Adversarial Machine Learning for 5G Communications Security

• URL: http://arxiv.org/abs/2101.02656v1
• Date: Thu, 7 Jan 2021 17:52:17 GMT
• Title: Adversarial Machine Learning for 5G Communications Security
• Authors: Yalin E. Sagduyu, Tugba Erpek, Yi Shi
• Abstract summary: This paper identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications. The focus is on attacks against spectrum sharing of 5G communications with incumbent users. Results indicate major vulnerabilities of 5G systems to adversarial machine learning.
• Score: 4.336971448707467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio capabilities empowered by machine learning to perform critical tasks such as spectrum awareness and spectrum sharing, they also become susceptible to new vulnerabilities due to the attacks that target the machine learning applications. This paper identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications in the context of 5G systems. The focus is on attacks against (i) spectrum sharing of 5G communications with incumbent users such as in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer authentication of 5G User Equipment (UE) to support network slicing. For the first attack, the adversary transmits during data transmission or spectrum sensing periods to manipulate the signal-level inputs to the deep learning classifier that is deployed at the Environmental Sensing Capability (ESC) to support the 5G system. For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station. Results indicate major vulnerabilities of 5G systems to adversarial machine learning. To sustain the 5G system operations in the presence of adversaries, a defense mechanism is presented to increase the uncertainty of the adversary in training the surrogate model used for launching its subsequent attacks.

Related papers

• Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks [82.3753728955968]
  We introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system.
  arXiv  Detail & Related papers  (2024-09-24T03:17:51Z)
• Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks [9.86830550255822]
  Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) make them vulnerable to increasing vectors of security and privacy attacks. We propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs privacy, and minimizing the communication overhead.
  arXiv  Detail & Related papers  (2024-07-03T12:42:31Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems [23.183028451271745]
  Magmaw is the first black-box attack methodology capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel. For proof-of-concept evaluation, we build a real-time wireless attack platform using a software-defined radio system. Surprisingly, Magmaw is also effective against encrypted communication channels and conventional communications.
  arXiv  Detail & Related papers  (2023-11-01T00:33:59Z)
• Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects [78.84264189471936]
  NextG communication systems are beginning to explore shifting this design paradigm to reliably executing a given task such as in task-oriented communications. Wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label. Task-oriented communications is considered by jointly training the transmitter, receiver and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB.
  arXiv  Detail & Related papers  (2022-12-19T17:54:36Z)
• Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals [0.41998444721319217]
  This paper provides a vulnerability analysis of spectrum sensing approaches using AI-based semantic segmentation models. It shows that mitigation methods can significantly reduce the vulnerabilities of AI-based spectrum sensing models against adversarial attacks.
  arXiv  Detail & Related papers  (2022-09-27T11:14:47Z)
• 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)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems [5.918387680589584]
  5G networks have transitioned to software-defined infrastructures. New technologies, like network function virtualization and software-defined networking, have been incorporated in the 5G core network (5GCN) architecture to enable this transition. This article presents a comprehensive security analysis framework for the 5GCN.
  arXiv  Detail & Related papers  (2021-08-07T20:07:08Z)
• A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence [152.89360859658296]
  5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in 3D space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
  arXiv  Detail & Related papers  (2020-10-19T08:56:04Z)
• NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion [0.3007949058551534]
  Before the rise of machine learning, network anomalies which could imply an attack, were detected using well-crafted rules. With the advancements of machine learning for network anomaly, it is not easy for a human to understand how to bypass a cyber-defence system. In this paper, we show that even if we build a classifier and train it with adversarial examples for network data, we can use adversarial attacks and successfully break the system.
  arXiv  Detail & Related papers  (2020-02-20T01:54:45Z)

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.