A New DIDS Design Based on a Combination Feature Selection Approach
Feature selection has been used in many fields such as
classification, data mining and object recognition and proven to be
effective for removing irrelevant and redundant features from the
original dataset. In this paper, a new design of distributed intrusion
detection system using a combination feature selection model based
on bees and decision tree. Bees algorithm is used as the search
strategy to find the optimal subset of features, whereas decision tree
is used as a judgment for the selected features. Both the produced
features and the generated rules are used by Decision Making Mobile
Agent to decide whether there is an attack or not in the networks.
Decision Making Mobile Agent will migrate through the networks,
moving from node to another, if it found that there is an attack on one
of the nodes, it then alerts the user through User Interface Agent or
takes some action through Action Mobile Agent. The KDD Cup 99
dataset is used to test the effectiveness of the proposed system. The
results show that even if only four features are used, the proposed
system gives a better performance when it is compared with the
obtained results using all 41 features.
[1] Revision by Tzeyoung Max Wu, Information Assurance Technology
Analysis Center (IATAC), Information Assurance Tools Report –
Intrusion Detection Systems,6th ed. 2009.
[2] V. Jyothsna, V. V. Ramaprasad, K. M. Prasad, A Review of Anomaly
based Intrusion Detection Systems, International Journal of Computer
Applications, vol. 28, no.7, pp. 26-35, 2011. [3] S. R. Sriram, K. C,Vijaya, An Overview of Intrusion Detection Systems,
IDT Workshop on Interesting Results in Computer Science and
Engineering (IRCSE 9), Malardalen University, Sweden,2009.
[4] R. Sasikumar, D. Manjula,A Distribulated Intrusion Detection System
Based on Mobile Agents with Fault Tolerance, European Journal of
Scientific Research, vol. 62 no.1, pp. 48-55, 2011.
[5] S.Manmeet, S. S. Sodhi, Distributed Intrusion Detection using Aglet
Mobile Agent Technology, Proceedings of National Conference on
Challenges & Opportunities in Information Technology (COIT-2007),
RIMT-IET, Mandi Gobindgarh. March 23, 2007.
[6] B Imen, B. Y. Sadok, P. Pascal, MAD-IDS: Novel Intrusion Detection
System Using Mobile Agents and Data Mining Approaches, Intelligence
and Security Informatics, Lecture Notes in Computer Science, Springer,
vol. 6122/2010, pp. 73–76, 2010.
[7] G. Donald, Marks, M. Peter, S. Michael,Optimizing the Scalability of
Network Intrusion Detection Systems Using Mobile Agents, Journal of
Network and Systems Management, Springer, vol. 12, no. 1, pp. 95-110,
2004.
[8] E.Mohamad, A New Mobile Agent-Based Intrusion Detection System
Using Distributed Sensors, In proceeding of FEASC, 2004.
[9] V. Bolon-Canedo, N. Sanchez-Marono, A. Alonso-Betanzos,Feature
selection and classification in multiple class datasets: An application to
KDD Cup 99 dataset, Expert Systems with Applications, Elsevier, vol.
38, no. 5, pp. 5947-5957, 2011.
[10] L.Shih-Wei, Y.Kuo-Ching, L.Chou-Yuan, L.Zne-Jung,An intelligent
algorithm with feature selection and decision rules applied to anomaly
intrusion detection, Applied Soft Computing, Elsevier, vol. 12, no. 10,
pp. 3285-3290, 2012.
[11] T.Chi-Ho, K.Sam, W.Hanli,Genetic-fuzzy rule mining approach and
evaluation of feature selection techniques for anomaly intrusion
detection, Pattern Recognition, Elsevier, vol. 40, no. 9, pp. 2373-2391,
2007.
[12] L.Jean-Louis, R.Ryan, S.Stephen, M.Srinivas, Signature Based Intrusion
Detection using Latent Semantic Analysis, IEEE World Congress on
Computational Intelligence, Neural Networks, 2008. IJCNN, pp 1068-
1074, 2008.
[13] T. N.Hai, F.Katrin, P.Slobodan,Towards a Generic Feature-Selection
Measure for Intrusion Detection, International Conference on Pattern
Recognition (ICPR), IEEE, pp. 1529-1532, 2010.
[14] N. P.Neelakantan, C.Nagesh, M.Tech,Role of Feature Selection in
Intrusion Detection Systems for 802.11 Networks, International Journal
of Smart Sensors and Ad Hoc Networks (IJSSAN), vol. 1, no. 1, pp. 98-
101, 2011.
