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

中文翻译：

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基于卷积神经网络的高精度入侵识别系统

随着通信系统的发展，信息安全仍是最近几年的主要问题之一。连接智能设备可以通信，处理，计算和监视各种实时场景。入侵者正试图攻击网络并捕获组织的重要信息以获取自身利益。入侵检测是一种识别安全违规并检查计算机网络中不需要的事件的方法。构建用于入侵检测或恶意活动的准确有效的识别系统，可以确保现有系统的安全性，从而实现端到端的顺畅通信。在提出的研究工作中，为了精确的入侵检测目的，遵循了基于深度学习的方法，以确保网络的高安全性。遵循基于卷积神经网络的方法进行特征分类和恶意数据识别。最后，在评估了所提出算法与所提出领域中其他竞争算法的性能之后，产生了比较结果。这些比较算法是FGSM，JSMA，C＆W和ENM。在评估了这些算法和基于不同阈值范围的拟议算法的性能后，L _p范数，以及c的不同参数值，可以得出结论，该算法在L _p值较小和Kitsune分数较高的情况下表现优异。这些结果表明，所提出的研究对识别数据包的攻击是有希望的，并且也反映了所提出的算法在网络安全领域中的适用性。