Open Access

ARTICLE

Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

by Zibo Wang^1,3, Chaobin Huo², Yaofang Zhang^1,3, Shengtao Cheng^1,3, Yilu Chen^1,3, Xiaojie Wei⁵, Chao Li⁴, Bailing Wang^1,3,*

1 School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China
2 National Computer System Engineering Research Institute of China, Beijing, 100083, China
3 School of Cyber Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
4 Weihai Cyberguard Technologies Co. Ltd., Weihai, 264209, China
5 Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, Netherlands

* Corresponding Author: Bailing Wang. Email:

Computers, Materials & Continua 2023, 75(2), 2957-2979. https://doi.org/10.32604/cmc.2023.035694

Received 31 August 2022; Accepted 26 October 2022; Issue published 31 March 2023

Abstract

With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths. The proposed method consists of three primary building blocks: KG construction, relation path representation, and query relation reasoning. A security-oriented ontology combines exploit modeling, which provides a guideline for the integration of the scattered knowledge while constructing the KG. We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning. In order to acquire a high-quality representation, the entity and relation embeddings take advantage of their local structure and related semantics. Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity. In particular, similarity calculation is introduced into a critical path selection algorithm, which improves search and reasoning performance. Meanwhile, the proposed algorithm avoids redundant paths between the given pairs of entities. Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

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Keywords

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