ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks

• URL: http://arxiv.org/abs/2505.07837v1
• Date: Mon, 05 May 2025 08:49:18 GMT
• Title: ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks
• Authors: Maria-Lamprini A. Bartsioka, Ioannis A. Bartsiokas, Panagiotis K. Gkonis, Dimitra I. Kaklamani, Iakovos S. Venieris,
• Abstract summary: This paper focuses on the utilization of Machine and Deep Learning (ML/DL) techniques to tackle with the common problem of eavesdropping detection.<n> ML/DL models classify users as either legitimate or malicious ones based on channel state information (CSI), position data, and transmission power.<n>According to the presented numerical results, DCNN and RF models achieve a detection accuracy approaching 100% in identifying eavesdroppers with zero false alarms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advanced fifth generation (5G) and beyond (B5G) communication networks have revolutionized wireless technologies, supporting ultra-high data rates, low latency, and massive connectivity. However, they also introduce vulnerabilities, particularly in decentralized Industrial Internet of Things (IIoT) environments. Traditional cryptographic methods struggle with scalability and complexity, leading researchers to explore Artificial Intelligence (AI)-driven physical layer techniques for secure communications. In this context, this paper focuses on the utilization of Machine and Deep Learning (ML/DL) techniques to tackle with the common problem of eavesdropping detection. To this end, a simulated industrial B5G heterogeneous wireless network is used to evaluate the performance of various ML/DL models, including Random Forests (RF), Deep Convolutional Neural Networks (DCNN), and Long Short-Term Memory (LSTM) networks. These models classify users as either legitimate or malicious ones based on channel state information (CSI), position data, and transmission power. According to the presented numerical results, DCNN and RF models achieve a detection accuracy approaching 100\% in identifying eavesdroppers with zero false alarms. In general, this work underlines the great potential of combining AI and Physical Layer Security (PLS) for next-generation wireless networks in order to address evolving security threats.

Related papers

• Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning [39.58317527488534]
  This paper proposes a novel method using hybrid statistical-deep learning for the detection, prevention, and source localization of IEC 61850 SV injection attacks.<n>It effectively discards malicious SV frames with minimal processing overhead and latency, maintains robustness against communication network latency variation and time-synchronization issues.<n>Results demonstrate the method's suitability for practical deployment in IEC 61850-compliant digital substations.
  arXiv  Detail & Related papers  (2025-07-01T07:38:22Z)
• Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks [39.08784216413478]
  This paper proposes an automated security framework targeting Physical Layer Authentication and Cross-Layer Intrusion Detection Systems.<n>The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments.
  arXiv  Detail & Related papers  (2025-02-28T01:16:11Z)
• 5G NR PRACH Detection with Convolutional Neural Networks (CNN): Overcoming Cell Interference Challenges [0.0]
  We present a novel approach to interference detection in 5G New Radio (5G-NR) networks using Convolutional Neural Networks (CNN) Our CNN-based model is designed to detect Physical Random Access Channel (PRACH) sequences amidst various interference scenarios. Experimental results show that our CNN-based approach outperforms traditional PRACH detection methods in accuracy, precision, recall and F1-score.
  arXiv  Detail & Related papers  (2024-08-21T14:33:43Z)
• DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs [10.215216950059874]
  jamming attacks pose substantial risks to the 5G network.<n>This work presents a novel deep learning-based technique for detecting jammers in CAV networks.<n>Results show that the proposed method achieves 96.4% detection rate in extra low jamming power.
  arXiv  Detail & Related papers  (2024-03-05T04:29:31Z)
• Towards Scalable Wireless Federated Learning: Challenges and Solutions [40.68297639420033]
  federated learning (FL) emerges as an effective distributed machine learning framework. We discuss the challenges and solutions of achieving scalable wireless FL from the perspectives of both network design and resource orchestration.
  arXiv  Detail & Related papers  (2023-10-08T08:55:03Z)
• Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI [46.61358701676358]
  Edge artificial intelligence (AI) has been proposed to provide high-performance computation of a conventional cloud down to the network edge. Recently, convergence of wireless sensing, computation and communication (SC$2$) for specific edge AI tasks, has aroused paradigm shift. It is paramount importance to advance fully integrated sensing, computation and communication (I SCC) to achieve ultra-reliable and low-latency edge intelligence acquisition.
  arXiv  Detail & Related papers  (2023-06-11T06:40:51Z)
• 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)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• 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)
• A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges [1.5469452301122175]
  5G networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications. Massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures.
  arXiv  Detail & Related papers  (2022-01-03T14:42:53Z)
• Machine Learning based Anomaly Detection for 5G Networks [0.0]
  This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic.
  arXiv  Detail & Related papers  (2020-03-07T00:17:08Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.