Intrusion detection and attack classification leveraging machine learning technique

Shahtaj Shaukat, Arshid Ali, Amreen Batool, Fehaid Alqahtani, Jan Sher Khan, Arshad, Jawad Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Due to the advancement in information exchange over the Internet and mobile technologies, malicious network attacks have significantly increased. Machine learning algorithms can play a vital role in network security and attacks classification. This paper compares two different types of classifiers (Naive Bayes and Decision Tree) for the intrusion detection system on the publicly available dataset. Simulations are carried out using the WEKA machine learning tool and experimentation is performed on full data and selected features using subset evaluator algorithm. The classifier performance is evaluated in terms of accuracy, specificity, recall, precision, f1-score, error rates and response time. Naive Bayes classifier performance was better in terms of computational time, however, the accuracy, error rate, f1-score, and recall values of Decision Tree were better than Naive Bayes.
Original languageEnglish
Title of host publication2020 14th International Conference on Innovations in Information Technology (IIT)
PublisherIEEE
Pages198-202
Number of pages5
ISBN (Electronic)9781728181844
ISBN (Print)9781728181851
DOIs
Publication statusPublished - 25 Dec 2020
EventIEEE 14th International Conference on Innovations in Information Technology (IIT'20) - Online
Duration: 17 Nov 202018 Nov 2020
https://conferences.uaeu.ac.ae/iit20/en/ (Link to Conference website)

Conference

ConferenceIEEE 14th International Conference on Innovations in Information Technology (IIT'20)
Abbreviated titleIIT'20
Period17/11/2018/11/20
Internet address

Keywords

  • machine learning
  • feature extraction
  • intrusion detection system
  • subset evaluator

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Computer Science Applications

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