With the development of communication systems, information securities remain one of the main concerns for the last few years. The\nsmart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios. Intruders are trying to attack\nthe network and capture the organizationâ??s important information for its own benefits. Intrusion detection is a way of identifying\nsecurity violations and examining unwanted occurrences in a computer network. Building an accurate and effective identification system\nfor intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication. In the\nproposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high\nsecurity of the network. A convolution neural network based approach is followed for the feature classification and malicious data\nidentification purposes. In the end, comparative results are generated after evaluating the performance of the proposed algorithm to\nother rival algorithms in the proposed field. These comparative algorithms were FGSM, JSMA, C&W, and ENM. After evaluating the\nperformance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different\nparametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores. These\nresults reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the\napplicability of the proposed algorithms in the network security field.
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