Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in Low-Cost UAVs

• URL: http://arxiv.org/abs/2406.19881v1
• Date: Fri, 28 Jun 2024 12:44:01 GMT
• Title: Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in Low-Cost UAVs
• Authors: Ashish Sharma, SVSLN Surya Suhas Vaddhiparthy, Sai Usha Goparaju, Deepak Gangadharan, Harikumar Kandath,
• Abstract summary: This paper explores the critical issue of enhancing cybersecurity measures for low-cost, Wi-Fi-based Unmanned Aerial Vehicles (UAVs) against Distributed Denial of Service (DDoS) attacks. We have developed a detection mechanism that runs on the companion computer of the UAV system. In this work, we present the necessary steps required to build an on-board DDoS detection mechanism.
• Score: 1.6820106165213193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the critical issue of enhancing cybersecurity measures for low-cost, Wi-Fi-based Unmanned Aerial Vehicles (UAVs) against Distributed Denial of Service (DDoS) attacks. In the current work, we have explored three variants of DDoS attacks, namely Transmission Control Protocol (TCP), Internet Control Message Protocol (ICMP), and TCP + ICMP flooding attacks, and developed a detection mechanism that runs on the companion computer of the UAV system. As a part of the detection mechanism, we have evaluated various machine learning, and deep learning algorithms, such as XGBoost, Isolation Forest, Long Short-Term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), LSTM with attention, Bi-LSTM with attention, and Time Series Transformer (TST) in terms of various classification metrics. Our evaluation reveals that algorithms with attention mechanisms outperform their counterparts in general, and TST stands out as the most efficient model with a run time of 0.1 seconds. TST has demonstrated an F1 score of 0.999, 0.997, and 0.943 for TCP, ICMP, and TCP + ICMP flooding attacks respectively. In this work, we present the necessary steps required to build an on-board DDoS detection mechanism. Further, we also present the ablation study to identify the best TST hyperparameters for DDoS detection, and we have also underscored the advantage of adapting learnable positional embeddings in TST for DDoS detection with an improvement in F1 score from 0.94 to 0.99.

Related papers

• MDHP-Net: Detecting Injection Attacks on In-vehicle Network using Multi-Dimensional Hawkes Process and Temporal Model [44.356505647053716]
  In this paper, we consider a specific type of cyberattack known as the injection attack. These injection attacks have effect over time, gradually manipulating network traffic and disrupting the vehicle's normal functioning. We propose an injection attack detector, MDHP-Net, which integrates optimal MDHP parameters with MDHP-LSTM blocks to enhance temporal feature extraction.
  arXiv  Detail & Related papers  (2024-11-15T15:05:01Z)
• 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)
• MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network [9.305680247704542]
  Control Area Network (CAN) is an essential communication protocol that interacts between Electronic Control Units (ECUs) in the vehicular network. CAN is facing stringent security challenges due to innate security risks. We propose a self-supervised multi-knowledge fused anomaly detection model, called MKF-ADS.
  arXiv  Detail & Related papers  (2024-03-07T07:40:53Z)
• Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs [13.581341206178525]
  This paper presents an unsupervised-learning-based convolutional autoencoder architecture for detecting zero-day attacks. We quantise the model using Vitis-AI tools from AMD/Xilinx targeting a resource-constrained Zynq Ultrascale platform. The proposed model successfully achieves equal or higher classification accuracy (> 99.5%) on unseen DoS, fuzzing, and spoofing attacks.
  arXiv  Detail & Related papers  (2024-01-19T14:36:01Z)
• LogShield: A Transformer-based APT Detection System Leveraging Self-Attention [2.1256044139613772]
  This paper proposes LogShield, a framework designed to detect APT attack patterns leveraging the power of self-attention in transformers. We incorporate customized embedding layers to effectively capture the context of event sequences derived from provenance graphs. Our framework achieved superior F1 scores of 98% and 95% on the two datasets respectively, surpassing the F1 scores of 96% and 94% obtained by LSTM models.
  arXiv  Detail & Related papers  (2023-11-09T20:43:15Z)
• Deep Metric Learning for Unsupervised Remote Sensing Change Detection [60.89777029184023]
  Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs) The performance of existing RS-CD methods is attributed to training on large annotated datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues.
  arXiv  Detail & Related papers  (2023-03-16T17:52:45Z)
• Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests [73.32304304788838]
  This paper systematically uncovers the failure mode of non-parametric TSTs through adversarial attacks. To enable TST-agnostic attacks, we propose an ensemble attack framework that jointly minimizes the different types of test criteria. To robustify TSTs, we propose a max-min optimization that iteratively generates adversarial pairs to train the deep kernels.
  arXiv  Detail & Related papers  (2022-02-07T11:18:04Z)
• 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)
• Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection [46.67179436529369]
  Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. We propose a structured anomaly detection framework to defend WTNs by modeling thetemporal characteristics of cyber attacks in WTNs.
  arXiv  Detail & Related papers  (2020-08-26T15:56:55Z)
• Learning to Detect Anomalous Wireless Links in IoT Networks [1.0017195276758455]
  We introduce four types of wireless network anomalies that are identified at the link layer. We study the performance of threshold- and machine learning (ML)-based classifiers to automatically detect these anomalies. Our results demonstrate that; i) selected supervised approaches are able to detect anomalies with F1 scores of above 0.98, while unsupervised ones are also capable of detecting the said anomalies with F1 scores of, on average, 0.90, and ii) OC-SVM outperforms all the other unsupervised ML approaches reaching at F1 scores of 0.99 for SuddenD, 0.95 for SuddenR, 0.93 for InstaD
  arXiv  Detail & Related papers  (2020-08-12T11:03:57Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.