Review of Anomaly Detection Methods During System Call Auditing in OS
Abstract
This paper presents a systematic review of state-of-the-art techniques for anomaly detection in system calls—a critical component of Host-based Intrusion Detection Systems (HIDS). The analyzed approaches include: statistical models, machine learning and deep learning methods, hybrid architectures, and sequence analysis techniques. Particular attention is paid to assessing their applicability in contemporary computing environments, such as Internet of Things (IoT) devices, containers, cloud platforms, and mobile operating systems.
The study results show that state-of-the-art techniques can be categorized based on key characteristics. Deep learning architectures achieve the lowest false positive rate (FPR), but this comes at the cost of significant computational resources. Hybrid approaches demonstrate a more balanced combination of accuracy and efficiency, yet they may remain vulnerable to sophisticated, adaptive attacks. Resource-efficient solutions find application in constrained IoT environments; however, their effectiveness is often diminished by sensitivity to data imbalance. The research confirms that the choice of the optimal method is determined by the target platform. For resource-constrained IoT devices, lightweight models are preferred, while in cloud environments, comprehensive solutions capable of delivering low FPR demonstrate maximum efficiency. Key unresolved challenges remain the high false positive rate and the computational complexity of processing real-time data streams.
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