NULLDect: A Dynamic Adaptive Learning Framework for Robust NULL Pointer Dereference Detection

The identification of null pointer dereference vulnerabilities has implications for software security and reliability, as well as satisfying market needs for user data protection. This study introduces NULLDect, an adaptive learning-based approach that addresses this issue using the CWE-476 (NULL Pointer Dereference) dataset. Such detection becomes essential for averting software failures and unforeseen events that could compromise system stability and security. The proposed approach combines the uses of Long-Short-Term Memory (LSTM) networks, attention mechanisms, and adaptive learning with callback techniques to produce a phenomenal accuracy rate of 0.806 by extracting features utilizing the CodeT5 paradigm. Furthermore, the work incorporates and evaluates advanced computational models, including CodeT5, BERT, UniXcoder, and NLP-based GloVe embeddings, to discover the most successful strategy for null pointer detection across many evaluation metrics. This adaptability improves model accuracy, robustness, and longevity. NULLDect’s synergistic combination of approaches defines it as a comprehensive and effective solution for detecting and mitigating NULL pointer dereference problems.