Enhancing Network Resilience against DDoS Attacks: Critical Node Identification using Load based k-shell Analysis

Although IoT technology streamlines services by linking various nodes via the Internet, this interconnect brings forth security hurdles. These challenges arise from the integration itself, rendering IoT infrastructure susceptible to cyber threats such as Distributed Denial of Service (DDoS) attacks. DDoS attacks can disrupt IoT functionality, causing financial loss. Intruders mainly concentrate to overwhelm the network connecting nodes. So, the connecting nodes must be safeguarded. Investing mitigation policies on all nodes is not cost effective and hence critical nodes must be identified through ranking procedure. The prioritization of connecting nodes to enhance network resilience has been largely overlooked. This paper proposes a novel Load-based K shell algorithm for ranking critical nodes in heterogeneous networks by identification of critical nodes with the graph structure and node/edge load feature aggregation. The method is implemented and evaluated using Python module and NS3 simulation of an IoT network with various sensor nodes. The results depict the proposed approach can identify the most crucial nodes in the network, which can help to mitigate the impact of DDoS attacks and improve network resilience.