores the concept of advanced persistent threats in the context of cyber security and cyber warfare. APTs represent one of the most insidious and challenging forms of cyber threats, characterized by their sophistication, persistence, and targeted nature. The paper examines the origins, characteristics and methods used by APT actors. It also (...) explores the complexities associated with APT detection, analyzing the evolving tactics used by threat actors and the corresponding advances in detection methodologies. It highlights the importance of a multi-faceted approach that integrates technological innovations with proactive defense strategies to effectively identify and mitigate APT. (shrink)

This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data (...) integrity, behavioral analytics for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats. (shrink)

Given the growing dependence on digital systems and the escalation of financial fraud occurrences, it is imperative to implement efficient cyber security protocols and fraud detection methodologies. The threat's dynamic nature often challenges conventional methods, necessitating the adoption of more sophisticated strategies. Individuals depend on pre-established regulations or problem-solving processes, which possess constraints in identifying novel and intricate fraudulent trends. Conventional techniques need help handling noise data and the substantial expenses incurred by false positives and true positives. To (...) tackle these obstacles, the present study introduces Deep Fraud Net, a framework that utilizes deep learning to detect and classify instances of financial fraud and cyber security threats. The Deep Fraud Net system model entails the utilization of a deep neural network to acquire intricate patterns and characteristics from extensive datasets through training. The framework integrates noise reduction methods to enhance the precision of fraud detection and improve the quality of input data. The Deep Fraud Net method attains a precision of 98.85%, accuracy of 93.35%, sensitivity of 99.05%, specificity of 93.16%, false positive rate of 7.34%, and true positive rate of 89.58%. The findings suggest that Deep Fraud Net can effectively detect and categorize instances of fraudulent behavior with a reduced occurrence of misclassifications. The method exhibits potential implications for diverse domains that prioritize robust security and fraud detection, including but not limited to banking, e-commerce, and online transactions. (shrink)

The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface for cyber threats, necessitating robust security measures. Traditional Intrusion Detection Systems (IDS) often rely on centralized architectures, which can compromise data privacy and scalability. This paper explores the integration of Federated Learning (FL) into IDS for IoT networks, enabling decentralized model training while preserving data privacy. By leveraging local computation and aggregating model updates, FL facilitates collaborative learning across distributed IoT devices. The proposed approach aims (...) to enhance detection accuracy, reduce latency, and maintain user privacy, addressing the challenges posed by the dynamic and heterogeneous nature of IoT environments. (shrink)

This paper explores the development of a sensor-based system for detecting water impurities and designing an elevator-equipped robotic boat for autonomous waste collection. Leveraging RaspberryPi for real- time data processing and motor drivers for precise navigation, this project aims to address the pressing issue of water pollution. The proposed system integrates advanced sensors, robotics, and IoTtechnologies to enhance water quality management. Water pollution poses a significant threat to environmental and public health, driven by the accumulation of chemical impurities and (...) solid waste in natural water bodies. This project proposes a smart, autonomous system that combines water quality monitoring and floating waste collection into a single mobile platform. Utilizing a Raspberry Pi as the core controller, the system integrates various water quality sensors— including pH, turbidity, and Total Dissolved Solids (TDS) sensors—to detect pollutants in real time. The boat's navigation is handled by DC motors driven by L298N motor drivers, while an elevator mechanism collects floating debris from the water surface. Additional features such as obstacle detection using ultrasonic sensors enhance the system’s autonomy. Experimental results demonstrated the system’s ability to accurately detect water impurities and efficiently collect surface waste, offering a scalable, low-cost solution for automated water pollution control. This design has the potential to contribute significantly to the preservation of clean water in urban and rural environments. (shrink)

The global cultivation of grapes reaches approximately 77.8 million tons annually, according to the International Organization of Vine and Wine. While grapes remain a vital agricultural commodity and dietary staple worldwide, their production faces serious threats from common diseases like black rot, Esca, and leaf blight. Current disease detection methods in modern vineyards primarily depend on manual visual inspection, a practice that often delays diagnosis and leads to reduced yields and compromised fruit quality. The integration of automated detection (...) methods, particularly those based on machine learning, is crucial for promoting sustainable viticulture. This study leverages the power of deep learning, a subfield of machine learning particularly adept at interpreting image data. Specifically, we implemented Convolutional Neural Networks (CNNs) to classify images of grapevine leaves as either healthy or diseased. The models employed included a custom-built baseline CNN and a range of advanced transfer learning models: DenseNet121, EfficientNetB7, MobileNetV2, ResNet50, and VGG16. While we initially hypothesized that ResNet50 would yield the highest accuracy due to its deep architecture, experimental results revealed that EfficientNetB7 outperformed all other models. To further enhance classification performance, we constructed a max-voting ensemble using the top three performing models. This ensemble approach outmatched the performance of individual models. The final model was deployed via a web-based interface, enabling vineyard professionals and growers to detect black rot, Esca, leaf blight, or confirm leaf health by uploading images captured in real-world vineyard environments. (shrink)

