Discovering computer networks intrusion using data analytics and machine intelligence

Abstract In this era of a digital revolution, the use of the Internet for information storage, access, and dissemination has increased astronomically. Also, the advent of the Internet of Things (IoT) technologies has removed the digital barrier and accentuate the seamless exchange of data and information among many ubiquitous systems. Therefore, the challenge of information theft, privacy, and confidentiality of data and information over the internet has become a major quandary for many users of several online platforms. Network intrusion detection systems are one of the viable approaches to curb the menace of information theft and other data security threats over the internet. In this paper, we show a comparison between two intrusion detection systems–one that uses the association rule data mining approach–Apriori and the other that adapts the use of a machine learning technique–Support Vector Machine (SVM). The performance of the two systems was compared using the Network Security Laboratory Knowledge Discovery and Data Mining (NSL-KDD) dataset and the University of New South Wales–NB 2015 (UNSW-NB15) dataset. Evaluation results show that SVM performs better than Apriori in terms of accuracy, while Apriori gives a better performance in terms of testing speed.

[1]  Kenneth Lai,et al.  Support vs Confidence in Association Rule Algorithms , 2008 .

[2]  K. Sundarakantham,et al.  Machine Learning Based Intrusion Detection System , 2019, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).

[3]  Alfredo Cuzzocrea,et al.  Approximation to Expected Support of Frequent Itemsets in Mining Probabilistic Sets of Uncertain Data , 2015, KES.

[4]  Mohd Juzaiddin Ab Aziz,et al.  Anomalies Classification Approach for Network-based Intrusion Detection System , 2016, Int. J. Netw. Secur..

[5]  Adetunmbi A. Olusola,et al.  Analysis of KDD '99 Intrusion Detection Dataset for Selection of Relevance Features , 2010 .

[6]  Julian Jang,et al.  A survey of emerging threats in cybersecurity , 2014, J. Comput. Syst. Sci..

[7]  Flora S. Tsai,et al.  Blog Data Mining for Cyber Security Threats , 2009 .

[8]  Carson Kai-Sang Leung,et al.  Spark-based data analytics of sequence motifs in large omics data , 2018, KES.

[9]  Snehal A. Mulay,et al.  Intrusion Detection System using Support Vector Machine and Decision Tree , 2010 .

[10]  P. Sumathi,et al.  An analysis of intrusion detection system using back propagation neural network , 2013, 2013 International Conference on Information Communication and Embedded Systems (ICICES).

[11]  Er. Amit Doegar,et al.  An Ensemble Approach for Intrusion Detection System Using Machine Learning Algorithms , 2018, 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence).

[12]  Mahmod S. Mahmod,et al.  A COMPARISON STUDY FOR INTRUSION DATABASE (KDD99, NSL-KDD) BASED ON SELF ORGANIZATION MAP (SOM) ARTIFICIAL NEURAL NETWORK , 2013 .

[13]  Hadi Sarvari,et al.  Improving the accuracy of intrusion detection systems by using the combination of machine learning approaches , 2010, 2010 International Conference of Soft Computing and Pattern Recognition.