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NIS is a comprehensive discipline involving computer science, network technology, communication technology, cryptography, information security technology, applied mathematics, number theory, and information theory. It mainly means that information systems (including hardware, software, data, humans, the physical environment, and infrastructure) are protected from damage, change, and disclosure due to accidental or malicious reasons (Rzym et al., 2024). The system operates continuously, reliably, and normally, and the information service is not interrupted.
Finally, business continuity is realized. With the rapid development of internet technology and the diversification of hacker attack methods, NIS is facing a huge threat in recent years. Information security incidents such as web page tampering, computer viruses, illegal system intrusion, data disclosure, website fraud, service paralysis, and illegal exploitation of vulnerabilities occur from time to time (Andrade-Hoz et al., 2024). Therefore, how to detect and defend network attacks has become a topic of concern. Network attacks generally attack the system and resources by using loopholes and security defects in the network information system (Yun et al., 2024).
Threats are mainly divided into man-made threats and natural threats. Natural threats come from various natural disasters, harsh site environments, electromagnetic interference, natural aging of network equipment, etc. Man-made threats are man-made attacks on the NIS. By looking for the weakness of the system, the purpose of destroying, cheating, and stealing data and information is achieved in an unauthorized way (Palma et al., 2024). In contrast, many types of well-designed man-made attack threats are difficult to prevent. These are the attacks prevention efforts should focus on.
Network attack detection is the primary concern for NIS, and the resulting network attack detection systems are diverse, such as open-source HIDS security, Snort, Huawei NIP series intrusion detection system, Venustech IDS, and NSFOCUS NIDS, all with their own characteristics (Kong et al., 2024).
Although the research on network attack detection has never stopped, there are still deficiencies in the face of the same endless attack methods. From the perspective of communication, any new network information technology is bound to be accompanied by new attack modes and characteristics, making it more difficult to automatically extract network attack characteristics, which results in the loss of effectiveness of network attack detection technology through fixed rule matching (Casado-Vara et al., 2024). Moreover, in the real environment, real-time response to network attack means is required, so there is not enough time to slowly mark the attack samples. Under the condition of capturing a small number of samples, the detection system needs to accurately find the intrusion virus (Kan & Fang, 2024). The emergence of new attack technologies greatly tests the real-time performance of the system. In addition, artificial intelligence (AI) technology based on deep learning has developed rapidly in recent years and has been applied to network attack technology by hackers. This has made attack methods more and more intelligent, requiring the use of AI technology as part of the continuous updating of defense technology (Hasas et al., 2024).
In summary, NIS has always been a topic of concern for scholars. The detection technology of network attacks must be constantly updated and developed to better face the various network attack methods, and the use of AI technology is a new development trend.