mlsanityData to 14 Oct 2026

Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System

arXiv 2310.03334 ↗First posted 5 Oct 2023cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

Network Intrusion Detection System (NIDS) is a key component in securing the computer network from various cyber security threats and network attacks. However, consider an unfortunate situation where the NIDS is itself attacked and vulnerable more specifically, we can say, How to defend the defender?. In Adversarial Machine Learning (AML), the malicious actors aim to fool the Machine Learning (ML) and Deep Learning (DL) models to produce incorrect predictions with intentionally crafted adversarial examples. These adversarial perturbed examples have become the biggest vulnerability of ML and DL based systems and are major obstacles to their adoption in real-time and mission-critical applications such as NIDS. AML is an emerging research domain, and it has become a necessity for the in-depth study of adversarial attacks and their defence strategies to safeguard the computer network from various cyber security threads. In this research work, we aim to cover important aspects related to NIDS, adversarial attacks and its defence mechanism to increase the robustness of the ML and DL based NIDS. We implemented four powerful adversarial attack techniques, namely, Fast Gradient Sign Method (FGSM), Jacobian Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini & Wagner (C&W) in NIDS. We analyzed its performance in terms of various performance metrics in detail. Furthermore, the three heuristics defence strategies, i.e., Adversarial Training (AT), Gaussian Data Augmentation (GDA) and High Confidence (HC), are implemented to improve the NIDS robustness under adversarial attack situations. The complete workflow is demonstrated in real-time network with data packet flow. This research work provides the overall background for the researchers interested in AML and its implementation from a computer network security point of view.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 27 of its 68 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

27 of 68 references.

References · 27

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2013Intriguing properties of neural networkscited by 5,155
  4. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  5. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  6. 2016Adversarial Machine Learning at Scalecited by 936
  7. 2017Ensemble Adversarial Training: Attacks and Defensescited by 840
  8. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  9. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
  10. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 460
  11. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 337
  12. 2018On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Modelscited by 328
15 more
  1. 2018Certified Defenses against Adversarial Examplescited by 306
  2. 2018Online Learning: A Comprehensive Surveycited by 205
  3. 2017Online Deep Learning: Learning Deep Neural Networks on the Flycited by 86
  4. 2019Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAScited by 83
  5. 2020Opportunities and Challenges in Deep Learning Adversarial Robustness: A Surveycited by 72
  6. 2017Efficient Defenses Against Adversarial Attackscited by 53
  7. 2020Adversarial Machine Learning in Network Intrusion Detection Systemscited by 25
  8. 2021Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)cited by 15
  9. 2018Adversarial Examples - A Complete Characterisation of the Phenomenoncited by 11
  10. 2019Rallying Adversarial Techniques against Deep Learning for Network Securitycited by 11
  11. 2020Adversarial Examples in Constrained Domainscited by 11
  12. 2022Adaptive Online Incremental Learning for Evolving Data Streamscited by 11
  13. 2023Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Modelcited by 10
  14. 2021Launching Adversarial Attacks against Network Intrusion Detection Systems for IoTcited by 9
  15. 2021Certified Federated Adversarial Trainingcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 1.

8 papers.

  1. 2024A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunitiescited by 4
  2. 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspectivecited by 3
  3. 2024Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layerscited by 2
  4. 2025Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigationcited by 1
  5. 2024Impact of White-Box Adversarial Attacks on Convolutional Neural Networkscited by 0
  6. 2025Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDScited by 0
  7. 2025Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matterscited by 0
  8. 2026A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Trafficcited by 0