CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET

International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.3, May 2019
DOI: 10.5121/ijnsa.2019.11302 21
CLASSIFICATION PROCEDURES FOR INTRUSION
DETECTION BASED ON KDD CUP 99 DATA SET
Shaker El-Sappagh, Ahmed Saad Mohammed, Tarek Ahmed AlSheshtawy
Faculty of Computers & Informatics, Benha University, Egypt.
ABSTRACT
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way
to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number
of false alerts; this issue motivates several experts to discover the solution for minifying false alerts
according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD
CUP 99. This paper presented various data mining classification for handling false alerts in intrusion
detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99
that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The
best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4
seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
KEYWORDS
Intrusion Detection, Data Mining, KDD CUP 99, False Alarms
1. INTRODUCTION
Communication system plays an inevitable role in common people’s daily life. Computer
networks are effectively used for business data processing, education and learning, collaboration,
widespread data acquisition, and entertainment [1]. With the enormous growth of computer
networks usage and internet accessibility, more organizations are becoming susceptible to a wide
variety of attacks and threats [2]. One of the main challenges in the security management of large-
scale high-speed networks is the detection of suspicious anomalies in network traffic patterns due
to distributed denial of service (DDoS) attacks or worm propagation [3].
Generally, the major focus of the network attacks is to increase the threat against the commercial
business and our daily life, so it becomes a serious problem for the researchers to find a suitable
solution for these types of attacks [4]. Network security is becoming an absolute necessity to
protect information contained in the computer systems worldwide. With the rapid expansion of
computer networks during the past decade, the network grows in size and complexity, and
computer services expansion, vulnerabilities within the local area and wide area network become
a huge problem [5]. Nowadays, network security is a world hot topic in computer security and
defense. Intrusions, attacks, or anomalies in network infrastructure lead mostly in great financial
losses and massive sensitive data leaks. Therefore, they decrease the efficiency and quality of
productivity of organizations [6]. Reliance on Internet and world wide connectivity has increased
the potential damage that can be inflicted by attacks launched over Internet against remote
systems. Successful attacks inevitably occur despite the best security precautions [7].
Intrusion detection system (IDS) is a program that tries to ﬁnd indications that the computer has
been compromised [8]. It attempts to detect an intruder breaking into computer system or

22
legitimate user misuses system resources. Intrusion detection is an important issue and has
captured the attention of network administrators and security professionals. It is the art of
detecting unauthorized, inappropriate, or anomalous activity on computer systems. IDSs are
classified as network based, host based, or application based depending on their mode of
deployment and data used for analysis [9]. In addition, IDSs can also be classified as signature
based or anomaly based depending upon the attack detection method. The signature-based
systems are trained by extracting specific patterns (or signatures) from previously known attacks
while the anomaly-based systems learn from the normal data collected when there is no
anomalous activity [10, 11]. The main purpose of IDS is to detect as many attacks as possible
with the minimum number of false alarms. In other words, the system must be accurate in
detecting attacks. However, accurate systems that cannot handle large amount of network traffic
and is slow in decision making will not fulfill the purpose of an intrusion detection system [12].
The huge issue in IDs is presence of huge number of false alerts; this issue is being motivated by
several experts to discover the solution for minifying false alerts. . This research will be presented
as various data mining classification for handling this issue in IDSs as reviewed.
2. RELATED WORK
This section briefly discusses many techniques of classification used for classifying intrusion
detection datasets including decision trees, Bayesian classification, artificial neural network,
support vector machines, associative classification, and k-nearest neighbor. Classifiers have been
suggested and developed to reduce false alarm of intrusion detection in the area of network
security based on different ideas.
Warrender et al. [13] have proposed several intrusion detection methods based on system call
trace data. They tested a method that utilizes sliding windows to determine a database of normal
sequences to form a database for testing against test instances and classify instances according to
those in the normal sequence database. This requires the maintenance of a large database of
normal system call trace sequences. Wenke et al. [14] proposed the Mining Audit Data for
Automated Models for Intrusion Detection project. It is one of the best known data mining
projects in intrusion detection. It is an off-line IDS to produce anomaly and misuse intrusion
detection models for network and host systems. Association rules and frequent episodes are
applied to replace hand-coded intrusion patterns and profiles with the learned rules. Agarwal et al.
[15] proposed a two-stage general-to-specific framework for learning a rule-based model
(PNrule) to learn classifier models on KDD 99 data set. Barbara et al. [16] proposed (Audit Data
Analysis and Mining), which is an intrusion detector built to detect intrusions using data mining
techniques. It first absorbs training data known to be free of attacks. Next, it uses an algorithm to
group attacks, unknown behavior, and false alarms.
Abraham [17] proposed (Intrusion Detection using Data Mining Technique), which is a real-time
NIDS for misuse and anomaly detection. It applies association rules, Meta rules, and
characteristic rules. It employs data mining to produce a description of network data and uses this
information for deviation analysis. Zhang et al. [18] proposed a statistical neural network
classifier for anomaly detection, which can identify UDP flood attacks. Comparing different
neural network classifiers, the back propagation neural network (BPN) has shown to be more
efficient in developing IDS. . Xu et al. [19] presented a framework for adaptive intrusion
detection based on machine learning. Multi-class Support Vector Machines (SVMs) is applied to
classifier construction in IDSs. Li et al. [20] though realized the deficiencies of KDD dataset,
developed a supervised network intrusion detection method based on Transductive Confidence
Machines for K-Nearest Neighbors (TCM-KNN) machine learning algorithm and active learning

