LLM agents that filter suspicious nodes before a GNN classifier restore clean-level accuracy on a synthetic node injection attack.
Security Testbed for the Internet of Things
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time and in any place, to each other and to the Internet. One of the major problems associated with the IoT is maintaining security; the heterogeneous nature of such deployments poses a challenge to many aspects of security, including security testing and analysis. In addition, there is no existing mechanism that performs security testing for IoT devices in different contexts. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed supports both standard and context-based security testing, with a set of security tests conducted under the different environmental conditions in which IoT devices operate. The requirements and architectural design of the proposed testbed are discussed, and the testbed operation is demonstrated in several testing scenarios.
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Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis
LLM agents that filter suspicious nodes before a GNN classifier restore clean-level accuracy on a synthetic node injection attack.