A Review of the Duality of Adversarial Learning in Network Intrusion: Attacks and Countermeasures

• URL: http://arxiv.org/abs/2412.13880v1
• Date: Wed, 18 Dec 2024 14:21:46 GMT
• Title: A Review of the Duality of Adversarial Learning in Network Intrusion: Attacks and Countermeasures
• Authors: Shalini Saini, Anitha Chennamaneni, Babatunde Sawyerr,
• Abstract summary: Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity. Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering. Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks.
• Score: 0.0
• License:
• Abstract: Deep learning solutions are instrumental in cybersecurity, harnessing their ability to analyze vast datasets, identify complex patterns, and detect anomalies. However, malevolent actors can exploit these capabilities to orchestrate sophisticated attacks, posing significant challenges to defenders and traditional security measures. Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity. Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering, specifically impacting Network Intrusion Detection Systems. Our research explores the intricacies and countermeasures of attacks to deepen understanding of network security challenges amidst adversarial threats. In our study, we present insights into the dynamic realm of adversarial learning and its implications for network intrusion. The intersection of adversarial attacks and defenses within network traffic data, coupled with advances in machine learning and deep learning techniques, represents a relatively underexplored domain. Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks. Through our in-depth analysis, we identify domain-specific research gaps, such as the scarcity of real-life attack data and the evaluation of AI-based solutions for network traffic. Our focus on these challenges aims to stimulate future research efforts toward the development of resilient network defense strategies.

Related papers

• Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies [4.606106768645647]
  Ad adversarial examples (AE) pose a critical challenge to the robustness and reliability of deep learning-based systems. This paper provides a comprehensive review of the impact of AE attacks on key cybersecurity applications. We explore recent advancements in defense mechanisms, including gradient masking, adversarial training, and detection techniques.
  arXiv  Detail & Related papers  (2024-12-16T01:54:07Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security [1.2369895513397127]
  Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. To efficiently secure IoT devices, real-time detection of intrusion systems is critical. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security.
  arXiv  Detail & Related papers  (2024-10-01T19:24:34Z)
• Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects [0.33554367023486936]
  This paper provides a comprehensive review of machine learning-based Network Intrusion Detection Systems (NIDS) We critically examine existing research in NIDS, highlighting key trends, strengths, and limitations. We discuss emerging challenges in the field and offer insights for the development of more robust and resilient NIDS.
  arXiv  Detail & Related papers  (2024-09-27T13:27:29Z)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• Investigating Human-Identifiable Features Hidden in Adversarial Perturbations [54.39726653562144]
  Our study explores up to five attack algorithms across three datasets. We identify human-identifiable features in adversarial perturbations. Using pixel-level annotations, we extract such features and demonstrate their ability to compromise target models.
  arXiv  Detail & Related papers  (2023-09-28T22:31:29Z)
• Designing an attack-defense game: how to increase robustness of financial transaction models via a competition [69.08339915577206]
  Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical. We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input. We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data. The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
  arXiv  Detail & Related papers  (2023-08-22T12:53:09Z)
• A reading survey on adversarial machine learning: Adversarial attacks and their understanding [6.1678491628787455]
  Adversarial Machine Learning exploits and understands some of the vulnerabilities that cause the neural networks to misclassify for near original input. A class of algorithms called adversarial attacks is proposed to make the neural networks misclassify for various tasks in different domains. This article provides a survey of existing adversarial attacks and their understanding based on different perspectives.
  arXiv  Detail & Related papers  (2023-08-07T07:37:26Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review [0.0]
  It has been found that deep learning models are vulnerable to data instances that can mislead the model to make incorrect classification decisions. This survey explores the researches that employ different aspects of adversarial machine learning in the area of network intrusion detection.
  arXiv  Detail & Related papers  (2021-12-06T19:10:23Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.