mlsanityData to 7 Oct 2026

The Role of Machine Learning in Cybersecurity

arXiv 2206.09707 ↗First posted 20 Jun 2022cs.CR cs.LGcited by 21

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Abstract (arXiv)

Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such discrepancy has its root cause in the current state-of-the-art, which does not allow to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain -- to any potential reader with an interest in this topic. We highlight the advantages of ML with respect to human-driven detection methods, as well as the additional tasks that can be addressed by ML in cybersecurity. Moreover, we elucidate various intrinsic problems affecting real ML deployments in cybersecurity. Finally, we present how various stakeholders can contribute to future developments of ML in cybersecurity, which is essential for further progress in this field. Our contributions are complemented with two real case studies describing industrial applications of ML as defense against cyber-threats.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 35 of its 183 references that are in the map. First posted on arXiv 20 Jun 2022.

Builds on

35 of 183 references.

References · 35

  1. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
  2. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  3. 2018Certified Defenses against Adversarial Examplescited by 305
  4. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  5. 2021The Benchmark Lotterycited by 133
  6. 2018Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detectioncited by 121
  7. 2020Adversarial Machine Learning -- Industry Perspectivescited by 121
  8. 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
  9. 2019Deep Reinforcement Learning for Cyber Securitycited by 92
  10. 2016Defensive Distillation is Not Robust to Adversarial Examplescited by 60
  11. 2017Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detectioncited by 51
  12. 2020SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detectioncited by 49
23 more
  1. 2019A Survey of Network-based Intrusion Detection Data Setscited by 46
  2. 2021Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systemscited by 39
  3. 2017Survey of Machine Learning Techniques for Malware Analysiscited by 31
  4. 2018Applying Deep Learning To Airbnb Searchcited by 24
  5. 2018An Adversarial Approach for Explainable AI in Intrusion Detection Systemscited by 22
  6. 2015Reviewer Integration and Performance Measurement for Malware Detectioncited by 19
  7. 2022SoK: The Impact of Unlabelled Data in Cyberthreat Detectioncited by 15
  8. 2016DeepDGA: Adversarially-Tuned Domain Generation and Detectioncited by 14
  9. 2017DeltaPhish: Detecting Phishing Webpages in Compromised Websitescited by 13
  10. 2019Hardening Random Forest Cyber Detectors Against Adversarial Attackscited by 12
  11. 2018Evaluation of Machine Learning Algorithms for Intrusion Detection Systemcited by 9
  12. 2018Early Warnings of Cyber Threats in Online Discussionscited by 8
  13. 2022The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systemscited by 8
  14. 2019A new method for flow-based network intrusion detection using the inverse Potts modelcited by 6
  15. 2021Explanation-Guided Diagnosis of Machine Learning Evasion Attackscited by 6
  16. 2017Android Malware Clustering through Malicious Payload Miningcited by 3
  17. 2018HeNet: A Deep Learning Approach on Intel^circledR Processor Trace for Effective Exploit Detectioncited by 3
  18. 2021On the Evaluation of Sequential Machine Learning for Network Intrusion Detectioncited by 3
  19. 2020How Did That Get In My Phone? Unwanted App Distribution on Android Devicescited by 2
  20. 2021Bridging the gap to real-world for network intrusion detection systems with data-centric approachcited by 2
  21. 2015ELDA: Towards Efficient and Lightweight Detection of Cache Pollution Attacks in NDNcited by 1
  22. 2017Decision-Making and Biases in Cybersecurity Capability Development: Evidence from a Simulation Game Experimentcited by 1
  23. 2018Fast Flux Service Network Detection via Data Mining on Passive DNS Trafficcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 2; 2024: 6; 2025: 3; 2026: 6.

21 papers.

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  1. 2022"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practicecited by 55
  2. 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
  3. 2023SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detectioncited by 13
  4. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  5. 2024An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approachcited by 9
  6. 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learningcited by 7
  7. 2024DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classificationcited by 5
  8. 2024SoK: On the Offensive Potential of AIcited by 5
  9. 2022Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2cited by 4
  10. 2024Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shellscited by 1
  11. 2025Divided We Fall: Defending Against Adversarial Attacks via Soft-Gated Fractional Mixture-of-Experts with Randomized Adversarial Trainingcited by 1
  12. 2024Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networkscited by 0
9 more
  1. 2024A Systematic Literature Review on the Use of Machine Learning in Software Engineeringcited by 0
  2. 2025Enhancing Network Security Management in Water Systems using FM-based Attack Attributioncited by 0
  3. 2025Feature Attribution in 5G Intrusion Detection: A Statistical vs. Logic-Based Comparisoncited by 0
  4. 2026Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networkscited by 0
  5. 2026AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detectioncited by 0
  6. 2026Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detectioncited by 0
  7. 2026Reliable LLM-Powered Decision Engines for Large-Scale Supply Chain Operations: Architecture, Safety, and Performance Guaranteescited by 0
  8. 2026Beyond Accuracy: Centroid-Guided Contrastive Loss for Structured Fraudulent Job Posting Detectioncited by 0
  9. 2026Unmasking Shortcut Learning in IoT Intrusion Detection: A Forensic, Multi-Paradigm Evaluation of Feature Dependence and Data Leakagecited by 0