AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce

• URL: http://arxiv.org/abs/2302.07953v1
• Date: Wed, 15 Feb 2023 21:21:20 GMT
• Title: AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce
• Authors: Sudip Mittal, Jingdao Chen
• Abstract summary: Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust. The security of these systems is becoming increasingly important to prevent cyber-attacks that could lead to privacy invasion, critical operations sabotage, and bodily harm. This course description includes details about seven self-contained and adaptive modules on "AI security threats against pervasive robotic systems"
• Score: 0.9137554315375919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust. With growing dependency on physical robots that work in close proximity to humans, the security of these systems is becoming increasingly important to prevent cyber-attacks that could lead to privacy invasion, critical operations sabotage, and bodily harm. The current shortfall of professionals who can defend such systems demands development and integration of such a curriculum. This course description includes details about seven self-contained and adaptive modules on "AI security threats against pervasive robotic systems". Topics include: 1) Introduction, examples of attacks, and motivation; 2) - Robotic AI attack surfaces and penetration testing; 3) - Attack patterns and security strategies for input sensors; 4) - Training attacks and associated security strategies; 5) - Inference attacks and associated security strategies; 6) - Actuator attacks and associated security strategies; and 7) - Ethics of AI, robotics, and cybersecurity.

Related papers

• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [70.93622520400385]
  This paper systematically quantifies the robustness of VLA-based robotic systems. We introduce an untargeted position-aware attack objective that leverages spatial foundations to destabilize robotic actions. We also design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• 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)
• Interoperability and Explicable AI-based Zero-Day Attacks Detection Process in Smart Community [0.0]
  This paper aims to explain how future technologies such as 6G mobile communication, Internet of Everything (IoE), Artificial Intelligence (AI), and Smart Contract embedded WPA3 protocol-based WiFi-8 can work together to prevent known attack vectors and provide protection against zero-day attacks.
  arXiv  Detail & Related papers  (2024-08-06T03:11:36Z)
• 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)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities [4.466887678364242]
  Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions. We begin by surveying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems.
  arXiv  Detail & Related papers  (2023-10-12T17:54:20Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)
• CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models [0.0]
  A need to identify system vulnerabilities, potential threats, characterize properties that will enhance system robustness. A secondary need is to share this AI security threat intelligence between different stakeholders like, model developers, users, and AI/ML security professionals. In this paper, we create and describe a prototype system CTI4AI, to overcome the need to methodically identify and share AI/ML specific vulnerabilities and threat intelligence.
  arXiv  Detail & Related papers  (2022-08-16T00:16:58Z)
• 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)
• 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.