Intelligent Attacks on Cyber-Physical Systems and Critical Infrastructures

• URL: http://arxiv.org/abs/2501.12762v1
• Date: Wed, 22 Jan 2025 09:54:58 GMT
• Title: Intelligent Attacks on Cyber-Physical Systems and Critical Infrastructures
• Authors: Alan Oliveira de Sá, Charles Bezerra Prado, Mariana Luiza Flavio, Luiz F. Rust da C. Carmo,
• Abstract summary: This chapter provides an overview of the evolving landscape of attacks in cyber-physical systems and critical infrastructures. It highlights the possible use of Artificial Intelligence (AI) algorithms to develop intelligent cyberattacks.
• Score: 0.0
• License:
• Abstract: This chapter provides an overview of the evolving landscape of attacks in cyber-physical systems (CPS) and critical infrastructures, highlighting the possible use of Artificial Intelligence (AI) algorithms to develop intelligent cyberattacks. It describes various existing methods used to carry out intelligent attacks in Operational Technology (OT) environments and discusses AI-driven tools that automate penetration tests in Information Technology (IT) systems, which could potentially be used as attack tools. The chapter also discusses mitigation strategies to counter these emerging intelligent attacks by hindering the learning process of AI-based attacks and points to future research directions on the matter.

Related papers

• Considerations Influencing Offense-Defense Dynamics From Artificial Intelligence [0.0]
  AI can enhance defensive capabilities but also presents avenues for malicious exploitation and large-scale societal harm. This paper proposes a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society.
  arXiv  Detail & Related papers  (2024-12-05T10:05:53Z)
• 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)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• A Survey on Explainable Artificial Intelligence for Cybersecurity [14.648580959079787]
  Explainable Artificial Intelligence (XAI) aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of network cybersecurity, XAI has the potential to revolutionize the way we approach network security by enabling us to better understand the behavior of cyber threats.
  arXiv  Detail & Related papers  (2023-03-07T22:54:18Z)
• 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)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review [0.0]
  Smart grid systems are critical to the power industry, however their sophisticated architectural design and operations expose them to a number of cybersecurity threats. Artificial Intelligence (AI)-based technologies are becoming increasingly popular for detecting cyber assaults in a variety of computer settings. The present AI systems are being exposed and vanquished because of the recent emergence of sophisticated adversarial systems such as Generative Adversarial Networks (GAN)
  arXiv  Detail & Related papers  (2022-02-14T21:41:36Z)
• Automating Privilege Escalation with Deep Reinforcement Learning [71.87228372303453]
  In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.
  arXiv  Detail & Related papers  (2021-10-04T12:20:46Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• Security and Privacy for Artificial Intelligence: Opportunities and Challenges [11.368470074697747]
  In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques. This challenge has motivated concerted research efforts into adversarial AI. We present a holistic cyber security review that demonstrates adversarial attacks against AI applications.
  arXiv  Detail & Related papers  (2021-02-09T06:06:13Z)
• 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.