Industrial Control System (ICS) is vital to critical infrastructures, yet it faces increasing security threats. Current Intrusion Detection System (IDS) designed for ICS often overlooks the unbalanced resource distribution among devices at different layers and primarily focus on known attacks, rendering it difficult to be deployed on all key nodes and vulnerable to unknown threats. To address above issues, we propose a Combined Intrusion Detection Framework (CIDF). This innovative approach is based on strategy of “multi-level layered deployment, combined detection”, deploying the Packet Signature model and the Enhanced Fast Search and Find of Density Peaks (EFSFDP) model on devices at different layers. To achieve optimal use of resource and full protection for ICS and combining the advantages of multiple detection methods to effective detect both known and unknown attacks. The Evaluation using a public gas pipeline dataset and a private dataset shows our approach outperforms existing methods, achieving an average Accuracy, Precision, and Recall of 94%, 95.5%, and 86.5% respectively, and along with superior detection speed.

Binary code similarity detection for cross-platform is widely used in plagiarism detection, malware detection and vulnerability search, aiming to detect whether two binary functions over different platforms are similar. Existing cross-architecture approaches mainly rely on the approximate matching calculation of complex high-dimensional features, such as graph, which are inevitably slow and unsuitable for large-scale applications. To solve this problem, we propose a novel approach based on index table called CBSDI, improving efficiency by screening a batch of mismatched functions before similarity detection. We select three features and compare them across architectures to select the most appropriate one to construct the index table, and this table can be embedded in other tools. The evaluation shows that the index table can roughly cut the computational costs in half when there are few errors. Moreover, compared with the related works in the literature, our proposed approach can improve not only the efficiency but also the accuracy.