Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

=Data Mining, False Alarm, Network Intrusion Detection System, Naïve Bayes, Multinomial Logistic Regression. Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate. How to cite this article: S. M. Shareef and S. H. Hashim, “Proposed hybrid classifier to improve network intrusion detection system using data mining techniques,” Engineering and Technology Journal, Vol. 38, Part B, No. 11, pp. 6-14, 2020. DOI: https://doi.org/10.30684/etj.v38i1B.149

[1]  Krishnan Chandrasekaran,et al.  Improving false alarm rate in intrusion detection systems using Hadoop , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[2]  Vijay D. Katkar,et al.  Lightweight approach for detection of denial of service attacks using numeric to binary preprocessing , 2014, 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA).

[3]  Soukaena Hassan Hashim,et al.  A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms , 2013 .

[4]  Neha Gupta,et al.  Reducing False Positive in Intrusion Detection System : A Survey , 2016 .

[5]  Javed Akhtar Khan,et al.  A Survey on Intrusion Detection Systems and Classification Techniques , 2016 .

[6]  Archana Singh,et al.  Network intrusion detection system using various data mining techniques , 2016, 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS).

[7]  B. B. Gupta,et al.  Estimating strength of DDoS attack using various regression models , 2010, Int. J. Multim. Intell. Secur..

[8]  Meenakshi Bansal,et al.  Improvement of Intrusion Detection System in Data Mining using Neural Network , 2013 .

[9]  Kathleen Goeschel,et al.  Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis , 2016, SoutheastCon 2016.

[10]  G. Keerthika,et al.  Feature Subset Evaluation and Classification using Naive Bayes Classifier , 2015 .

[12]  Yasmen Wahba,et al.  Improving the Performance of Multi-class Intrusion Detection Systems using Feature Reduction , 2015, ArXiv.

[13]  Balachandra Muniyal,et al.  Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection , 2016 .

[14]  Mohammad Khubeb Siddiqui,et al.  Analysis of KDD CUP 99 Dataset using Clustering based Data Mining , 2013 .

[15]  Md. Manirul Islam,et al.  A NOVEL SIGNATURE-BASED TRAFFIC CLASSIFICATION ENGINE TO REDUCE FALSE ALARMS IN INTRUSION DETECTION SYSTEMS , 2015 .