23
based training data selection method. Panda et al. [21] study performance of three well known
data mining classifier algorithms namely, ID3, J48, and Naïve Bayes are evaluated on the KDD
CUP 99 data set. Mohammed et al. [22] proposed a comprehensive analysis classification
techniques are used to predict the severity of attacks over the network. They compared zero R
classifier, Decision table classifier and Random Forest classifier with KDD CUP 99 databases
from MIT Lincoln laboratory. Sathyabama et al. [23] used clustering techniques to group user’s
behavior together depending on their similarity and to detect different behaviors and specified as
outliers.
Chihab et al. [24] presented five data mining algorithms like (ID3, Naive Bayes, Random forest,
C4.5, and multilayer perceptron) to make the comparison between them which applied on
network intrusions and get the best proposal of a hybrid classifier based on naïve Bayes and
random forest algorithms. The results shows that the hybrid system improved the prediction with
reduced, consuming time. Keerthika et al,[25] provided proposal, which focuses on the naïve
feature reduction, in addition to feature selection methods such as gain ratio and information gain
for reducing the redundant and irrelevant of features. This proposal used naïve Bayes classifier to
design intrusion detection system. Tesfahun et al. [26] suggested an effective hybrid layered
intrusion detection system by combining misuse and anomaly IDS for detecting both previously
known and unknown attacks. The first layer consisted of misuse detector, which is based on
random forest classifier for detect and stop known attacks; the second layer involved anomaly
detector was built using bagging technique with a staff of one class (SVM) classifiers. The results
showed that system can detect previously unknown attacks with a detection rate improvement of
(18.73%) by using NSL-KDD dataset. Aggarwal et al. [27] assessed several classification
algorithms like Random Forest, Naïve Bayes, C4.5, and Decision Table. They compared these
classification algorithms in WEKA with KDD99 dataset. These classifiers were resolved
according to metrics like accuracy, precision, and F-score. Random Tree displays the best
outcomes aggregate in contrast to the algorithms, which have high detection and low false alarm
rate were C4.5 and Random Forest. Mukund et al. [28] proposed the existing algorithms for
intrusion detection system to introduce an improved way of using the HDFS (Hadoop Distributed
File System). So to reduce the false alarm rate, they used decision tree technique and enhanced it
in the process with the multi-system capabilities of the HDFS. Therefore this approach reduced
the time taken by the DFS and improved the accuracy of the IDS.
Gupta et al. [29] IDSs monitors the network or malicious activities and forbidden access to
devices. IDSs used to protect the data's features and integrity. The proposal was used NSL-KDD
dataset to learn the manner of the attacks depending on the methods of data mining such as
logistic regression and K-means clustering. Hence, it generates rules for classifying network
activities. The results show that linear regression was very effective accuracy in detecting attacks
was (80%) while the K-means clustering was showed kind results with (67%) accuracy.
Akashdeep Sharma et al. [30] work proposes an intelligent system which first performs feature
ranking on the basis of information gain and correlation. Feature reduction is then done by
combining ranks obtained from both information gain and correlation using a novel approach to
identify useful and useless features. These reduced features are then fed to a feed forward neural
network for training and testing on KDD99 dataset. Kabir et al. [31]. Proposes a novel approach
for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-
SVM). Decision making is performed in two stages. In the ﬁrst stage, the whole dataset is divided
into some predetermined arbitrary subgroups. The proposed algorithm selects representative
samples from these subgroups such that the samples reﬂect the entire dataset. In the second stage,
least square support vector machine (LS-SVM) is applied to the extracted samples to detect
intrusions on KDD 99 database which is considered a de facto benchmark for evaluating the
performance of intrusions detection algorithm.