Web attacks, such as SQL injection, cross-site scripting (XSS), and distributed denial-ofservice (DDoS), pose significant threats to the security and integrity of online systems. Traditional detection methods, relying on rulebased systems or shallow machine learning, often struggle to keep pace with the evolving sophistication of these attacks. This paper proposes an end-to-end deep learning framework for detecting web attacks, leveraging the power of neural networks to automatically learn complex patterns and features from raw web traffic data. Unlike conventional approaches (...) that require extensive manual feature engineering, our method processes unprocessed HTTP request logs and network traffic directly, enabling a fully automated detection pipeline. The proposed model employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both spatial and temporal dependencies in web traffic data. CNN layers extract local patterns, such as malicious payloads or anomalous headers, while RNN layers, enhanced with attention mechanisms, model sequential behaviors indicative of attacks. The framework is trained endto-end on a diverse dataset comprising benign traffic and real-world attack samples, ensuring robustness across various attack vectors. Preprocessing is minimized to tokenization and normalization, preserving the raw data's integrity and reducing human intervention. Evaluation results demonstrate that the model achieves superior performance compared to traditional methods, with an accuracy of over 95% and a low false-positive rate. It effectively identifies both known and zero-day attacks by generalizing learned representations. Additionally, the system scales efficiently, processing large volumes of traffic in real-time, making it suitable for deployment in production environments. This end-to-end deep learning approach not only enhances detection accuracy but also adapts to emerging threats, offering a proactive solution to the growing challenge of web security. Future work will focus on integrating explainability to improve trust and interpretability in security operations. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with (...) Signature-Based Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with (...) Signature-Based Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

This paper examines the increasing importance of robust cybersecurity measures in the digital payments industry. As the volume and value of online financial transactions continue to grow exponentially, the sector faces a corresponding surge in cyber-attacks, necessitating advanced cybersecurity protocols. This study explores key cybersecurity measures and implementation strategies, including encryption, multi-factor authentication (MFA), tokenization, artificial intelligence (AI)- based fraud detection, and regulatory compliance, to safeguard digital payments against various cyber threats. Through a detailed review of existing literature, case (...) studies, and statistical analysis, the article provides strategic insights into how organizations can enhance security in digital payment ecosystems, maintain compliance, and achieve resilience in the face of evolving cyber threats. (shrink)

The time window between the disclosure of a new cyber vulnerability and its use by cybercriminals has been getting smaller and smaller over time. Recent episodes, such as the Log4j vulnerability, exemplify this well. Within hours after the exploit being released, attackers started scanning the internet looking for vulnerable hosts to deploy threats like crypto currency miners and ransom ware on vulnerable systems. Thus, it becomes imperative for the cybersecurity defense strategy to detect threats and their capabilities as early as (...) possible to maximize the success of prevention actions. Although crucial, discovering new threats is a challenging activity for security analysts due to the immense volume of data and information sources to be analyzed for signs that a threat is emerging. In this sense, we present a framework for automatic identification and profiling of emerging threats using Twitter messages as a source of events and MITRE ATT&CK as a source of knowledge for threat characterization. The framework comprises three main parts: identification of cyber threats and their names; profiling the identified threat in terms of its intentions or goals by employing two machine learning layers to filter and classify tweets; and alarm generation based on the threat’s risk . The main contribution of our work is the approach to characterize or profile the identified threats in terms of their intentions or goals, providing additional context on the threat and avenues for mitigation. In our experiments, the profiling stage reached an F1 score of 77% in correctly profiling discovered threats. (shrink)

