With the continuous advancement of technology, malicious code has become a major challenge in the field of network security, seriously threatening data confidentiality, normal system operation, and personal privacy protection. Existing detection technologies face problems such as insufficient identification of obfuscated code, adversarial sample interference, and excessive reliance on feature libraries leading to false negatives when dealing with complex and diverse malicious code, making it difficult to effectively prevent increasingly complex network threats. This article starts with the harm of malicious code and elaborates on its negative effects in areas such as information leakage and property damage. It also compares the advantages and disadvantages of static and dynamic detection techniques and explores their limitations in application. On this basis, an optimization path is proposed, including building a framework that combines static and dynamic analysis, using artificial intelligence to achieve intelligent detection, strengthening threat intelligence sharing and collaboration, and enhancing behavior analysis and real-time monitoring of unknown malicious code, opening up new directions for enhancing the detection efficiency of network security.

Yihong Zou. Exploration of the Application of Malicious Code Detection in Network Security. International Journal of Neural Network (2025), Vol. 4, Issue 1: 65-72. https://doi.org/10.38007/NN.2025.040108.

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