24
Liyu Duan and Youan Xiao [32] large volume of the data and unbalanced data, intrusion data
were inevitable obstacles. So, solve those issues utilizing the fuzzy c-means procedure to
reconstruct feature vectors according to central points. Nathan et al [33] This paper shows deep
learning procedure for intrusion detection, which implemented in GPU-enabled TensorFlow and
evaluated utilizing KDD 99 dataset. Osamah et al [34] In this paper, introduced learning
procedure for intrusion detection according to tree calculation on the KDD-99.
3. INTRUSION DETECTION SYSTEM
IDS can be defined as a combination of software and/or hardware components which monitors
computer systems and makes an alarm when an intrusion occurs [35]. The basic architecture of
IDS is shown in Figure 1.
Figure 1: Basic Architecture of IDS [36].
The components in this architectural framework are as follows [36].
• Data Gathering Device: responsible for collecting the data from the monitored system.
• Detector–ID Engine: processes the data collected from sensors to identify intrusive
behavior and send an alarm signal to response component if there is an intrusion.
• Knowledge Base: contains pre-processed information provided by network experts and
collected by sensors.
• Configuration Device: provides information about the current state of IDS.
• Response Component: initiates the response (active or inactive) when intrusion is
detected.
4. EVOLUTION OF INTRUSION DETECTION
There are many metrics for evaluating the IDS performance. The following is a description of
some of these metrics [37]:
• Predictive accuracy: The two measures used for evaluating the predictive performance of
IDS are: (i) detection rate and (ii) false alarm rate. Detection Rate (DR) also known as True
Positive Rate (TPR) is defined as the ratio of number of attacks correctly detected to the total
number of attacks, while the False Alarm (false positive) Rate (FAR) is the ratio of the
number of normal connections that are incorrectly classified as attacks to the total number of
normal connections.

