With the development of big data, cloud computing, and artificial intelligence technologies, web applications have become important carriers of human-computer interaction. However, their security threats continue to escalate, with attack types expanding from traditional SQL injection and cross site scripting (XSS) to new types of file free attacks, deserialization vulnerability exploitation, etc. The security of software supply chains is out of control, attack methods are diversified, and attack targets are shifting towards data trafficking and malicious code implantation, posing serious challenges to the security of individuals, enterprises, and critical infrastructure. This article is based on the development and functional extension analysis of computer platforms using JSP and JavaScript, focusing on two main threats: web vulnerabilities and web malicious code. Combining traditional program analysis and machine learning techniques, innovative detection methods are proposed: for traditional web vulnerabilities (such as XSS), a gray box fuzzy testing method based on reinforcement learning is proposed. Through static analysis to locate injection points, structured partitioning of attack payloads, and optimization of reinforcement learning models, the experimental detection rate reaches 93.75%, which is better than mainstream scanners; To address the new Java deserialization vulnerability, a hybrid attribute graph model was constructed and the exploitation chain was searched, achieving a detection rate of 80%; Targeting traditional file based malicious code (JavaScript), combining semantic analysis with BiLSTM model, generating semantic slices through program dependency graph and optimizing input with optimal accuracy; A hybrid method of dynamic monitoring, security sensitivity, and lightweight taint analysis was used to detect malicious code on the Java file free web shell, achieving a detection rate of 81.33%. The research aims to improve detection efficiency and accuracy, and respond to the evolving threats of network attacks. Future work will focus on the transferability of detection models, improvement of program analysis accuracy, establishment of standard test sets, and research on code obfuscation mitigation techniques.

Jiaji Zhou. Development and Function Extension Analysis of Computer Platform Based on JSP and JavaScript. International Journal of Multimedia Computing (2025), Vol. 6, Issue 1: 81-92. https://doi.org/10.38007/IJMC.2025.060108.

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