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An increasing number of artificial intelligence techniques are being applied to computer network security detection, so many academics have researched and analyzed this.The main aim of machine learning with the utilization of artificial intelligence in cybersecurity was to make the malware detection process more actionable, extensible, and valid than conventional methods because traditional methods required human intervention, and the network security field involved challenges of AI, which required efficient methods and theoretical processing [10].Sarker Iqbal H found that DL derived from artificial neural networks was one of the main techniques for intelligent network security systems or policies to operate intelligently today.Li Jian-hua believed that there was a broad, interdisciplinary intersection between network security and AI. Therefore, the AI model needs specific network security defense and protection technologies to combat adversarial machine learning and maintain privacy in machine learning to ensure the security of joint learning [9].
An increasing number of artificial intelligence techniques are being applied to computer network security detection, so many academics have researched and analyzed this. Sarker Iqbal H found that DL derived from artificial neural networks was one of the main techniques for intelligent network security systems or policies to operate intelligently today. Zhang Jun's research found that with the vigorous development of network attacks and cyber crimes against network physical systems, the detection of these attacks was still challenging. DL provided better performance than machine learning because of its hierarchical settings and effective algorithms for extracting useful information from training data. The deep learning model was rapidly used for network attacks against physical systems [8]. Li Jian-hua believed that there was a broad, interdisciplinary intersection between network security and AI. Therefore, the AI model needs specific network security defense and protection technologies to combat adversarial machine learning and maintain privacy in machine learning to ensure the security of joint learning [9]. Ahsan Mostofa found that machine learning is of rising importance in cybersecurity. The main aim of machine learning with the utilization of artificial intelligence in cybersecurity was to make the malware detection process more actionable, extensible, and valid than conventional methods because traditional methods required human intervention, and the network security field involved challenges of AI, which required efficient methods and theoretical processing [10]. Zhao Shanshan found that technology based on computational intelligence played a key role in addressing the network security challenges of complex systems such as the Internet of Things and cyber-physical systems. At present, the Internet of Things is facing increasingly more security problems. Zhang Zhimin examined the use of AI techniques in a broad range of cybersecurity applications and identified some limitations and challenges. Based on his findings, he proposed a conceptual human-in-the-loop intelligent cybersecurity model. The above is about the relevant research of AI in network security monitoring
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