Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36788
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: An Adversarial Approach for Intrusion Detection Systems Using Jacobian Saliency Map Attacks (JSMA) Algorithm
Author(s): Qureshi, Ayyaz Ul Haq
Larijani, Hadi
Yousefi, Mehdi
Adeel, Ahsan
Mtetwa, Nhamoinesu
Contact Email: ahsan.adeel1@stir.ac.uk
Keywords: intrusion detection
adversarial attacks
J SMA
NSL-KDD
network security
Issue Date: 2020
Date Deposited: 7-Mar-2025
Citation: Qureshi AUH, Larijani H, Yousefi M, Adeel A & Mtetwa N (2020) An Adversarial Approach for Intrusion Detection Systems Using Jacobian Saliency Map Attacks (JSMA) Algorithm. <i>Computers</i>, 9 (3), Art. No.: 58. https://doi.org/10.3390/computers9030058
Abstract: 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.
DOI Link: 10.3390/computers9030058
Rights: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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