Security Testing

Security testing aims to identify vulnerabilities in software and systems before exploitation, focusing on automated methods to improve efficiency and accuracy. Current research heavily emphasizes the application of large language models (LLMs) and deep learning architectures, such as CNNs and LSTMs, alongside traditional static and dynamic analysis techniques, to detect vulnerabilities and improve the accuracy of security testing tools. These advancements are crucial for enhancing software security, reducing the burden on human experts, and mitigating the increasing threat of sophisticated cyberattacks. Furthermore, research is exploring methods to reduce false positives and improve the efficiency of analyzing the large volume of data generated by automated security testing tools.