Strategic Deployment of Honeypots in Blockchain-based IoT Systems

• URL: http://arxiv.org/abs/2405.12951v1
• Date: Tue, 21 May 2024 17:27:00 GMT
• Title: Strategic Deployment of Honeypots in Blockchain-based IoT Systems
• Authors: Daniel Commey, Sena Hounsinou, Garth V. Crosby,
• Abstract summary: It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. The model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks.
• Score: 1.3654846342364306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of Things (BIoTs) systems, which are increasingly vulnerable to sophisticated cyberattacks. It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. This model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks. The paper analyses strategic interactions between potential attackers and the AI-enhanced IDS through a game-theoretic model, specifically Bayesian games. The model focuses on understanding and predicting sophisticated attacks that may initially appear normal, emphasizing strategic decision-making, optimized honeypot deployment, and adaptive strategies in response to evolving attack patterns.

Related papers

• Cyber Knowledge Completion Using Large Language Models [1.4883782513177093]
  Integrating the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) has expanded their cyber-attack surface. Assessing the risks of CPSs is increasingly difficult due to incomplete and outdated cybersecurity knowledge. Recent advancements in Large Language Models (LLMs) present a unique opportunity to enhance cyber-attack knowledge completion.
  arXiv  Detail & Related papers  (2024-09-24T15:20:39Z)
• 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)
• 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)
• IoT Botnet Detection Using an Economic Deep Learning Model [0.0]
  This paper proposes an economic deep learning-based model for detecting IoT botnet attacks along with different types of attacks. The proposed model achieved higher accuracy than the state-of-the-art detection models using a smaller implementation budget and accelerating the training and detecting processes.
  arXiv  Detail & Related papers  (2023-02-03T21:41:17Z)
• Dynamics-aware Adversarial Attack of Adaptive Neural Networks [75.50214601278455]
  We investigate the dynamics-aware adversarial attack problem of adaptive neural networks. We propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient. Our LGM achieves impressive adversarial attack performance compared with the dynamic-unaware attack methods.
  arXiv  Detail & Related papers  (2022-10-15T01:32:08Z)
• AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks [1.1172382217477126]
  We present AdIoTack, a system that highlights vulnerabilities of decision trees against adversarial attacks. To assess the model for the worst-case scenario, AdIoTack performs white-box adversarial learning to launch successful volumetric attacks. We demonstrate how the model detects all non-adversarial volumetric attacks on IoT devices while missing many adversarial ones.
  arXiv  Detail & Related papers  (2022-03-18T08:18:03Z)
• 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)
• TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack [46.79557381882643]
  We present TANTRA, a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack. Our evasion attack utilizes a long short-term memory (LSTM) deep neural network (DNN) which is trained to learn the time differences between the target network's benign packets. TANTRA achieves an average success rate of 99.99% in network intrusion detection system evasion.
  arXiv  Detail & Related papers  (2021-03-10T19:03:38Z)
• Blockchained Federated Learning for Threat Defense [0.0]
  This research paper introduces the development of an intelligent Threat Defense system, employing Federated Learning. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods.
  arXiv  Detail & Related papers  (2021-02-25T09:16:48Z)
• Robust Federated Learning with Attack-Adaptive Aggregation [45.60981228410952]
  Federated learning is vulnerable to various attacks, such as model poisoning and backdoor attacks. We propose an attack-adaptive aggregation strategy to defend against various attacks for robust learning.
  arXiv  Detail & Related papers  (2021-02-10T04:23:23Z)
• Towards Learning-automation IoT Attack Detection through Reinforcement Learning [14.363292907140364]
  Internet of Things (IoT) networks have unique characteristics, which make the attack detection more challenging. In addition to the traditional high-rate attacks, the low-rate attacks are also extensively used by IoT attackers to obfuscate the legitimate traffic. We propose a reinforcement learning-based attack detection model that can automatically learn and recognize the transformation of the attack pattern.
  arXiv  Detail & Related papers  (2020-06-29T06:12:45Z)

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.