A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference

• URL: http://arxiv.org/abs/2207.10810v1
• Date: Sat, 16 Jul 2022 22:08:12 GMT
• Title: A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference
• Authors: Joseanne Viana, Hamed Farkhari, Luis Miguel Campos, Pedro Sebastiao, Katerina Koutlia, Sandra Lagen, Luis Bernardo, Rui Dinis
• Abstract summary: This research offers a deep learning (DL) approach for detecting attacks in UAVs equipped with multiplexing (OFDM) receivers on Clustered Delay Line (CDL) channels. The prospective algorithm is generalizable regarding attack identification, which does not occur during training. A deeper investigation into the timing requirements for recognizing attacks show that after training, the minimum time necessary after the attack begins is 100 ms.
• Score: 3.1230069539161405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When users exchange data with Unmanned Aerial vehicles - (UAVs) over air-to-ground (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements in a city. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a deep learning (DL) approach for detecting attacks in UAVs equipped with orthogonal frequency division multiplexing (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The prospective algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks show that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. Our algorithm also detects moving attackers from a distance of 500 m.

Related papers

• 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)
• Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges [137.47736805685457]
  We show that interference can still be effectively mitigated even without knowing its channel information. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.
  arXiv  Detail & Related papers  (2023-05-11T18:06:46Z)
• Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation [3.3253720226707992]
  Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations. We propose Deep Attention Recognition (DAtR) as a solution to identify attacks based on a small deep network embedded in authenticated UAVs.
  arXiv  Detail & Related papers  (2023-03-03T17:10:35Z)
• A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters [3.468596481227013]
  This paper presents the first synthetic dataset for Unmanned Aerial Vehicle (UAV) attacks in 5G and beyond networks. The main objective of this data is to enable deep network development for UAV communication security. The proposed dataset provides insights into network functionality when static or moving UAV attackers target authenticated UAVs in an urban environment.
  arXiv  Detail & Related papers  (2022-11-05T15:12:51Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• 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)
• Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset [3.473643536900493]
  This paper presents two strategies to identify Jammers in UAV networks. The first strategy is based on time series approaches for anomaly detection. The second is based on newly designed deep networks.
  arXiv  Detail & Related papers  (2022-03-21T22:32:37Z)
• Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication [53.46235596543596]
  We study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. We propose a novel multi-agent deep reinforcement learning framework, coined a graph attention exchange network (GAXNet) GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.
  arXiv  Detail & Related papers  (2021-05-22T12:43:25Z)
• 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)
• Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks [17.2528983535773]
  This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN. It can detect and classify four types of jamming attacks: constant jamming, random jamming, jamming, and reactive jamming. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.
  arXiv  Detail & Related papers  (2020-04-13T17:21:45Z)

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.