Intrusion detection using swarm intelligence

Ayyaz Ul Haq Qureshi, Hadi Larijani, Abbas Javed, Nhamoinesu Mtetwa, Jawad Ahmad

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

16 Citations (Scopus)


Recent advances in networking and communication technologies have enabled Internet-of-Things (IoT) devices to communicate more frequently and faster. An IoT device typically transmits data over the Internet which is an insecure channel. Cyber attacks such as denial-of-service (DoS), man-in-middle, and SQL injection are considered as big threats to IoT devices. In this paper, an anomaly-based intrusion detection scheme is proposed that can protect sensitive information and detect novel cyber-attacks. The Artificial Bee Colony (ABC) algorithm is used to train the Random Neural Network (RNN) based system (RNN-ABC). The proposed scheme is trained on NSL-KDD Train+ and tested for unseen data. The experimental results suggest that swarm intelligence and RNN successfully classify novel attacks with an accuracy of 91.65%. Additionally, the performance of the proposed scheme is also compared with a hybrid multilayer perceptron (MLP) based intrusion detection system using sensitivity, mean of mean squared error (MMSE), the standard deviation of MSE (SDMSE), best mean squared error (BMSE) and worst mean squared error (WMSE) parameters. All experimental tests confirm the robustness and high accuracy of the proposed scheme.

Original languageEnglish
Title of host publication2019 UK/China Emerging Technologies (UCET)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728127972
Publication statusPublished - 24 Oct 2019
Eventthe 4th International Conference on UK-China Emerging Technologies - University of Glasgow, Glasgow, United Kingdom
Duration: 21 Aug 201922 Aug 2019 (Link to conference website)

Publication series

Name2019 UK/China Emerging Technologies, UCET 2019


Conferencethe 4th International Conference on UK-China Emerging Technologies
Abbreviated titleUCET 2019
Country/TerritoryUnited Kingdom
Internet address


  • artificial Bee Colony
  • intrusion detection
  • IoT security
  • neural networks
  • swarm intelligence

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Health Informatics
  • Instrumentation


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