DDoS Attacks Detection via Three-Tier Optimization Algorithm in Cloud Environment
Keywords:
Cloud environment, Sequential Backward Selection, Independent Component Analysis, Swarm Intelligence, unauthorized personAbstract
Cloud environment is significantly threatened by distributed denial of service (DDoS) attacks, which can quickly reduce the system's resources, keep the servers busy, and cause severe system damage. Recently, many DDoS attacks have been carried out using cunning tactics, including low-rate attacks and attacks that target authenticated users. In this paper, a novel DDoS Attack Detection (DAD) technique has been proposed to detect and recover the attacker. The suggested technique involves preprocessing process in terms of three processes including Sequential Backward Selection (SBS), Independent Component Analysis (ICA), and Sewing Training-Based Optimization (STBO). An SBS analyzes and detects unauthorized or authorized requests, as well as recover attacks. But some attacks will not recover in beginning stage so the further preprocessing process will perform using ICA under identify, retrieval, organizer. The Identification process will check whether the request is from attacker or not from the database using SBS algorithm. If the request from unauthorized person, the retrieval process will start to recover and after recover the request to store data into the organizer. Finally, the performance of the suggested DAD technique is compared with MLDMF, PSD, and ADADM and the performance analysis of the proposed technique is determined using detection rate, precision, False alarm rate, and accuracy. The proposed method achieves a higher level of accuracy is 97%.
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