Cyber intrusion detection using machine learning classification techniques

Hamed Alqahtani, Iqbal H. Sarker*, Asra Kalim, Syed Md Minhaz Hossain, Sheikh Ikhlaq, Sohrab Hossain

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

76 Citations (Scopus)


As the alarming growth of connectivity of computers and the significant number of computer-related applications increase in recent years, the challenge of fulfilling cyber-security is increasing consistently. It also needs a proper protection system for numerous cyberattacks. Thus, detecting inconsistency and attacks in a computer network and developing intrusion detection system (IDS) that performs a potential role for cyber-security. Artificial intelligence, particularly machine learning techniques, has been used to develop a useful data-driven intrusion detection system. In this paper, we employ various popular machine learning classification algorithms, namely Bayesian Network, Naive Bayes classifier, Decision Tree, Random Decision Forest, Random Tree, Decision Table, and Artificial Neural Network, to detect intrusions due to provide intelligent services in the domain of cyber-security. Finally, we test the effectiveness of various experiments on cyber-security datasets having several categories of cyber-attacks and evaluate the effectiveness of the performance metrics, precision, recall, f1-score, and accuracy.

Original languageEnglish
Title of host publicationComputing Science, Communication and Security
Subtitle of host publicationFirst International Conference, COMS2 2020 Gujarat, India, March 26–27, 2020, Revised Selected Papers
EditorsNirbhay Chaubey, Satyen Parikh, Kiran Amin
Place of PublicationSingapore
PublisherSpringer, Springer Nature
Number of pages11
ISBN (Electronic)9789811566486
ISBN (Print)9789811566479
Publication statusPublished - 2020
Event1st International Conference on Computing Science, Communication and Security, COMS2 2020 - Mehsana, India
Duration: 26 Mar 202027 Mar 2020

Publication series

NameCommunications in Computer and Information Science
Volume1235 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference1st International Conference on Computing Science, Communication and Security, COMS2 2020


  • Artificial intelligence
  • Classification
  • Cyber-attack prediction
  • Cyber-attacks
  • Cybersecurity
  • Cybersecurity analytics
  • Intrusion detection system
  • Intrusions
  • Machine learning


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