Ransomware and Artificial Intelligence: A Comprehensive Systematic Review of Reviews

• URL: http://arxiv.org/abs/2603.13734v1
• Date: Sat, 14 Mar 2026 03:39:39 GMT
• Title: Ransomware and Artificial Intelligence: A Comprehensive Systematic Review of Reviews
• Abstract summary: This study gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies.<n>The findings highlight the effectiveness of hybrid models that combine multiple analysis techniques.<n>It examines key challenges in ransomware defense, including techniques designed to deceive AI-driven detection systems.
• Score: 1.6863755729554886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study provides a comprehensive synthesis of Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), in ransomware defense. Using a "review of reviews" methodology based on PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies during the past five years (2020-2024). The findings highlight the effectiveness of hybrid models that combine multiple analysis techniques such as code inspection (static analysis) and behavior monitoring during execution (dynamic analysis). The study also explores anomaly detection and early warning mechanisms before encryption to address the increasing complexity of ransomware. In addition, it examines key challenges in ransomware defense, including techniques designed to deceive AI-driven detection systems and the lack of strong and diverse datasets. The results highlight the role of AI in early detection and real-time response systems, improving scalability and resilience. Using a systematic review-of-reviews approach, this study consolidates insights from multiple review articles, identifies effective AI models, and bridges theory with practice to support collaboration among academia, industry, and policymakers. Future research directions and practical recommendations for cybersecurity practitioners are also discussed. Finally, this paper proposes a roadmap for advancing AI-driven countermeasures to protect critical systems and infrastructures against evolving ransomware threats.

Related papers

• AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders [0.6524460254566904]
  Cyberpsychology (CPSY) is defined as the use of psychology to enhance cybersecurity applications.<n>The review presents a comprehensive taxonomy of the cyber-security applications, the AI methodologies used, and the psychological concepts employed across the studies.
  arXiv  Detail & Related papers  (2026-07-14T17:18:30Z)
• Explainable Attention-Based LSTM Framework for Early Detection of AI-Assisted Ransomware via File System Behavioral Analysis [0.0]
  Ransomware continues to evolve as one of the most disruptive cyber threats.<n>Early detection of such attacks remains a significant challenge.<n>This study proposes an explainable attention-based framework for the early detection of AI assisted ransomware variants.
  arXiv  Detail & Related papers  (2026-04-19T16:27:57Z)
• Techniques of Modern Attacks [51.56484100374058]
  Advanced Persistent Threats (APTs) represent a complex method of attack aimed at specific targets.<n>I will investigate both the attack life cycle and cutting-edge detection and defense strategies proposed in recent academic research.<n>I aim to highlight the strengths and limitations of each approach and propose more adaptive APT mitigation strategies.
  arXiv  Detail & Related papers  (2026-01-19T22:15:25Z)
• A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives [65.3369988566853]
  Recent studies have demonstrated that adversaries can replicate a target model's functionality.<n>Model Extraction Attacks pose threats to intellectual property, privacy, and system security.<n>We propose a novel taxonomy that classifies MEAs according to attack mechanisms, defense approaches, and computing environments.
  arXiv  Detail & Related papers  (2025-08-20T19:49:59Z)
• Security through the Eyes of AI: How Visualization is Shaping Malware Detection [15.361074638653477]
  We evaluate existing visualization-based approaches applied to malware detection and classification.<n>Within this framework, we analyze state-of-the-art approaches across the critical stages of the malware detection pipeline.<n>We shed light on the main challenges in visualization-based approaches and provide insights into the advancements and potential future directions in this critical field.
  arXiv  Detail & Related papers  (2025-05-12T13:53:56Z)
• 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.<n>This paper provides a comprehensive review of the impact of AE attacks on key cybersecurity applications.<n>We explore recent advancements in defense mechanisms, including gradient masking, adversarial training, and detection techniques.
  arXiv  Detail & Related papers  (2024-12-16T01:54:07Z)
• Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process [39.677420930301736]
  Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for fault diagnosis in ACS using the Tennessee Eastman Process dataset.
  arXiv  Detail & Related papers  (2024-03-20T10:59:06Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• 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 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)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.