BR-HIDF: An Anti-Sparsity and Effective Host Intrusion Detection Framework Based on Multi-Granularity Feature Extraction

Host-based intrusion detection systems (HIDS) have been widely acknowledged as an effective approach for detecting and mitigating malicious activities. Among various data sources utilized in HIDS, system call traces have gained significant popularity due to their inherent advantage of providing fine-grained information. Nevertheless, conventional feature extraction techniques relying on system calls tend to overlook the issue of high-dimensional sparse feature space. In this paper, we conduct a theoretical analysis to investigate the underlying causes of the sparsity problem. Subsequently, we propose an anti-sparse theory (anti-ST) as a solution to address this issue. Then, we design a multi-granularity feature extraction method (MGFE), which also meets the prerequisite mathematical conditions of the anti-ST. By applying this method, we effectively reduce the size of the feature space and minimize the number of generated features, thus mitigating sparsity. Furthermore, leveraging this approach, we propose a robust and anti-sparsity host intrusion detection framework, known as the MGFE-based Host Intrusion Detection Framework (BR-HIDF). A series of experiments were conducted to evaluate the proposed framework and compare it with the state-of-the-art method. The results demonstrate that our framework achieves impressive accuracy (97.26%), precision (97.62%), recall (96.85%), and F1 score (97.23%) in the intrusion detection task, surpassing existing frameworks. Moreover, the proposed framework significantly reduces the time overhead by 38.80%, exhibiting the highest AUC value of 0.992. Furthermore, we enhance the robustness of the detection system by integrating host-based and network-based detection, which provides greater flexibility in identifying various types of attacks.