AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders
- URL: http://arxiv.org/abs/2607.13123v1
- Date: Tue, 14 Jul 2026 17:18:30 GMT
- Title: AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders
- Abstract summary: 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.
- Score: 0.6524460254566904
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. Cyberpsychology (CPSY) is defined as the use of psychology to enhance cybersecurity applications. Since the early 2010s, the evolution of Artificial Intelligence (AI) has increasingly integrated with CPSY, leveraging advanced data analysis to decode the distinct personality traits and behavioral patterns of victims, attackers, and defenders. In this systematic literature review (SLR), we carefully analyze 34 collected research studies of AI usage in cyberpsychology (AI-CPSY) using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The review presents a comprehensive taxonomy of the cyber-security applications, the AI methodologies used, and the psychological concepts employed across the studies . We sort the research studies into four cybersecurity applications: Anomaly Detection (AD), Vulnerability Risk Prediction (VRP), Security Awareness Training (SAT), and Authentication/Identity Verification (AIV). Within each application area, studies are further sorted according to the AI method used including machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL). Furthermore, the review identifies the most commonly utilized psychological concepts, quantify the datasets used in the field, and present their current implementation and deployment status. At last, it detect research gaps, present open challenges, and deduce the trending and most effective and emerging methodologies used across the AI-CPSY landscape.
Related papers
- Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications [0.0]
integration into Cybersecurity has garnered significant attention.<n>Current trends include the use of Generative AI, Natural Language Processing, Federated Learning for privacy-preserving collaborative training.<n>Aims to uncover meaningful insights through comparative analysis based on the employed AI techniques and reported performance.
arXiv Detail & Related papers (2026-05-17T01:44:23Z) - Ransomware and Artificial Intelligence: A Comprehensive Systematic Review of Reviews [1.6863755729554886]
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.
arXiv Detail & Related papers (2026-03-14T03:39:39Z) - From Pre-trained Models to Large Language Models: A Comprehensive Survey of AI-Driven Psychological Computing [45.96605393710063]
This survey introduces the first systematic taxonomy that organizes AI-driven psychology tasks by computational processing patterns rather than application domains.<n>We examine how computational approaches evolved from task-specific feature engineering to transfer learning and few-shot adaptation.
arXiv Detail & Related papers (2026-03-12T04:03:27Z) - Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems [47.03825808787752]
This paper transitions from literature review to practical countermeasures.<n>We report on improved AI-generated content through Large Language Models (LLMs) and multimodal systems.<n>We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI.
arXiv Detail & Related papers (2026-01-29T16:42:22Z) - AI Agents vs. Human Investigators: Balancing Automation, Security, and Expertise in Cyber Forensic Analysis [0.0]
AI agents are being adopted across digital forensic practices due to their ability to automate processes such as anomaly detection, evidence classification, and behavioral pattern recognition.<n>However, the characteristics that make AI indispensable also introduce notable risks.<n>This study presents a comparative analysis of the effectiveness of the most used AI agent, ChatGPT, and human forensic investigators.
arXiv Detail & Related papers (2026-01-20T23:52:32Z) - 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) - AI-Based Software Vulnerability Detection: A Systematic Literature Review [6.604556571951421]
This study presents a systematic review of software vulnerability detection (SVD) research from 2018 to 2023.<n>Our analysis reveals that 91% of studies use AI-based methods, with graph-based models being the most prevalent.<n>We identify key limitations, including dataset quality, interpretability, and highlight emerging opportunities in underexplored techniques.
arXiv Detail & Related papers (2025-06-12T01:42:38Z) - Enhancing Cyber Security Through Predictive Analytics: Real-Time Threat Detection and Response [0.8883733362171032]
This study evaluates the application of predictive analytics for real-time cyber-attack detection and response.<n>We analyzed key features such as attack type, packet length, anomaly scores, protocol usage, and geo-location patterns to assess their predictive value.
arXiv Detail & Related papers (2024-07-15T16:11:34Z) - 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) - A Comprehensive Analysis of the Role of Artificial Intelligence and
Machine Learning in Modern Digital Forensics and Incident Response [0.0]
The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response.
This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice.
Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.
arXiv Detail & Related papers (2023-09-13T16:23:53Z) - SoK: On the Semantic AI Security in Autonomous Driving [42.15658768948801]
Autonomous Driving systems rely on AI components to make safety and correct driving decisions.
For such AI component-level vulnerabilities to be semantically impactful at the system level, it needs to address non-trivial semantic gaps.
In this paper, we define such research space as semantic AI security as opposed to generic AI security.
arXiv Detail & Related papers (2022-03-10T12:00:34Z) - 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.