Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be developed.Earlier studies have developed different approaches for the detection of spam bots in OSN.But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be explored.With this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBDHDL.The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs.The technique has different stages of operations such as pre-processing,classification,and parameter optimization.Besides,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification process.In order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this domain.The experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management capabilities.Many trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in transportation.IoV communication enables the collection and distribution of real-time essential data regarding road network condition.In this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing.With this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban computing.The proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing protocol.The presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)algorithm.QCROalgorithmderives a fitness function with the help of vehicle speed,trust level,and energy level of the vehicle.In order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization Algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was conducted.The experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capability of software testing activity.Test Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution results.TCP ismainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics.The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing.In this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software testing.The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time.In addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm.In order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects.The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of cyberattacks.The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process.Furthermore,Android malware is increasing on a daily basis.So,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile devices.In this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud environment.The pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud platforms.The presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter tuning.To detect and classify the malware,the GCNN model is exploited in this work.In order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

Metaheuristics with Machine Learning Enabled Information Security on Cloud Environment

The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud environment.Cloud computing(CC)is used for storing as well as processing data.Therefore,security becomes important as the CC handles massive quantity of outsourced,and unprotected sensitive data for public access.This study introduces a novel chaotic chimp optimization with machine learning enabled information security(CCOML-IS)technique on cloud environment.The proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the network.The proposed CCOML-IS technique primarily normalizes the networking data by the use of data conversion and min-max normalization.Followed by,the CCOML-IS technique derives a feature selection technique using chaotic chimp optimization algorithm(CCOA).In addition,kernel ridge regression(KRR)classifier is used for the detection of security issues in the network.The design of CCOA technique assists in choosing optimal features and thereby boost the classification performance.A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several measures.The comparison study reported the enhanced outcomes of the CCOML-IS technique over the recent approaches interms of several measures.

A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

Multi and single carrier waveforms are utilized in cellular systems for high-speed data transmission.In The Fifth Generation(5G)system,several waveform techniques based on multi carrier waveforms are proposed.However,the Peak to Average Power Ratio(PAPR)is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the framework.The proposed article documents the study,progress,and implementation of PAPR reduction algorithms for the 5G radio framework.We compare the PAPR algorithm performance for advanced and conventional waveforms.The simulation results reveal that the advanced Partial Transmission Sequence(PTS)and Selective Mapping(SLM)methods enhanced the throughput and gain of the 5G waveforms.Furthermore,we have also analyzed the performance of Orthogonal Time Frequency Space Modulation(OTFSM)based on a single carrier system and found that PAPR is significantly low and is best suited to fading environments.It is seen that the conventional algorithms lower the PAPR but increase the complexity.The proposed PTS and SLM have shown good performance with low computational complexity.

Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively.

Intelligent Machine Learning Based EEG Signal Classification Model

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was conducted.The outcomes were examined under distinct aspects.The experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

In recent years,wireless networks are widely used in different domains.This phenomenon has increased the number of Internet of Things(IoT)devices and their applications.Though IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks periodically.This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks.The Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks.The Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this scenario.The current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT environment.The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature selection.Next,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment.Finally,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the hyperparameters.This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty.The proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based application.The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor nodes.The clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems.With this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted WSN.The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN environment.In addition,an effective multihop route planning approach was designed by the use of EHGS technique.The proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS algorithm.Moreover,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal routes.The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications.In spite of this,it is challenging to design energy-efficient WSN.The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network.In order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network development.In this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for WSN.The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN.To accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution quality.Besides,the inclusion of DE in traditional AOA helps in overcoming local optima problems.In addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance.In order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.