Trust-Awareness to Secure Swarm Intelligence from Data Injection Attack

• URL: http://arxiv.org/abs/2211.08407v4
• Date: Wed, 10 May 2023 10:28:53 GMT
• Title: Trust-Awareness to Secure Swarm Intelligence from Data Injection Attack
• Authors: Bin Han, Dennis Krummacker, Qiuheng Zhou, and Hans D. Schotten
• Abstract summary: swarm intelligence (SI) is envisaged to play an important role in future industrial Internet of Things (IIoT) that is shaped by Sixth Generation (6G) mobile communications and digital twin (DT) However, its fragility against data injection attack may halt it from practical deployment. In this paper we propose an efficient trust approach to address this security concern for SI.
• Score: 5.824096823117585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enabled by the emerging industrial agent (IA) technology, swarm intelligence (SI) is envisaged to play an important role in future industrial Internet of Things (IIoT) that is shaped by Sixth Generation (6G) mobile communications and digital twin (DT). However, its fragility against data injection attack may halt it from practical deployment. In this paper we propose an efficient trust approach to address this security concern for SI.

Related papers

• Countering Autonomous Cyber Threats [40.00865970939829]
  Foundation Models present dual-use concerns broadly and within the cyber domain specifically. Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations. This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
  arXiv  Detail & Related papers  (2024-10-23T22:46:44Z)
• An Approach To Enhance IoT Security In 6G Networks Through Explainable AI [1.9950682531209158]
  6G communication has evolved significantly, with 6G offering groundbreaking capabilities, particularly for IoT. The integration of IoT into 6G presents new security challenges, expanding the attack surface due to vulnerabilities introduced by advanced technologies. Our research addresses these challenges by utilizing tree-based machine learning algorithms to manage complex datasets and evaluate feature importance.
  arXiv  Detail & Related papers  (2024-10-04T20:14:25Z)
• High-Security Hardware Module with PUF and Hybrid Cryptography for Data Security [1.8434042562191815]
  This research highlights the rapid development of technology in the industry, particularly Industry 4.0. Despite providing efficiency, these developments also bring negative impacts, such as increased cyber-attacks. This research proposes a solution by developing a hardware security module (HSM) using a field-programmable gate array (FPGA) with physical unclonable function (PUF) authentication and a hybrid encryption data security system.
  arXiv  Detail & Related papers  (2024-09-16T02:06:49Z)
• Data Poisoning Attacks in Intelligent Transportation Systems: A Survey [8.27315203718422]
  This paper concentrates on data poisoning attack models against ITS. We identify the main ITS data sources vulnerable to poisoning attacks and application scenarios that enable staging such attacks. Our work can serve as a guideline to better understand the threat of data poisoning attacks against ITS applications, while also giving a perspective on the future development of trustworthy ITS.
  arXiv  Detail & Related papers  (2024-07-06T01:02:22Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
  This paper provides a classification of IoT malware. Major targets and used exploits for attacks are identified and referred to the specific malware. The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
  arXiv  Detail & Related papers  (2023-12-01T16:10:43Z)
• Trust-based Approaches Towards Enhancing IoT Security: A Systematic Literature Review [3.0969632359049473]
  This research paper presents a systematic literature review on the Trust-based cybersecurity security approaches for IoT. We highlighted the common trust-based mitigation techniques in existence for dealing with these threats. Several open issues were highlighted, and future research directions presented.
  arXiv  Detail & Related papers  (2023-11-20T12:21:35Z)
• Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study [37.97034388920841]
  Federated edge learning can be essential in supporting privacy-preserving, artificial intelligence (AI)-enabled activities in digital twin 6G-enabled Internet of Things (IoT) environments. We propose an anticipatory study for poisoning attacks in federated edge learning for digital twin 6G-enabled IoT environments.
  arXiv  Detail & Related papers  (2023-03-21T11:12:17Z)
• Massive Twinning to Enhance Emergent Intelligence [6.412075049216053]
  emergent intelligence (EI) exhibits various outstanding features including robustness, protection to privacy, and scalability, which makes it competitive for 6G IIoT applications. We propose to exploit the massive twinning paradigm, which 6G is envisaged to support, to reduce the data traffic in EI and therewith enhance its performance.
  arXiv  Detail & Related papers  (2022-04-20T08:51:06Z)
• AI-Empowered Data Offloading in MEC-Enabled IoV Networks [40.75165195026413]
  This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit. Various challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network.
  arXiv  Detail & Related papers  (2022-03-31T09:31:53Z)

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.