Security

This dataset is derived from the Andro-Dumpsys system, which analyzes Android applications through volatile memory acquisition and similarity-based profiling. During execution in an emulator, the system extracts odex bytecode to address challenges introduced by anti-analysis techniques such as packing, dynamic loading, and dex encryption. Creator-centric artifacts—including certificate serial numbers, operation code patterns, metadata from AndroidManifest.xml, suspicious API sequences, permission usage, and system command traces—are parsed to construct behavioral profiles.

Jordan is among the most water-scarce countries in the world, and understanding household water-use behavior is essential for designing effective demand-management policies. This study analyzes the determinants of monthly and per capita residential water consumption in Irbid Governorate, northern Jordan, using data from a structured household survey. The survey captured socioeconomic and demographic characteristics, dwelling features, supply conditions, and water-use practices for more than 500 households.

The Andro-AutoPsy dataset consists of Android malware and benign application samples collected to support research on similarity-based malware detection. Each application is analyzed using behavior-centric and creator-centric features, including certificate serial numbers, malicious API usage, permission likelihood ratios, system command execution, intent manipulation, and file integrity anomalies.

The Mal-Netminer dataset consists of malware samples and their corresponding system call graphs used to analyze behavioral patterns through social network analysis (SNA). The dataset was developed as part of the Mal-Netminer framework, which classifies malware by extracting topological properties from system call graphs rather than relying on traditional signature or frequency-based approaches.

This dataset consists of MAVLink message ID sequences collected from a UAV system operating in a Hardware-in-the-Loop (HITL) simulation environment. Communication between the UAV and the Ground Control Station (GCS) was monitored, and only MAVLink protocol packets were filtered from the captured traffic. From each MAVLink packet, the message ID was extracted and stored as a sequence in NumPy array format.

This dataset contains user reviews collected from 8,999 popular Android game applications on the Google Play Store, each with more than 10 million downloads. A total of 56,439,878 reviews were gathered to investigate whether user feedback reveals meaningful security concerns.

This dataset contains RTPS and ARP packet captures collected from a ROS2-based Unmanned Ground Vehicle (UGV) testbed under both benign and malicious conditions. Two representative attack types were performed: Command Injection and Command Injection combined with ARP Spoofing. Data were recorded across four time durations (180, 300, 600, 1200 seconds), with 30 scenarios per duration, totaling 240 scenarios.

This dataset was used in the paper, "Expanding the Attack Scenarios of SAE J1939: A Comprehensive Analysis of Established and Novel Vulnerabilities in Transport Protocol," presented at ESCAR USA in June 2024.

For more information about this dataset, please refer to our description paper below.

TESTBED

This dataset accompanies the research presented in “Function-Oriented Mobile Malware Analysis as First Aid” and provides behavior-oriented metadata for 906 Android malware samples collected from real-world smishing and spyware incidents in South Korea.

This dataset, titled "Survival Analysis Dataset for automobile IDS," was created to support the research in the following paper: Han, Mee Lan, Byung Il Kwak, and Huy Kang Kim. "Anomaly intrusion detection method for vehicular networks based on survival analysis." Vehicular Communications 14 (2018): 52-63.