Graph-Based DDoS Attack Detection in IoT Systems with Lossy Network
CoRR(2024)
摘要
This study introduces a robust solution for the detection of Distributed
Denial of Service (DDoS) attacks in Internet of Things (IoT) systems,
leveraging the capabilities of Graph Convolutional Networks (GCN). By
conceptualizing IoT devices as nodes within a graph structure, we present a
detection mechanism capable of operating efficiently even in lossy network
environments. We introduce various graph topologies for modeling IoT networks
and evaluate them for detecting tunable futuristic DDoS attacks. By studying
different levels of network connection loss and various attack situations, we
demonstrate that the correlation-based hybrid graph structure is effective in
spotting DDoS attacks, substantiating its good performance even in lossy
network scenarios. The results indicate a remarkable performance of the
GCN-based DDoS detection model with an F1 score of up to 91
observe at most a 2
loss. The findings from this study highlight the advantages of utilizing GCN
for the security of IoT systems which benefit from high detection accuracy
while being resilient to connection disruption.
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