Research on Cybersecurity Risks and Protection Strategies Derived from Intelligent Algorithms

Abstract

While intelligent algorithms improve the efficiency of network system operation and maintenance, they also lower the technical threshold for malicious attacks, leading to new network threats exhibiting high concealment, automation, and precision. This study focuses on the network security risks derived from intelligent algorithms, deeply analyzing the evolution mechanisms of three core vulnerabilities: intelligent identity forgery, algorithm-driven vulnerability mining, and automated traffic attacks. The study points out that traditional feature-matching-based defense models lag significantly in dealing with such dynamic threats. Based on this, this paper proposes solutions from three dimensions: dynamic perception, proactive verification, and collaborative governance, aiming to build a more adaptive, interconnected, and forward-looking network security protection system. The study argues that, facing the constantly evolving attack patterns driven by intelligent algorithms, network security governance cannot rely solely on static rules and post-incident handling. Instead, it should further strengthen the capabilities of real-time threat identification, trusted identity verification, cross-entity collaborative response, and dynamic updates of security policies, thereby providing new practical references for the iterative upgrade of network security protection systems.