AI-Enhanced Ethical Hacking: A Linux-Focused Experiment
- URL: http://arxiv.org/abs/2410.05105v1
- Date: Mon, 7 Oct 2024 15:02:47 GMT
- Title: AI-Enhanced Ethical Hacking: A Linux-Focused Experiment
- Authors: Haitham S. Al-Sinani, Chris J. Mitchell,
- Abstract summary: The study evaluates GenAI's effectiveness across the key stages of penetration testing on Linux-based target machines.
The report critically examines potential risks such as misuse, data biases, hallucination, and over-reliance on AI.
- Score: 2.3020018305241337
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This technical report investigates the integration of generative AI (GenAI), specifically ChatGPT, into the practice of ethical hacking through a comprehensive experimental study and conceptual analysis. Conducted in a controlled virtual environment, the study evaluates GenAI's effectiveness across the key stages of penetration testing on Linux-based target machines operating within a virtual local area network (LAN), including reconnaissance, scanning and enumeration, gaining access, maintaining access, and covering tracks. The findings confirm that GenAI can significantly enhance and streamline the ethical hacking process while underscoring the importance of balanced human-AI collaboration rather than the complete replacement of human input. The report also critically examines potential risks such as misuse, data biases, hallucination, and over-reliance on AI. This research contributes to the ongoing discussion on the ethical use of AI in cybersecurity and highlights the need for continued innovation to strengthen security defences.
Related papers
- Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.
We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.
We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
arXiv Detail & Related papers (2025-02-18T02:26:50Z) - PenTest++: Elevating Ethical Hacking with AI and Automation [2.3020018305241337]
PenTest++ is an AI-augmented system that integrates automation with generative AI (GenAI) to optimise ethical hacking.
It balances automation with human oversight, ensuring informed decision-making at key stages.
arXiv Detail & Related papers (2025-02-13T16:46:23Z) - AI-Augmented Ethical Hacking: A Practical Examination of Manual Exploitation and Privilege Escalation in Linux Environments [2.3020018305241337]
This study explores the application of generative AI (GenAI) within manual exploitation and privilege escalation tasks in Linux-based penetration testing environments.
Our findings demonstrate that GenAI can streamline processes, such as identifying potential attack vectors and parsing complex outputs for sensitive data during privilege escalation.
arXiv Detail & Related papers (2024-11-26T15:55:15Z) - Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security [0.0]
This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity.
It develops an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks.
ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously.
arXiv Detail & Related papers (2024-05-09T18:15:12Z) - 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) - Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models [7.835719708227145]
Deepfakes and the spread of m/disinformation have emerged as formidable threats to the integrity of information ecosystems worldwide.
We highlight the mechanisms through which generative AI based on large models (LM-based GenAI) craft seemingly convincing yet fabricated contents.
We introduce an integrated framework that combines advanced detection algorithms, cross-platform collaboration, and policy-driven initiatives.
arXiv Detail & Related papers (2023-11-29T06:47:58Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - Cyber Security Requirements for Platforms Enhancing AI Reproducibility [0.0]
This study focuses on the field of artificial intelligence (AI) and introduces a new framework for evaluating AI platforms.
Five popular AI platforms; Floydhub, BEAT, Codalab, Kaggle, and OpenML were assessed.
The analysis revealed that none of these platforms fully incorporates the necessary cyber security measures.
arXiv Detail & Related papers (2023-09-27T09:43:46Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - 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.