Hybrid Leap Boost (HLB) Algorithm for Detection of Attacks Using Intrusion Detection in Cloud Network

The rapid revolution of technology development made the growth of communication and network technology towards increasing scale. Massive connectivity of devices over the cloud allows the unauthorized users to inject malwares. Intrusion attacks are security issues commonly occur in cloud connected systems that create degradation in cloud security. In terms of cloud security, the impacted parameters that involved in the attack scenario is important. The proposed system focused on Brute force FTP attack (BF-FTP), Brute force SSH attack (BF-SSH), DoS attack, DDoS attack, Web attack (WA), Botnet (BN) etc. The novel algorithm is derived through Sailing frog leap-based optimization algorithm for feature mapping, Density weighted boosted regression (DWB) for classification mapping. The Novel hybrid leap boost algorithm (HLB) is formulated here. The proposed approach focused on classification accuracy on specified attacks from labeled IDS2017 dataset. The (CIC) Canadian institute of cyber security dataset is the publicly available reliable dataset on smart city recording. The data is unbalanced in nature. Classification of CIC data with proposed newly innovated HLB process is challenging. The system achieved the accuracy of 91%. Further the impacted values are required to be enhanced in research scope.