Siyi Wang, Yan Zhuang*, Zhizhuang Zhou, Xinhao Wang, Menglan Li
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3041-3066, 2025, DOI:10.32604/cmc.2025.067636
- 23 September 2025
Abstract Heap memory anomalies, such as Use-After-Free (UAF), Double-Free, and Memory Leaks, pose critical security threats including system crashes, data leakage, and remote exploits. Existing methods often fail to handle multiple anomaly types and meet real-time detection demands. To address these challenges, this paper proposes MemHookNet, a real-time multi-class heap anomaly detection framework that combines log hooking with deep learning. Without modifying source code, MemHookNet non-intrusively captures memory operation logs at runtime and transforms them into structured sequences encoding operation types, pointer identifiers, thread context, memory sizes, and temporal intervals. A sliding-window Long Short-Term Memory (LSTM) More >
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