25
• True Positive (TP): IDS producing an alarm when a legitimate attack occurs. False Positive
(FP): IDS producing an alarm when no attack occurs. False Negative (FN): IDS producing no
alarm when the actual attack occurs. True Negative (TN): IDS producing no alarm when no
attack occurs
• Receiver Operating Characteristics (ROC): Evaluation of IDS can also be performed
using Receiver Operating Characteristics (ROC). ROC graphs depicts trade-offs between
detection rate and false alarm rate. In ROC , the point that corresponds to 0% false alarm rate
and 100% detection rate represents the perfect IDS (Foster Provost, Tom Fawcett)
• performance Time: The performance time of IDS is the total time taken by IDS to detect the
intrusion
5. INTRUSION DETECTION DATASET
In this section, brief description of KDD Cup 1999 dataset which was derived from the 1998
DARPA Intrusion detection Evaluation program is provided. It is the most widespread dataset
collected over a period of nine weeks for a LAN simulating a typical U.S. Air Force LAN. The
dataset contains a collection of simulated raw TCP dump data, where, multiple intrusions attacks
was introduced and widely used in the research community from seven weeks of network traffic.
The dataset contains 4,898,430 labeled and 311,029 unlabeled connection records. The labeled
connection records consist of 41 attributes. In network data of KDD99 dataset, each instance
represents feature values of a class, where each class is categorized either normal or attack. The
classes in the dataset are characterized into one normal class and four main intrusion classes [38] :
• Normal: connections are generated by simulating user behavior.
• DoS attacks: use of resources or services is denied to authorized users.
• Probe attacks: information about the system is exposed to unauthorized entities.
• User to Remote attacks: access to account types of administrator is gained by
unauthorized entities.
• Remote to Local attacks: access to hosts is gained by unauthorized entities.
6. DATA MINING AND INTRUSION DETECTION
Data mining is the process of discovering interesting knowledge from large amounts of data
stored either in databases, data warehouses, or other information repositories [39]. Classification
is a data mining technique, which arranges data into predefined groups. The goal of predictive
classification is to predict the target class accurately for each record in a set of new data, that is,
data that is not in the historical data [40]. Intrusion detection can be defined as a classification
problem where each audit record can be classified into one of a discrete set of possible categories
(i.e. normal or a particular kind of intrusion). Intrusion detection using data mining have attracted
more and more interests in recent years by utilizing procedures programs applied to audit data to
compute misuse and anomaly detection models [41].
7. IMPLEMENTED DATA MINING METHODS BASED ON KDD CUP 99
In IDSs, there are important surveys of implemented data mining methods on KDD Cup 99 by
various experts.

26
• Multilayer perceptron for classification of KDD dataset: it is consider as the most usually
neural network procedure according to one layer for input, hidden and output [41] [42].
• Rule based model: simple procedure usually with good rules for depicting the framework in
data [43].
• Support vector machines: it is a procedure for converting the training data to a feature scope
hence getting the best splitting hyperplane [44].
• Naïve Bayes: it is a simple procedure according to probabilistic relined underlay an individual
structure [44].
• Apriori Association Rule Mining Algorithm: It is the procedure to giving frequent item sets
according to the dataset and making scan process to identify, most frequent items [45].
• K Means clustering: it is a procedure for tasking of dataset points to clusters according to the
distance between dataset points and cluster centroid [46].
• ID3, C4.5, and C5.0 decision tree algorithms: its procedure for building a decision tree for
classification dataset according to training data [47].
8. RESULTS AND DISCUSSION
The fundamental aim of this research is to decide the excellent procedure of data mining
procedures to classify KDD99 so as to has a high accuracy and low time in knowing attacks.
Furthermore, smoothing the mission of select for expert’s in the future on KDD dataset, Good
implementing cases of all the seven procedures aforementioned over were assessed. Results are
given in the Table 1 to compare the classifiers; for IDs, utilizing accuracy and Training Time for
knowing the best procedure for the classifier. Simply as predictably that no sole procedure can
reveal all attack class, with high accuracy and without false alarm ratio. The best accuracy in
Multilayer Perceptron is 92% however the best Training Time in Rule based model is 4 seconds.
Table 1: Comparison of Seven Procedures
9. CONCLUSION
Many data mining procedures have been focused by the experts in IDS scope and they purpose to
minify the great load of analyzing massive quantity of data. KDD Cup’99 data set is suffering
from a variance inter the classes which impact miserable detection and is a main issue to data
mining procedures. An important issue in designing IDS is minified false alarm ratio and
attaining high detection ratio. Utilizing various classification procedures, potential to minify false
alarm enhances the detection accuracy and many classification procedures. In this research,
utilized by the experts in the performance of IDS structure are discussed and reviewed. From the

