Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV

• URL: http://arxiv.org/abs/2407.02969v1
• Date: Wed, 3 Jul 2024 10:06:15 GMT
• Title: Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV
• Authors: Abdelaziz Amara korba, Abdelwahab Boualouache, Yacine Ghamri-Doudane,
• Abstract summary: Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. As connectivity expands, cybersecurity threats have become a significant concern. We propose Zero-X, an innovative security framework that effectively detects both 0-day and N-day attacks.
• Score: 5.176552248390308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. The emergence of 5G and the forthcoming 6G networks presents an enormous potential to transform the IoV by enabling ultra-reliable, low-latency, and high-bandwidth communications. Nevertheless, as connectivity expands, cybersecurity threats have become a significant concern. The issue has been further exacerbated by the rising number of zero-day (0-day) attacks, which can exploit unknown vulnerabilities and bypass existing Intrusion Detection Systems (IDSs). In this paper, we propose Zero-X, an innovative security framework that effectively detects both 0-day and N-day attacks. The framework achieves this by combining deep neural networks with Open-Set Recognition (OSR). Our approach introduces a novel scheme that uses blockchain technology to facilitate trusted and decentralized federated learning (FL) of the ZeroX framework. This scheme also prioritizes privacy preservation, enabling both CAVs and Security Operation Centers (SOCs) to contribute their unique knowledge while protecting the privacy of their sensitive data. To the best of our knowledge, this is the first work to leverage OSR in combination with privacy-preserving FL to identify both 0-day and N-day attacks in the realm of IoV. The in-depth experiments on two recent network traffic datasets show that the proposed framework achieved a high detection rate while minimizing the false positive rate. Comparison with related work showed that the Zero-X framework outperforms existing solutions.

Related papers

• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
  Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
  arXiv  Detail & Related papers  (2025-02-14T23:11:51Z)
• A Novel Zero-Touch, Zero-Trust, AI/ML Enablement Framework for IoT Network Security [0.0]
  This paper presents a novel framework based on the integration of Zero Trust, Zero Touch, and AI/ML powered for the detection, mitigation, and prevention of DDoS attacks in modern IoT ecosystems. The focus will be on the new integrated framework by establishing zero trust for all IoT traffic, fixed and mobile 5G/6G IoT network traffic, and data security.
  arXiv  Detail & Related papers  (2025-02-05T21:03:07Z)
• A Robust Multi-Stage Intrusion Detection System for In-Vehicle Network Security using Hierarchical Federated Learning [0.0]
  In-vehicle intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations.
  arXiv  Detail & Related papers  (2024-08-15T21:51:56Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems [41.1371349978643]
  We present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy. We formulate this methodology, as an optimization, where we establish a trade-off between the latency of co-inference and the privacy-level of data.
  arXiv  Detail & Related papers  (2022-08-27T14:50:00Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [0.0]
  We propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
  arXiv  Detail & Related papers  (2022-04-08T19:06:16Z)
• 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)
• Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things [40.20432511421245]
  Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT) FEL faces two critical challenges: communication overhead and data privacy. We propose a communication-efficient and privacy-enhanced asynchronous FEL framework for edge computing in IIoT.
  arXiv  Detail & Related papers  (2020-12-08T14:11:32Z)
• Lightweight Collaborative Anomaly Detection for the IoT using Blockchain [40.52854197326305]
  Internet of things (IoT) devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. We present a distributed IoT simulation platform, which consists of 48 Raspberry Pis.
  arXiv  Detail & Related papers  (2020-06-18T14:50:08Z)

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.