Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.

Wind Driven Butterfly Optimization Algorithm with Hybrid Mechanism Avoiding Natural Enemies for Global Optimization and PID Controller Design

This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabilities,the butterfly actions were divided into downwind and upwind states.The algorithm of exploration ability was improved with the wind,while the algorithm of exploitation ability was improved against the wind.Also,a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching ability.Aiming at improving the explorative performance at the initial stages and later stages,the fragrance generation method was modified.To evaluate the effectiveness of the suggested algorithm,a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark functions.Further,the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC 2020.Finally,the WDBOA algorithm is used proportional-integral-derivative(PID)controller parameter optimization.Experimental results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm(GA),Flower Pollination Algorithm(FPA),Cuckoo Search(CS)and BOA.

Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems

This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version.Due to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance strategy.Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint problems.Various performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance comparison.It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared quantitatively.From all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.

mLBOA:A Modified Butterfly Optimization Algorithm with Lagrange Interpolation for Global Optimization

Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its performance.The proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and exploitation.Also,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better searching.To evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark suite.The results have been compared to that of six state-of-the-art algorithms and five BOA variants.Moreover,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed mLBOA.Finally,the mLBOA has been applied to solve three real-world engineering design problems.From all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.

A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

Many respiratory infections around the world have been caused by coronaviruses.COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate.There is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems.Machine learning(ML)has been used to examine chest X-ray frames.In this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal person.First,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature extraction.Second,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN models.These selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray images.The proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

A Hybrid Moth Flame Optimization Algorithm for Global Optimization

The Moth Flame Optimization(MFO)algorithm shows decent performance results compared to other meta-heuristic algorithms for tackling non-linear constrained global optimization problems.However,it still suffers from obtaining quality solution and slow convergence speed.On the other hand,the Butterfly Optimization Algorithm(BOA)is a comparatively new algorithm which is gaining its popularity due to its simplicity,but it also suffers from poor exploitation ability.In this study,a novel hybrid algorithm,h-MFOBOA,is introduced,which integrates BOA with the MFO algorithm to overcome the shortcomings of both the algorithms and at the same time inherit their advantages.For performance evaluation,the proposed h-MFOBOA algorithm is applied on 23 classical benchmark functions with varied complexity.The tested results of the proposed algorithm are compared with some well-known traditional meta-heuristic algorithms as well as MFO variants.Friedman rank test and Wilcoxon signed rank test are employed to measure the performance of the newly introduced algorithm statistically.The computational complexity has been measured.Moreover,the proposed algorithm has been applied to solve one constrained and one unconstrained real-life problems to examine its problem-solving capability of both type of problems.The comparison results of benchmark functions,statistical analysis,real-world problems confirm that the proposed h-MFOBOA algorithm provides superior results compared to the other conventional optimization algorithms.