A task Offloading Approach Based on Risk Assessment to Mitigate Edge DDoS Attacks

Edge computing mitigates the high latency and other issues associated with cloud computing, but it also introduces new risks. One such issue is DDoS attacks on edge servers, which arise when edge tasks are offloaded. There is a dearth of research on countermeasures for these kinds of DDoS attacks. Consequently, we present EDM_TOS, a task offloading strategy. This technique makes sure that tasks are transferred to dependable edge nodes based on the edge nodes' risk assessment mechanism, thereby reducing edge DDoS attacks. On the other hand, EDM_TOS works better than current approaches in five domains of evaluating awareness and capacity limitations for resolving edge DDoS attacks and issues with edge computing offloading. The edge computing task offloading algorithm, weighted cluster analysis algorithm, and risk assessment algorithm, respectively, are used to implement the risk assessment module, cluster analysis module, and task offloading module of the EDM_TOS mechanism. The three modules were evaluated experimentally. The findings demonstrate that the task offloading module's transactions per second (TPS) and the risk assessment module's malicious detection rate (MDR) respectively outperform to those of other policies. The cluster analysis module's clustering effect is superior to that of the conventional K-means algorithm. EDM_TOS performs better than current methods for thwarting edge DDoS attacks, according to performance assessments on five datasets. EDM_TOS can successfully fend off edge DDoS attacks, as demonstrated by practice.