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Abstract <p>Vehicle network data is being created in massive quantities due to the Internet of Vehicles (IoV) rapid expansion. Network communication security is challenged by the volume of data. The significant amount of data created within the vehicle network presents time-consuming detection issues, even while intrusion detection technologies can help protect the system from unwanted attacks. In this manuscript, advancing connected vehicle security using advanced deep learning and optimized feature selection for intrusion detection (ACVS-OFSID-NCGNN) is proposed. The CIC-IDS-2017 dataset is where the data is first gathered. The gathered information is then sent to preprocessing. In prior to processing, to employ unsharp mask guided filtering (UMGF), identify the missing values, clean the data and standardization are carried out. Next, the previously processed data are provided to superb fairy-wren optimization algorithm (SFOA) for feature selection. SFOA selected 10&#xa0;optimum features features from CIC-IDS-2017 information. The node-level capsule graph neural network (NCGNN) is then fed the chosen features to detecting the intrusion and classify as benign, brute force, DoS, portscan, web attack, bot, and infiltration. generally speaking, NCGNN doesn’t articulate how to modify optimization techniques to identify the best parameters to guarantee Internet of Vehicles. Therefore, the purpose of the house swallow optimizer (HSO) is to optimize the node-level capsule graph neural network, which correctly classifies intrusion detection. The proposed EID-IoV-NCGNN Python is used to implement this strategy. Using performance criteria such as accuracy, recall, FPR, precision, F1-score, and detection time, the effectiveness of the suggested approach was evaluated. The proposed NCGNN-HSO approach.</p>

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Advancing Connected Vehicle Security Using Advanced Deep Learning and Optimized Feature Selection for Intrusion Detection

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • M. Udaya Prakash Reddy,
  • Yugandhar Garapati

摘要

Abstract

Vehicle network data is being created in massive quantities due to the Internet of Vehicles (IoV) rapid expansion. Network communication security is challenged by the volume of data. The significant amount of data created within the vehicle network presents time-consuming detection issues, even while intrusion detection technologies can help protect the system from unwanted attacks. In this manuscript, advancing connected vehicle security using advanced deep learning and optimized feature selection for intrusion detection (ACVS-OFSID-NCGNN) is proposed. The CIC-IDS-2017 dataset is where the data is first gathered. The gathered information is then sent to preprocessing. In prior to processing, to employ unsharp mask guided filtering (UMGF), identify the missing values, clean the data and standardization are carried out. Next, the previously processed data are provided to superb fairy-wren optimization algorithm (SFOA) for feature selection. SFOA selected 10 optimum features features from CIC-IDS-2017 information. The node-level capsule graph neural network (NCGNN) is then fed the chosen features to detecting the intrusion and classify as benign, brute force, DoS, portscan, web attack, bot, and infiltration. generally speaking, NCGNN doesn’t articulate how to modify optimization techniques to identify the best parameters to guarantee Internet of Vehicles. Therefore, the purpose of the house swallow optimizer (HSO) is to optimize the node-level capsule graph neural network, which correctly classifies intrusion detection. The proposed EID-IoV-NCGNN Python is used to implement this strategy. Using performance criteria such as accuracy, recall, FPR, precision, F1-score, and detection time, the effectiveness of the suggested approach was evaluated. The proposed NCGNN-HSO approach.