So, I climbed to the top of the pyramid of pain -- now what?
- URL: http://arxiv.org/abs/2505.24685v1
- Date: Fri, 30 May 2025 15:09:03 GMT
- Title: So, I climbed to the top of the pyramid of pain -- now what?
- Authors: Vasilis Katos, Emily Rosenorn-Lanng, Jane Henriksen-Bulmer, Ala Yankouskaya,
- Abstract summary: Humal Layer Kill Chain integrates human psychology and behaviour into the analysis of cyber threats.<n>By merging the Human Layer with the Cyber Kill Chain, we propose a Sociotechnical Kill Plane.<n>This framework not only aids cybersecurity professionals in understanding adversarial methods, but also empowers non-technical personnel to engage in threat identification and response.
- Score: 1.3249509346606658
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper explores the evolving dynamics of cybersecurity in the age of advanced AI, from the perspective of the introduced Human Layer Kill Chain framework. As traditional attack models like Lockheed Martin's Cyber Kill Chain become inadequate in addressing human vulnerabilities exploited by modern adversaries, the Humal Layer Kill Chain offers a nuanced approach that integrates human psychology and behaviour into the analysis of cyber threats. We detail the eight stages of the Human Layer Kill Chain, illustrating how AI-enabled techniques can enhance psychological manipulation in attacks. By merging the Human Layer with the Cyber Kill Chain, we propose a Sociotechnical Kill Plane that allows for a holistic examination of attackers' tactics, techniques, and procedures (TTPs) across the sociotechnical landscape. This framework not only aids cybersecurity professionals in understanding adversarial methods, but also empowers non-technical personnel to engage in threat identification and response. The implications for incident response and organizational resilience are significant, particularly as AI continues to shape the threat landscape.
Related papers
- Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence [0.0]
This manuscript explores how the convergence of agentic AI and Frontier AI is transforming cybersecurity.<n>We examine the roles of real time monitoring, automated incident response, and perpetual learning in forging a resilient, dynamic defense ecosystem.<n>Our vision is to harmonize technological innovation with unwavering ethical oversight, ensuring that future AI driven security solutions uphold core human values of fairness, transparency, and accountability while effectively countering emerging cyber threats.
arXiv Detail & Related papers (2025-02-28T20:23:35Z) - Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks [0.0]
This paper delves into the escalating threat posed by the misuse of AI, specifically through the use of Large Language Models (LLMs)
Through a series of controlled experiments, the paper demonstrates how these models can be manipulated to bypass ethical and privacy safeguards to effectively generate cyber attacks.
We also introduce Occupy AI, a customized, finetuned LLM specifically engineered to automate and execute cyberattacks.
arXiv Detail & Related papers (2024-08-23T02:56:13Z) - Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme.
Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
arXiv Detail & Related papers (2023-12-20T05:06:01Z) - Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks [0.0]
Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures.
These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk.
To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity.
arXiv Detail & Related papers (2023-10-09T10:31:04Z) - Cyber Sentinel: Exploring Conversational Agents in Streamlining Security Tasks with GPT-4 [0.08192907805418582]
This paper introduces Cyber Sentinel, an innovative task-oriented cybersecurity dialogue system.
It embodies the fusion of artificial intelligence, cybersecurity domain expertise, and real-time data analysis to combat the multifaceted challenges posed by cyber adversaries.
Our work is a novel approach to task-oriented dialogue systems, leveraging the power of chaining GPT-4 models combined with prompt engineering.
arXiv Detail & Related papers (2023-09-28T13:18:33Z) - Impacts and Risk of Generative AI Technology on Cyber Defense [0.0]
We propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks.
This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques.
We also analyze the strategies employed by threat actors, highlighting the implications for cyber defense.
arXiv Detail & Related papers (2023-06-22T16:51:41Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - Towards Automated Classification of Attackers' TTPs by combining NLP
with ML Techniques [77.34726150561087]
We evaluate and compare different Natural Language Processing (NLP) and machine learning techniques used for security information extraction in research.
Based on our investigations we propose a data processing pipeline that automatically classifies unstructured text according to attackers' tactics and techniques.
arXiv Detail & Related papers (2022-07-18T09:59:21Z) - Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the
Age of AI-NIDS [70.60975663021952]
We study blackbox adversarial attacks on network classifiers.
We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions.
We show that a continual learning approach is required to study attacker-defender dynamics.
arXiv Detail & Related papers (2021-11-23T23:42:16Z) - 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.