mlsanityData to 14 Oct 2026

Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems

arXiv 2303.06664 ↗First posted 12 Mar 2023cs.CR cs.AIcited by 14

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Abstract (arXiv)

Due to the numerous advantages of machine learning (ML) algorithms, many applications now incorporate them. However, many studies in the field of image classification have shown that MLs can be fooled by a variety of adversarial attacks. These attacks take advantage of ML algorithms' inherent vulnerability. This raises many questions in the cybersecurity field, where a growing number of researchers are recently investigating the feasibility of such attacks against machine learning-based security systems, such as intrusion detection systems. The majority of this research demonstrates that it is possible to fool a model using features extracted from a raw data source, but it does not take into account the real implementation of such attacks, i.e., the reverse transformation from theory to practice. The real implementation of these adversarial attacks would be influenced by various constraints that would make their execution more difficult. As a result, the purpose of this study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems (NIDS), demonstrating that it is entirely possible to fool these ML-based IDSs using our proposed adversarial algorithm while assuming as many constraints as possible in a black-box setting. In addition, since it is critical to design defense mechanisms to protect ML-based IDSs against such attacks, a defensive scheme is presented. Realistic botnet traffic traces are used to assess this work. Our goal is to create adversarial botnet traffic that can avoid detection while still performing all of its intended malicious functionality.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 25 of its 70 references that are in the map. First posted on arXiv 12 Mar 2023.

Builds on

25 of 70 references.

References · 25

  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. 2016Adversarial examples in the physical worldcited by 1,769
  6. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  7. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  8. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
  9. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 666
  10. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 546
  11. 2017On the (Statistical) Detection of Adversarial Examplescited by 180
  12. 2017SafetyNet: Detecting and Rejecting Adversarial Examples Robustlycited by 76
13 more
  1. 2018Adversarial Examples: Opportunities and Challengescited by 63
  2. 2016Defensive Distillation is Not Robust to Adversarial Examplescited by 60
  3. 2021Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systemscited by 41
  4. 2021Towards a Standard Feature Set for Network Intrusion Detection System Datasetscited by 40
  5. 2020Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectorscited by 26
  6. 2020Adversarial Feature Selection against Evasion Attackscited by 13
  7. 2022Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detectioncited by 13
  8. 2019Hardening Random Forest Cyber Detectors Against Adversarial Attackscited by 12
  9. 2020Adversarial Network Traffic: Towards Evaluating the Robustness of Deep Learning-Based Network Traffic Classificationcited by 11
  10. 2017When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Timecited by 9
  11. 2022TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systemscited by 7
  12. 2021Adversarial Training for Deep Learning-based Intrusion Detection Systemscited by 6
  13. 2021Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systemscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 7; 2026: 4.

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  1. 2025Frontier AI's Impact on the Cybersecurity Landscapecited by 21
  2. 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspectivecited by 3
  3. 2023Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Onescited by 2
  4. 2024Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layerscited by 2
  5. 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Thingscited by 1
  6. 2025A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning based Intrusion Detection Systemscited by 1
  7. 2025FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detectioncited by 0
  8. 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Reviewcited by 0
  9. 2025Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDScited by 0
  10. 2025Behavior-Aware and Generalizable Defense Against Black-Box Adversarial Attacks for ML-Based IDScited by 0
  11. 2026Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networkscited by 0
  12. 2026PLAA: Packet-level Adversarial Attacks in Network Traffic Detectioncited by 0
2 more
  1. 2026A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Trafficcited by 0
  2. 2026Probabilistic Robustness-driven Universal Adversarial Perturbations with Explainability against Deep Reinforcement Learning-based Intrusion Detection Systemcited by 0