以AI应对AI 应对网络攻击威胁更高效 | 2025年国家网络安全宣传周

Core Viewpoint - The rapid advancement of artificial intelligence (AI) technology is significantly enhancing the efficiency of cybersecurity measures against network attacks, necessitating discussions on AI risks, governance, and application safety [1]. Group 1: AI in Cybersecurity - AI is increasingly being integrated into various industries, including cybersecurity, to improve the response to network attacks [1]. - Traditional methods of handling network attacks relied heavily on human intervention, which was time-consuming and labor-intensive. The introduction of AI has streamlined this process by filtering alerts through different models [6]. - A mixed architecture of large and small models has been developed to filter alerts, achieving a noise reduction rate of over 98% for identifying genuine threats [7]. Group 2: Attack Types and Response - Large enterprises face a variety of network attack types, including web application attacks and more covert methods like trojan communications, which complicate defense efforts [3]. - AI models can assist in analyzing and tracing the origins of attacks, thereby accelerating the response time to network threats [5]. - After extracting attack information, analysis reports are generated and provided to law enforcement and relevant national departments for further action [9]. Group 3: Counteracting AI-Driven Attacks - There is a growing concern regarding the use of AI models by malicious actors to conduct network attacks, prompting the need for AI-driven defenses [11]. - AI can learn the characteristics of attacks initiated by large models, such as high frequency and fixed patterns, allowing for automated interception of these threats through trained models [13].