This abstract summarizes, in essence, what generative AI means to the insurance industry. The kind of promise generated AI offers to insurance is huge: in risk assessment, customer experience, and operational efficiency. Natural disaster impact, financial market volatility, and cyber threat are augmented with techniques of real time scenario generation and modeling as well as predictive simulation based on synthetic data. One of the challenges that stand in the way of deploying these AI methods, however, is data privacy, model (...) reliability and interpretability. The insurance industry must address these issues if it is to comply with regulations like GDPR and CCPA or mitigate reputational risks, as anybody can make complaints about any company based on any perceived data breach. Indeed, generative AI models, often convoluted and biased, can propagate unfair outcomes, especially in pricing and policy access. Navigating these challenges requires a whole new frayed society of AI specialists in collaboration with the industry’s insurers to offer bespoke solutions that address the firm’s needs. Based on this work, future developments will center on designing and implementing ethical and regulatory frameworks around AI-driven decisions that are transparent, fair and sustainable. Generative AI has historically been the most feared phenomenon in the history of western civilization, but with predictive analytics and risk simulation capabilities taking shape, generative AI is set to disrupt the insurance industry and add real-time decision support, automating claims processing and personalized interaction. To successfully integrate generative AI, technical, ethical and regulatory challenges will need to be addressed, and a trust-driven ecosystem will emerge to serve evolving customer needs. (shrink)

Illegal migration has become a critical and ongoing challenge for India, affecting the country’s national security, economic stability, and social harmony. This paper focuses specifically on individuals who enter or remain in India without legal authorization and engage in criminal activities, misuse public resources, or violate laws. Unlike legal migrants or refugees who seek protection and contribute positively, these unauthorized migrants often strain India’s infrastructure and public services, creating challenges for law enforcement and governance. This study analyzes the risks posed (...) by illegal migration, including threats to internal security, involvement in crime, and socio-economic pressures on local communities. It highlights the shortcomings in India’s current legal and administrative systems, which largely rely on outdated laws and fragmented policies that vary across states. To address these gaps, the paper proposes a comprehensive, technology-driven national framework that integrates advanced digital identity verification, artificial intelligence-based document authentication, and smart border surveillance systems. Key technological solutions include enhancing the Aadhaar system with a citizenship verification layer, deploying AI to detect forged documents, using satellite imagery to monitor illegal settlements, and implementing a centralized migrant tracking database. Alongside technology, the paper recommends legal reforms, improved coordination between central and state authorities, and ethical enforcement practices that respect humanitarian principles. By adopting this structured approach, India can better protect its borders, uphold its sovereignty, and maintain social cohesion without compromising on democratic values or international humanitarian commitments. This paper aims to provide a clear roadmap for policymakers and security agencies to tackle illegal migration effectively while ensuring fairness and justice for all residents. Keywords: Illegal Migration, India, National Security, Socioeconomic Stability, Strategic Solutions, Border Management, Immigration Policy, Demographic Impact, Internal Security, Technology in Border Control, Human Trafficking, Policy Framework, Refugee Crisis, Migration Governance, National Integration. (shrink)

Cloud computing has become an integral part of modern IT infrastructure, enabling organizations to store, process, and manage data with unprecedented flexibility and scalability. However, as more critical and sensitive data moves to the cloud, the need for robust security mechanisms becomes increasingly vital. This paper introduces "CloudShield," a forward-thinking security framework designed to address the emerging challenges of cloud security. We explore the core components of this model, including AI-powered threat detection, enhanced encryption protocols, decentralized access management, (...) and compliance automation. Additionally, we discuss how CloudShield integrates with existing cloud infrastructure and supports dynamic threat environments. Finally, the paper explores the role of AI, blockchain, and quantum computing in shaping the future of cloud security. The aim is to provide a comprehensive overview of how the CloudShield framework can serve as a blueprint for securing the next generation of cloud environments. (shrink)

The rapid evolution of cloud computing and the increasing sophistication of cyber threats have necessitated a paradigm shift in the approach to cybersecurity. The rapid growth of cloud computing has revolutionized business operations with unparalleled scalability, flexibility, and access to enormous computational power. Nevertheless, exponential growth has also led to an exponential rise in security threats, with cloud environments being the main target for cyberattacks. Conventional security controls lag behind the rising complexity of these threats. Artificial intelligence (AI) has become (...) a revolutionary solution to the problem, providing sophisticated capabilities that strengthen the security posture of cloud environments. This paper discusses the potential role of AI in shaping future cloud security, with an emphasis on its potential to facilitate real-time threat detection, automated response, and predict future vulnerabilities. Through an examination of AI-driven innovations in cloud security, this paper tries to shed light on how companies can use AI in a manner that will improve security, reduce cost, and improve operational resilience. (shrink)