Anomaly Detection using IoT Sensor-Assisted ConvLSTM Models for Connected Vehicles

Zekry, Ahmed; Sayed, Ahmed El; Moussa, Mohamed; Elhabiby, Mohamed

doi:10.1109/vtc2021-spring51267.2021.9449086

Cited by 16 publications

(16 citation statements)

References 5 publications

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“… Methods to find anomalies in spatial, temporal and Spatio-temporal elements. Where S.1 [ 33 ], S.2 [ 34 ], S.3 [ 61 ], S.4 [ 35 ], S.5 [ 62 ], S.6 [ 26 ] represent spatial anomaly detection methods, ST.1 [ 42 ], ST.2 [ 43 ], ST.3 [ 44 ], ST.4 [ 46 ], ST.5 [ 47 ], ST.6 [ 45 ] represent Spatio-temporal anomaly detection methods and T.1 [ 38 ], T.2 [ 39 ], T.3 [ 40 ], T.4 [ 41 ] represent Temporal anomaly detection methods. The colour variation represents the year when the method was first used in Robotics for anomaly detection.…”

Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

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Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

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Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

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Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

“…In another recent paper [ 44 ], the authors claimed that their ConvLSTM models identified unusual variations in spatiotemporal elements during the motions of an autonomous vehicle and termed these variations anomalies.…”

Section: Classification Of Anomalies In Armsmentioning

confidence: 99%

Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

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“…Time-series data [43,44,45,46,47,48,49,50] Smart city [51,52,53,54] Monitoring machinery health [55,5] Robotics and manufacturing [56,57,58] Detection applications for IoT sensors [59,60] Other industrial or manufacturing [61,62,63,64] Surveillance and video [65,66,67] General-purpose frameworks [68,69,70] Network and communication frameworks [71,72,73,74] User security and privacy frameworks [75,76,77] Other frameworks [78,79] Network traffic in IoT [80,81,82] Device and infrastructure security [83,21,84,85,86,87] Data transport security [88,…”

Section: Application Category Referencementioning

confidence: 99%

“…The safety and security of connected autonomous vehicles' passengers are essential for the development of autonomous vehicles. The method by Zekry et al [62] proposes an IoT sensor-assisted convolutional long short-term memory (LSTM) model for connected vehicles for anomaly detection. The method by Wang et al [63] targets log anomalies in large-scale IoT systems.…”

Section: Other Industrial or Manufacturing Applicationsmentioning

confidence: 99%

IoT anomaly detection methods and applications: A survey

2022

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“…Firstly, it is difficult to collect attack behavior data from the IoV. With the continuous development and expansion of the IoV, many new types of attack will appear [7,8]. This results in a lack of attack-related data in the dataset used to train the intrusion detection model, which reduces the intrusion detection performance due to the missed detection of attacks that have not yet appeared.…”

Section: Introductionmentioning

confidence: 99%

FL-MAAE: An Intrusion Detection Method for the Internet of Vehicles Based on Federated Learning and Memory-Augmented Autoencoder

Xing

Wang

et al. 2023

Electronics

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The Internet of Vehicles (IoV) is a network system that enables wireless communication and information exchange between vehicles and other traffic participants. Intrusion detection plays a very important role in the IoV. However, with the development of the IoV, unknown attack behaviors may appear. The lack of analysis and collection of these attack behavior has led to an imbalance in the sample data categories of the IoV intrusion detection, which causes the problem of low detection accuracy. At the same time, the intrusion detection model usually needs to upload data to the cloud for training, which will introduce the privacy risk due to of the leakage of vehicle users’ information. In this paper, we propose an intrusion detection method for the IoV based on federated learning and memory-augmented autoencoder (FL-MAAE). We add a memory module to the autoencoder model to enhance its ability to store the behavior feature patterns of the IoV, make it robust to imbalanced samples, and use the reconstruction error as the evaluation index, so as to detect unknown attacks in the IoV. We propose a federated learning based training method for the IoV intrusion detection model. Local training of intrusion detection models in roadside units can effectively protect the privacy of data resources. We also designed an aggregation method based on the performance contribution of participants to improve the reliability of model aggregation. We conducted experiments on the NSL-KDD intrusion detection dataset to evaluate the performance of the proposed method. Experimental results show that our method has the best intrusion detection performance. In the case of contaminated samples, the accuracy and F1 score of the proposed method are 9.6% and 7.39% higher than those of the comparison methods on average.

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Anomaly Detection using IoT Sensor-Assisted ConvLSTM Models for Connected Vehicles

Cited by 16 publications

References 5 publications

Anomaly Detection Methods in Autonomous Robotic Missions

Anomaly Detection Methods in Autonomous Robotic Missions

IoT anomaly detection methods and applications: A survey

FL-MAAE: An Intrusion Detection Method for the Internet of Vehicles Based on Federated Learning and Memory-Augmented Autoencoder

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