Detection of Remote Code Execution vulnerability in website source codes using LSTM machine learning model

This paper presents a novel approach to the detection of Remote Code Execution (RCE) vulnerabilities in website source codes using Long Short-Term Memory (LSTM) machine learning model. RCE vulnerabilities are a significant security concern for web applications, as they can be exploited by attackers to execute arbitrary code on the server. Traditional static code analysis and rule-based methods have limitations in effectively identifying such vulnerabilities, as they often struggle to capture the complex patterns and behaviors of RCE exploits. In this research, we propose an LSTM-based model trained on a dataset of source code snippets to automatically learn and detect patterns indicative of RCE vulnerabilities. Experimental results demonstrate that the LSTM model shows promising performance in accurately identifying RCE vulnerabilities in web application source codes, thus providing a valuable tool for enhancing the security of web applications. This approach contributes to the advancement of automated and efficient RCE vulnerability detection, thereby assisting in proactive mitigation of security risks in web development.

Detection of Remote Code Execution vulnerability in website source codes using LSTM machine learning model Keywords:

RCE , Vulnerabilities , LSTM , Machine learning