Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourcefluxes,and failures.Recently,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides,the GTO-FTASS model derives afitness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a task.In addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.

An Optimized Approach to Deep Learning for Botnet Detection and Classification for Cybersecurity in Internet of Things Environment

The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices.Anomaly detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual activities.Machine learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device behaviour.In addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to botnets.Various ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT systems.To overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)technique.The GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT environment.For data preprocessing,the min-max data normalization approach is primarily used.The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets.Moreover,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet detection.Finally,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM method.The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.