[15] R.Mohanabharathi, Mr T. Kalaikumaran, Dr.S.Karthi,Feature Selection
for Wireless Intrusion Detection System Using Filter and Wrapper
Model, International Journal of Modern Engineering Research (IJMER),
vol.2, no. 4, pp. 1552-1556, 2012.
[16] D.Rupali, L.Shilpa,Performance Comparison of Features Reduction
Techniques for Intrusion Detection System, International Journal of
Computer Science and Technology (IJCST), vol. 3, no. 1, 2012.
[17] E. B.Mohammad, G-A Nasser, H. A.Mehdi,Using Ant Colony
Optimization-Based Selected Features for Predicting Post-synaptic
Activity in Proteins, EvoBIO 2008. LNCS, Springer, vol. 4973, pp. 12-
23, 2008.
[18] D. T.Pham, A.Ghanbarzadeh, E.Koc, S.Otri, S.Rahim, M.Zaidi,The Bees
Algorithm.Technical Note, Manufacturing Engineering Centre, Cardiff
University, UK.
[19] L.Steven, Salzberg,Book Review: C4.5: Programs for Machine Learning
by Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993, Machine
Learning, Springer vol. 16, no. 3, pp. 235-240, 1993.
[20] R. Quinlan,C4.5: Programs for Machine Learning, Morgan Kaufmann
Publishers Inc, San Francisco, CA, USA, 1993.
[21] Adel Sabry Eesa, Zyenep Orman, Adnan Mohsin Abdulazeez,A New
Feature Selection Model Based on ID3 and Bees Algorithm for Intrusion
Detection System, Turkish Journal of Electrical Engineering and
Computer Sciences, olv. 23, no. 2, pp. 615-622, 2015.
[22] P.Sandhya, A.Ajith, G.Crina, T.Johnson,Modeling intrusion detection
system using hybrid intelligent systems, Journal of Network and
Computer Applications, Elsevier, vol. 30, no. 1, pp 114-132, 2007.
[23] E.Charles,Results of the KDD’99 Classifier Learning, SIGKDD
Explorations, ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, pp.
63-64, 2000.
[1] Revision by Tzeyoung Max Wu, Information Assurance Technology
Analysis Center (IATAC), Information Assurance Tools Report –
Intrusion Detection Systems,6th ed. 2009.
[2] V. Jyothsna, V. V. Ramaprasad, K. M. Prasad, A Review of Anomaly
based Intrusion Detection Systems, International Journal of Computer
Applications, vol. 28, no.7, pp. 26-35, 2011. [3] S. R. Sriram, K. C,Vijaya, An Overview of Intrusion Detection Systems,
IDT Workshop on Interesting Results in Computer Science and
Engineering (IRCSE 9), Malardalen University, Sweden,2009.
[4] R. Sasikumar, D. Manjula,A Distribulated Intrusion Detection System
Based on Mobile Agents with Fault Tolerance, European Journal of
Scientific Research, vol. 62 no.1, pp. 48-55, 2011.
[5] S.Manmeet, S. S. Sodhi, Distributed Intrusion Detection using Aglet
Mobile Agent Technology, Proceedings of National Conference on
Challenges & Opportunities in Information Technology (COIT-2007),
RIMT-IET, Mandi Gobindgarh. March 23, 2007.
[6] B Imen, B. Y. Sadok, P. Pascal, MAD-IDS: Novel Intrusion Detection
System Using Mobile Agents and Data Mining Approaches, Intelligence
and Security Informatics, Lecture Notes in Computer Science, Springer,
vol. 6122/2010, pp. 73–76, 2010.
[7] G. Donald, Marks, M. Peter, S. Michael,Optimizing the Scalability of
Network Intrusion Detection Systems Using Mobile Agents, Journal of
Network and Systems Management, Springer, vol. 12, no. 1, pp. 95-110,
2004.
[8] E.Mohamad, A New Mobile Agent-Based Intrusion Detection System
Using Distributed Sensors, In proceeding of FEASC, 2004.
[9] V. Bolon-Canedo, N. Sanchez-Marono, A. Alonso-Betanzos,Feature
selection and classification in multiple class datasets: An application to
KDD Cup 99 dataset, Expert Systems with Applications, Elsevier, vol.
38, no. 5, pp. 5947-5957, 2011.
[10] L.Shih-Wei, Y.Kuo-Ching, L.Chou-Yuan, L.Zne-Jung,An intelligent
algorithm with feature selection and decision rules applied to anomaly
intrusion detection, Applied Soft Computing, Elsevier, vol. 12, no. 10,
pp. 3285-3290, 2012.
