Intrusion Detection System with Machine Learning Algorithms
Keywords:
intrusion detection system, algorithms, machine learning, KDD, NSL-KDDAbstract
Machine learning has become increasingly relevant in recent years, including in IT security. Algorithms are used to train intrusion detection systems to be able to react to new attack vectors. In this work, the basics of machine learning are explained and the results of two research projects are presented in order to investigate which algorithms are suitable for training a machine-learning intrusion detection system. In addition, the software library Scikit-Learn and the software Weka, with which the implementations take place, are presented.
References
Jucknischke K. Intrusion detection system with machine-learning algorithms [Intrusion Detection System mit maschinell lernenden Algorithmen]. Proceedings [Tagungsband]. 2020: 61–70.
Stallings W. Computer security principles and practice. NJ: Pearson Education; 2015.
Eckert C. IT-Sicherheit: Konzepte-Verfahren–Protokolle [IT security: Concepts-procedures-protocols]. München: Oldenbourg Wissenschaftsverlag; 2012.
Groves NB, Wells EL, Soto EF, Marsh CL, Jaisle EM, Harvey TK, Kofler MJ. Executive functioning and emotion regulation in children with and without ADHD. Res Child Adolesc Psychopathol. 2021 Nov 11; 50((6):721–735.
Liebig KM. How did Corona Pandemic influence the adoption of the digital work-place? Doctoral dissertation. Austria: University of Innsbruck; 2021.
Scarfone K, Mell P. Guide to intrusion detection and prevention systems (idps). Gaithersburg: NIST special publication (800-94); 2007 Feb 20.
KDD. (2020). Cup 1999 Data. [Online]. Uci.edu. Available from: http://kdd.ics.uci.edu/databases/
kddcup99/kddcup99.html
Tavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications. 2009 Jul 8; 1–6.
Salih Ahmad A, Abdulazeez AM. Evaluation of classification algorithms for intrusion detection system: A review. Journal of Soft Computing and Data Mining (JSCDM). 2021 Apr 15; 2(1):
–40.
Almseidin M, Alzubi M, Kovacs S, Alkasassbeh M. Evaluation of machine learning algorithms for intrusion detection system. In 2017 IEEE 15th international symposium on intelligent systems and informatics (SISY). 2017 Sep 14; 000277–000282.
NSL-KDD. (2020). Datasets: Research: Canadian Institute for Cybersecurity. [Online]. UNB. Www.unb.ca. Available from: https://www.unb.ca/cic/datasets/nsl.html
Pathak M. (2020). Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning. [Online]. Analytics Vidhya. Available from: https://www.analyticsvidhya.com/blog/
/10/quick-guide-to-evaluation-metrics-for-supervised-and-unsupervised-machine-learning/
Frochte J, Lemmen M, Schmidt M. Concerning the integration of machine learning content in mechatronics curricula. In: Research Anthology on Machine Learning Techniques, Methods, and Applications. Pennsylvania, United States: IGI Global; 2022; pp. 379–395..
Kishore Ayyadevara V. Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R. Berkley, CA: Apress; 2018. Available from https://www.oreilly.com/library/view/pro-machine-learning/9781484235645/
Paper D. Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python. Berkley, CA: Apress; 2019 Nov 18.
Pedregosa F. Scikit‐learn: Machine learning in python Fabian. J Mach Learn Res. 2011; 12:
–2830.
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explor Newsletter. 2009 Nov 16; 11(1): 10–8.