27
tentative survey applied this paper specified that various experts suggest various procedures for
IDS scope in various group , but still, needed to search.
REFERENCES
[1] Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava, “A Comparative
Study of Anomaly Detection Schemes in Network Intrusion Detection”
[2] Denning D. E, An intrusion-detection model, IEEE Transactions on Software Engineering, vol. SE-
13, no. 2, pp.222-232.
[3] Zesheng C., L. Gao and K. Kwiat, “Modeling the Spread of Active Worms”, Twenty Second Annual
Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), vol. 3,
pp:1890-1900, 2003.
[4] W. Lee and S. J. Stolfo, “Data Mining Approaches for Intrusion Detection”, the 7th USENIX Security
Symposium, San Antonio, TX, January 1998.
[5] Moradi M., Zulkernine M., 2003, “A Neural Network Based System for Intrusion Detection and
Classification of Attack”, Natural Science and Engineering Research Council Canada (NSERC).
[6] Symantec Enterprise.: Internet Security Threat Report 2016.
https://www.symantec.com/content/dam/symantec/docs/reports/istr-21-2016-en.pdf. [accessed
18.03.17].
[7] Arman Tajbakhsh, Mohammad Rahmati, and Abdolreza Mirzaei, "Intrusion detection using fuzzy
association rules", Applied Soft Computing ASOC509, Elsevier B.V, 2008.
[8] J.Hu, Host-Based Anomaly IDS. Springer Handbook of Information and Communication Security,
Springer Verlag, 2010, ISBN978-3-642-04116-7 (Print), 978-3-642-04117-4 (Online)
[9] H Wang, J Cao, and Y Zhang, ”A ﬂexible payment scheme and its role-based access control”, IEEE
Transactions on knowledge and Data Engineering, vo. 17, no. 3, 425–436, 2005.
[10] Y. Zhang, Y. Shen, H. Wang, Y. Zhang, X. Jiang, “On Secure Wireless Communications for Service
Oriented Computing,” IEEE Transactions on Services Computing, no.1, pp. 1.
[11] D. Wang, Z. Zhang, P. Wang, J. Yan, and X. Huang, ”Targeted Online Password Guessing: An
Underestimated Threat,” ACM Conference on Computer and Communications Security, pp. 1242-
1254, 2016
[12] K.K. Gupta, B. Nath and R. Kotagiri, “Layered Approach Using Conditional Random Fields for
Intrusion Detection,” IEEE Transactions on Dependable and Secure Computing, vol. 7, no. 1, pp. 35–
49, 2010.
[13] Warrender C., Forrest S. and Pearl M.,“Detecting Intrusions Using System Calls: Alternative Data
Models”, in IEEE symposium on security and privacy, pp:133-145, 1999.
[14] Wenke L. and S. J.Stolfo, "A Framework for Constructing Features and Models for Intrusion
Detection Systems”, ACM transactions on Information and system security (TISSEC), vol.3, Issue 4,
Nov 2000.
[15] Agarwal R., Joshi M.V., “PNrule: A New Framework for Learning Classifier Models in Data
Mining”, Tech. Report, Dept. of Computer Science, University of Minnesota, 2000.
[16] Daniel B., J.Couto, S.Jajodia, and N.Wu, "ADAM: A Test Bed for Exploring the Use of Data Mining
in Intrusion Detection”, SIGMOD, vol30, no.4, pp: 15-24, 2001.
[17] Abraham T. , "IDDM: Intrusion Detection Using Data Mining Techniques”, Technical report DSTO
electronics and surveillance research laboratory, Salisbury, Australia, May 2001.
[18] Zheng Z., J. Li, C.N. Manikapoulos, J.Jorgenson, J.ucles, "HIDE: A Hierarchical Network Intrusion
Detection System Using Statistical Pre-Processing and Neural Network Classification”, IEEE
workshop proceedings on Information assurance and security, pp:85-90, 2001.