[11] T.Chi-Ho, K.Sam, W.Hanli,Genetic-fuzzy rule mining approach and
evaluation of feature selection techniques for anomaly intrusion
detection, Pattern Recognition, Elsevier, vol. 40, no. 9, pp. 2373-2391,
2007.
[12] L.Jean-Louis, R.Ryan, S.Stephen, M.Srinivas, Signature Based Intrusion
Detection using Latent Semantic Analysis, IEEE World Congress on
Computational Intelligence, Neural Networks, 2008. IJCNN, pp 1068-
1074, 2008.
[13] T. N.Hai, F.Katrin, P.Slobodan,Towards a Generic Feature-Selection
Measure for Intrusion Detection, International Conference on Pattern
Recognition (ICPR), IEEE, pp. 1529-1532, 2010.
[14] N. P.Neelakantan, C.Nagesh, M.Tech,Role of Feature Selection in
Intrusion Detection Systems for 802.11 Networks, International Journal
of Smart Sensors and Ad Hoc Networks (IJSSAN), vol. 1, no. 1, pp. 98-
101, 2011.
[15] R.Mohanabharathi, Mr T. Kalaikumaran, Dr.S.Karthi,Feature Selection
for Wireless Intrusion Detection System Using Filter and Wrapper
Model, International Journal of Modern Engineering Research (IJMER),
vol.2, no. 4, pp. 1552-1556, 2012.
[16] D.Rupali, L.Shilpa,Performance Comparison of Features Reduction
Techniques for Intrusion Detection System, International Journal of
Computer Science and Technology (IJCST), vol. 3, no. 1, 2012.
[17] E. B.Mohammad, G-A Nasser, H. A.Mehdi,Using Ant Colony
Optimization-Based Selected Features for Predicting Post-synaptic
Activity in Proteins, EvoBIO 2008. LNCS, Springer, vol. 4973, pp. 12-
23, 2008.
[18] D. T.Pham, A.Ghanbarzadeh, E.Koc, S.Otri, S.Rahim, M.Zaidi,The Bees
Algorithm.Technical Note, Manufacturing Engineering Centre, Cardiff
University, UK.
[19] L.Steven, Salzberg,Book Review: C4.5: Programs for Machine Learning
by Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993, Machine
Learning, Springer vol. 16, no. 3, pp. 235-240, 1993.
[20] R. Quinlan,C4.5: Programs for Machine Learning, Morgan Kaufmann
Publishers Inc, San Francisco, CA, USA, 1993.
[21] Adel Sabry Eesa, Zyenep Orman, Adnan Mohsin Abdulazeez,A New
Feature Selection Model Based on ID3 and Bees Algorithm for Intrusion
Detection System, Turkish Journal of Electrical Engineering and
Computer Sciences, olv. 23, no. 2, pp. 615-622, 2015.
[22] P.Sandhya, A.Ajith, G.Crina, T.Johnson,Modeling intrusion detection
system using hybrid intelligent systems, Journal of Network and
Computer Applications, Elsevier, vol. 30, no. 1, pp 114-132, 2007.
[23] E.Charles,Results of the KDD’99 Classifier Learning, SIGKDD
Explorations, ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, pp.
63-64, 2000.
@article{"International Journal of Information, Control and Computer Sciences:70686", author = "Adel Sabry Eesa and Adnan Mohsin Abdulazeez Brifcani and Zeynep Orman", title = "A New DIDS Design Based on a Combination Feature Selection Approach", abstract = "Feature selection has been used in many fields such as
classification, data mining and object recognition and proven to be
effective for removing irrelevant and redundant features from the
original dataset. In this paper, a new design of distributed intrusion
detection system using a combination feature selection model based
on bees and decision tree. Bees algorithm is used as the search
strategy to find the optimal subset of features, whereas decision tree
is used as a judgment for the selected features. Both the produced
features and the generated rules are used by Decision Making Mobile
Agent to decide whether there is an attack or not in the networks.
Decision Making Mobile Agent will migrate through the networks,
moving from node to another, if it found that there is an attack on one
of the nodes, it then alerts the user through User Interface Agent or
takes some action through Action Mobile Agent. The KDD Cup 99
dataset is used to test the effectiveness of the proposed system. The
results show that even if only four features are used, the proposed
system gives a better performance when it is compared with the
obtained results using all 41 features.", keywords = "Distributed intrusion detection system, mobile agent,
feature selection, Bees Algorithm, decision tree.", volume = "9", number = "8", pages = "1921-5", }