Cybersecurity has become a critical concern due to the exponential growth of the Internet of Things (IoT), computer networks, and associated applications, which have introduced new vulnerabilities and increased the ri...
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Cybersecurity has become a critical concern due to the exponential growth of the Internet of Things (IoT), computer networks, and associated applications, which have introduced new vulnerabilities and increased the risk of cyberattacks. Detecting such anomalies and designing an efficient intrusion detection system (IDS) is essential to secure interconnected systems. Therefore, this paper proposes an enhancing cybersecurity using optimized anti-interference dynamic integral neural network-based intrusion detection system (AIDINN-CSD). Here, the input data is collected through CIC IoT 2022 dataset. The input CIC IoT 2022 dataset is preprocessed using smoothing-sharpening filter (SSF) for handling missing values and data normalization. Synthetic minority oversampling technique (SMOTE) is used for data balancing. Then, the tyrannosaurus optimizationalgorithm (TOA) selects relevant features from the preprocessed data. The selected features are used by anti-interference dynamic integral neural network (AIDINN) for detecting normal and attack class from the data. Then, the weight parameters of AIDINN are optimized using capuchin search optimization algorithm (CSOA) for improving accuracy and lowering computational time. The results show that the proposed technique attains 99.23% accuracy rate, 98.97% precision and 98.47% detection rate by outperforming existing techniques. These results show the effectiveness of the AIDINN-CSD in addressing the limitations of conventional IDS, particularly its ability to handle imbalanced datasets and reduce false positives thereby offering a promising solution for enhancing IoT network security and mitigating cyber threats.
Vehicular Ad Hoc Networks (VANETs) is enhancing comfort and traffic control and have brought about a paradigm shift in the design of contemporary transportation systems. However, as smart sensing technologies become m...
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Vehicular Ad Hoc Networks (VANETs) is enhancing comfort and traffic control and have brought about a paradigm shift in the design of contemporary transportation systems. However, as smart sensing technologies become more widely used with the advent of the Internet of Things (IoT), intruders have found vehicular sensor networks to be a soft target. In this article, an optimized Attention Induced Multi Head Convolutional Neural Network for Intrusion Detection System in VANETs (AIMHCNN-IDS-VANET) is proposed. The data is collected from the CAN_HCRL_OTIDS dataset. This data is fed to a pre-processing segment where Tanh-based normalization (ThN) is used to normalize the data. Then, the pre-processed data serves as input to AIMHCNN which classifies the data into denial of service (DoS) attack, fuzzy attack, impersonation attack, and normal (attack-free). In general, AIMHCNN doesn't express some adaption of optimization approaches to determine optimal parameters to assure accurate classification of attack detection. Hence, the capuchin search optimization algorithm is proposed to enhance the weight parameter of the AIMHCNN classifier, which precisely classifies the IDS. The proposed method is implemented and its efficacy is analyzed on several performance parameters. The method is observed to attain higher accuracy, higher precision, and higher specificity when compared with existing methods.
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