28
[19] Xu X., “ Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier
Construction and equential Pattern Prediction”, International Journal of Web Services Practices 2(1-
2), pp:49–58, 2006.
[20] Li Y., Guo L., “An Active Learning Based TCM-KNN Algorithm for Supervised Network Intrusion
Detection”, In: 26 th Computers and Security, pp: 459–467, October 2007.
[21] Mrutyunjaya P. and M. Ranjan Patra, ” Evaluating Machine Learning Algorithms for Detecting
Network Intrusions”, International Journal of Recent Trends in Engineering, vol. 1, no.1, May
2009.
[22] Mohammed M Mazid, M. Shawkat Ali, Kevin S. Tickle,“A Comparison Between Rule Based and
Association Rule Mining Algorithms “, Third International Conference on Network and System
Security, 2009.
[23] Sathyabama S., Irfan Ahmed M., Saravanan A,”Network Intrusion Detection Using Clustering: A
Data Mining Approach”, International Journal of Computer Application (0975-8887), vol. 30, no.
4, Sep. 2011.c
[24] Chihab Y. , Ouhman A., Erritali m. and Ouahidi B.,2013," Detection & Classification of Internet
Intrusion Based on the Combination of Random Forest and Naïve Bayes ", International Journal of
Engineering and Technology (IJET), 2013
[25] Keerthika G. and Priya D. S.," Feature Subset Evaluation and Classification using Naive Bayes
Classifier ", Journal of Network Communications and Emerging Technologies (JNCET) Volume 1,
Issue 1, March (2015) 2015
[26] Tesfahun A. and D.Bhaskari L., ,"Effective Hybrid Intrusion Detection System: A Layered
Approach", IJCNIS, vol.7, no.3, pp.35-41, 2015
[27] Aggarwal P. and Sharma S.K.," An Empirical Comparison of Classifiers to Analyze Intrusion
Detection", Proc. of Fifth International Conference an Advanced Computing and Communication
Technologies, 2015.
[28] Mukund Y. and Nayak S., ‘Improving false alarm rate in intrusion detection systems using Hadoop’,
21-24 Sept, International Conference. Vol.3 , 2016
[29] Gupta D., Singhal S, Malik S. and Singha., Network intrusion detection system using various data
mining techniques, IEEE publication, 2016.
[30] Akashdeep Sharma ,Ishfaq Manzoor, Neeraj Kumar, A Feature Reduced Intrusion Detection
System Using ANN Classifier, Expert Systems With Applications (2017)
[31] E. Kabir, J. Hu, H. Wang, G. Zhuo, A novel statistical technique forintrusion detection systems,
Future Generation Computer Systems (2017)
[32] L. Duan and Y. Xiao, "An Intrusion Detection Model Based on Fuzzy C-means Algorithm,", 8th
International Conference on Electronics Information and Emergency Communication (ICEIEC),
Beijing, pp. 120-123. (2018)
[33] Wang, Zheng. "Deep learning-based intrusion detection with adversaries." IEEE Access 6 , 38367-
38384.(2018):
[34] Raheem Esraa and Saleh Alomari ,"An Adaptive Intrusion Detection System by using Decision Tree
Osamah Adil", Journal of AL-Qadisiyah for computer science and mathematics Vol.10 No.2,(2018).
[35] Chen M.S., Han J and Yu Philip S., Data Mining: An Overview from a Database Perspective,
IEEE Transactions on Knowledge and Data Engineering, vol.8,No.6,1996,pp.866-883.
[36] Christine Dartigue, Hyun IK Jang, Wenjun Zeng, A New data-mining based approach for network
Intrusion detection, Proc. of Seventh Annual Communication Networks and Services Research
Conference, 2009, pp.372-377.
[37] Foster Provost, Tom Fawcett, Robust Classification for Imprecise Environment, 2000, pp.1-38,
Kluwer Academic Publishers.

