An adversarial approach for intrusion detection systems using Jacobian Saliency Map Attacks (JSMA) Algorithm

Ayyaz Ul Haq Qureshi, Hadi Larijani*, Mehdi Yousefi, Ahsan Adeel, Nhamoinesu Mtetwa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
77 Downloads (Pure)


In today’s digital world, the information systems are revolutionizing the way we
connect. As the people are trying to adopt and integrate intelligent systems into daily lives, the risks around cyberattacks on user-specific information have significantly grown. To ensure safe communication, the Intrusion Detection Systems (IDS) were developed often by using machine learning (ML) algorithms that have the unique ability to detect malware against network security
violations. Recently, it was reported that the IDS are prone to carefully crafted perturbations known as adversaries. With the aim to understand the impact of such attacks, in this paper, we have proposed a novel random neural network-based adversarial intrusion detection system (RNN-ADV). The NSL-KDD dataset is utilized for training. For adversarial attack crafting, the Jacobian Saliency
Map Attack (JSMA) algorithm is used, which identifies the feature which can cause maximum change to the benign samples with minimum added perturbation. To check the effectiveness of the proposed adversarial scheme, the results are compared with a deep neural network which indicates that
RNN-ADV performs better in terms of accuracy, precision, recall, F1 score and training epochs.
Original languageEnglish
Article number58
Number of pages14
Issue number3
Publication statusPublished - 20 Jul 2020


  • Adversarial attacks
  • Intrusion detection
  • JSMA
  • Network security


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