The integration of clusters, grids, clouds, edges and other computing platforms result in contemporary technology of jungle computing. This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time. Federated learning is a collaborative machine learning approach without centralized training data. The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior, potentially caused by malicious adversaries and it can emerge with new and unknown attacks. The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service. Moreover, the updated system model is send to the centralized server in jungle computing, to detect their pattern. Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors. In our proposed work, we have implemented an intrusion detection system that has high accuracy, low False Positive Rate (FPR) scalable, and versatile for the jungle computing environment. The execution time taken to complete a round is less than two seconds, with an accuracy rate of 96%.

Cloud computing is the technology that is currently used to provide users with infrastructure, platform, and software services effectively. Under this system, Platform as a Service (PaaS) offers a medium headed for a web development platform that uniformly distributes the requests and resources. Hackers using Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks abruptly interrupt these requests. Even though several existing methods like signature-based, statistical anomaly-based, and stateful protocol analysis are available, they are not sufficient enough to get rid of Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks and hence there is a great need for a definite algorithm. Concerning this issue, we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis, Spearman coefficient, and mitigation technique. It can easily differentiate common traffic and attack traffic. Not only that, it greatly helps the network to distribute the resources only for authenticated requests. The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99% for the numerous DoS attack as well as DDoS attacks.Keywords: Hybrid algorithm (HA); distributed denial of service (DDoS); denial of service (DoS); platform as a service (PaaS); infrastructure as a service (IaaS); software as a service (SaaS)