Deep learning based anomaly detection for fog-assisted IoVs network

Yaqoob, Shumayla, Hussain, Asad, Subhan, Fazli, Pappalardo, Giuseppina and Awais, Muhammad ORCID: https://orcid.org/0000-0001-6421-9245 (2023) Deep learning based anomaly detection for fog-assisted IoVs network. IEEE Access, 11. pp. 19024-19038. ISSN 2169-3536

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Abstract

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era.

Item Type:	Article
Additional Information:	Funding Information: This work was supported by the SAFE DEMON SAFE Driving by E-Health Monitoring, Sicilian Region, Italy, under Grant G29J18000720007 and Grant PON FESR 2014/2020-Action 1.1.5.
Uncontrolled Keywords:	anomaly detection,fog computing,fog-assisted iovs,internet of vehicles,smooth communication,engineering(all),materials science(all),computer science(all),sdg 11 - sustainable cities and communities ,/dk/atira/pure/subjectarea/asjc/2200
Faculty \ School:	Faculty of Science > School of Computing Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	17 Oct 2023 00:44
Last Modified:	24 Oct 2023 01:43
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93302
DOI:	10.1109/access.2023.3246660

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