From Threat Intelligence to Firewall Rules: Semantic Relations in Hybrid AI Agent and Expert System Architectures
arXiv:2603.03911v1 Announce Type: new Abstract: Web security demands rapid response capabilities to evolving cyber threats. Agentic Artificial Intelligence (AI) promises automation, but the need for trustworthy security responses is of the utmost importance. This work investigates the role of semantic relations in extracting information for sensitive operational tasks, such as configuring security controls for mitigating threats. To this end, it proposes to leverage hypernym-hyponym textual relations to extract relevant information from Cyber Threat Intelligence (CTI) reports. By leveraging a neuro-symbolic approach, the multi-agent system automatically generates CLIPS code for an expert system creating firewall rules to block malicious network traffic. Experimental results show the superior performance of the hypernym-hyponym retrieval strategy compared to various baselines and the higher effectiveness of the agentic approach in mitigating threats.
arXiv:2603.03911v1 Announce Type: new Abstract: Web security demands rapid response capabilities to evolving cyber threats. Agentic Artificial Intelligence (AI) promises automation, but the need for trustworthy security responses is of the utmost importance. This work investigates the role of semantic relations in extracting information for sensitive operational tasks, such as configuring security controls for mitigating threats. To this end, it proposes to leverage hypernym-hyponym textual relations to extract relevant information from Cyber Threat Intelligence (CTI) reports. By leveraging a neuro-symbolic approach, the multi-agent system automatically generates CLIPS code for an expert system creating firewall rules to block malicious network traffic. Experimental results show the superior performance of the hypernym-hyponym retrieval strategy compared to various baselines and the higher effectiveness of the agentic approach in mitigating threats.
Executive Summary
This article explores the application of semantic relations in hybrid AI agent and expert system architectures to enhance web security. By leveraging hypernym-hyponym textual relations, the proposed system extracts relevant information from Cyber Threat Intelligence reports and generates firewall rules to mitigate threats. The neuro-symbolic approach demonstrates superior performance in threat mitigation, showcasing the potential of agentic AI in automating security responses.
Key Points
- ▸ The use of semantic relations to extract information from Cyber Threat Intelligence reports
- ▸ The application of a neuro-symbolic approach to generate firewall rules
- ▸ The evaluation of the proposed system's performance in mitigating threats
Merits
Effective Threat Mitigation
The proposed system demonstrates higher effectiveness in mitigating threats compared to various baselines.
Automation of Security Responses
The agentic AI approach enables automation of security responses, reducing the need for manual intervention.
Demerits
Limited Contextual Understanding
The reliance on hypernym-hyponym textual relations may limit the system's ability to understand complex contextual relationships.
Dependence on Cyber Threat Intelligence Reports
The system's effectiveness is dependent on the quality and availability of Cyber Threat Intelligence reports.
Expert Commentary
The article presents a compelling case for the application of semantic relations in hybrid AI agent and expert system architectures for web security. The use of hypernym-hyponym textual relations to extract information from Cyber Threat Intelligence reports is a novel approach that demonstrates significant potential. However, the system's limitations, such as its dependence on the quality of Cyber Threat Intelligence reports, must be carefully considered. As the field of AI in cybersecurity continues to evolve, it is essential to address these challenges and ensure that agentic AI systems are developed and deployed in a responsible and transparent manner.
Recommendations
- ✓ Further research is needed to improve the system's contextual understanding and ability to handle complex threat scenarios.
- ✓ The development of standardized frameworks for evaluating the effectiveness of agentic AI systems in cybersecurity is crucial for ensuring accountability and transparency.