Injection vulnerabilities are increasing, making webshells a serious concern for unauthorized access and running malicious code on web servers. Cybersecurity researchers found AI models excel in detecting webshells, surpassing traditional methods. Techniques include attention mechanisms, word embeddings, abstract syntax tree analysis, opcode vectorization, and more
However, these methods have limitations like inflexible filtering rules and language reliance. Challenges like unbalanced datasets, irrelevant features, and detection algorithms were tackled with techniques like de-duplication, SMOTE, and ensemble learning. Deep learning approaches such as CNN and LSTM were explored, but performance comparisons and data processing issues persisted. The debate on data representation for detecting webshells continues, with source code, opcodes, and static features suggested, each with its pros and cons. Industries are urged to collaborate for fairer representations leading to better AI training sets. ```https://cybersecuritynews.com/ai-webshell-detection-detailed-overview/