29
[38] Chawla N.V, Bowyer K.W, Hall L.O, Kegelmeyer W.P, Smote: Synthetic minority oversampling
technique, Journal of Artificial Intelligence Research, vol.16, 2002, pp.321–357.
[39] Dewan Md. Farid, Nouria Harbi, Mohammad Zahidur Rahman , Combining Naive Bayes and
Decision Tree for Adaptive Intrusion Detection, Proc. of Intl. Journal of Network Security & Its
Applications (IJNSA), Volume 2, Number 2, 2010, pp.12-25.
[40] Domingos P. and Pazzani M., Beyond Independence: Conditions for the optimality of the simple
Bayesian Classifier, In proceedings of the 13 th. Conference on Machine Learning, 1996, pp.105-110.
[41] Yeung D. Y. and Chow C., “Prazen-window Network Intrusion Detectors”, In: 16 th International
Conference on Pattern Recognition, Quebec, Canada, pp:11–15, August 2002.
[42] Yeung D. Y. and Chow C., “Prazen-window Network Intrusion Detectors”, In: 16 th International
Conference on Pattern Recognition, Quebec, Canada, pp:11–15, August 2002
[43] Witten I. H. and Frank E., “ Data Mining: Practical Machine Learning Tools and Techniques”, 2 nd
edn. Morgan Kaufmann, San Francisco, 2005.
[44] Huy A. N., D. Choi ,” Application of Data Mining to Network Intrusion Detection: Classifier
Selection Model ”, pp:1, 2008.
[45] Mohammed M Mazid, M. Shawkat Ali, Kevin S. Tickle,“A Comparison Between Rule Based and
Association Rule Mining Algorithms “, Third International Conference on Network and System
Security, 2009.
[46] Kusum K. Bharti, S. Shukla and S. Jain , “Intrusion detection using clustering”, vol.1, issue 2, 3, 4,
pp.6, 2010.
[47] Amanpreet C., G. Mishra, G. Kumar, “Survey on Data Mining Techniques in Intrusion Detection” ,
vol: 2, issue.7, pp:2, 2011.
AUTHORS
Shaker El-Sappagh received the bachelor's degree in computer science from the
Information Systems Department, Faculty of Computers and Information, Cairo
University, Egypt, in 1997,the master’s degree from Cairo University, in 2007,and the
Ph.D. degree in computer science from the Information Systems Department, Faculty of
Computers and Information, Mansura University,Mansura, Egypt, in 2015. In 2003, he
joined the Department of Information Systems, Faculty of Computers and Information,
Minia University, Egypt, as a Teaching Assistant. Since 2016, he has been an Assistant
Professor with the Department of Information Systems, Faculty of Computers and Information, Benha
University. He is currently a Postdoctoral Fellow with the UWB Wire-less Communications Research
Center, Department of Information and Communication Engineering, Inha University, South Korea. He has
publications in clinical decision support systems and semantic intelligence. His current research interests
include machine learning, medical informatics, (fuzzy) ontology engineering, distributed and hybrid
clinical decision support systems, semantic data modeling, fuzzy expert systems, and cloud computing. He
is very interested in the diseases diagnosis and treatment researches. He is a Reviewer for many journals
Ahmed saad Mohammed received the bachelor's degree from the Software engineering
Department, Baghdad College of Economic Sciences University, Iraq, Baghdad in 2005.
His current research interests include machine learning, data mining and artificial
intelligence
Dr. Tarek El-Shishtawy is a professor of Information System. His current work is vice
Dean of postgraduates and researches at faculty of computers and informatics. The
scientific interests in clude Information Retrieval, Data Mining, and researches related to
information systems in developing countries. Dr. Tarek published and refereed many
articles in NLP.

CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET

More Related Content

What's hot (15)

Similar to CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET (20)

Recently uploaded (20)

CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SET