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Energy Aware Secure Cyber-Physical systems with Clustered Wireless Sensor Networks

Computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： Masoud Alajmi Mohamed K.Nour Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

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***:Hybrid Deep Learning for Enhancing IoT Network intrusion detection

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Computers, Materials & Continua 2021年第12期69卷 3945-3966页

作者： Ziadoon K.Maseer Robiah Yusof Salama A.Mostafa Nazrulazhar Bahaman Omar Musa Bander Ali Saleh Al-rimy Faculty of Information and Communication Technology Universiti Teknikal MalaysiaMelaka76100Malaysia Center of Intelligent and Autonomous systems Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohor86400Malaysia Faculty of Business and Technology UNITAR International UniversitySelangor47301Malaysia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor81310Malaysia

With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access ***,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of ***,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern ***,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of ***,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(***)model to detect existing and novel *** HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)*** CICIDS2017 dataset is selected to evaluate the *** model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced *** have compared the performance of the *** model with three recent *** results show the *** model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,***,it can detect the occurrence of low-frequency attacks that are undetectable by other models.

关键词： Cyberattacks internet of things intrusion detection system deep learning neural network supervised and unsupervised deep learning

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A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly detection

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Computers, Materials & Continua 2021年第2期66卷 2217-2227页

作者： Lewis Nkenyereye Bayu Adhi Tama Sunghoon Lim Department of Computer and Information Security Sejong UniversitySeoul05006Korea Data Science Group Institute for Basic Science(IBS)Daejeon34126Korea Department of Industrial Engineering Ulsan National Institute of Science and TechnologyUlsan44919Korea

An anomaly-based intrusion detection system(A-IDS)provides a critical aspect in a modern computing infrastructure since new types of attacks can be *** prevalently utilizes several machine learning algorithms(ML)for detecting and classifying network *** date,lots of algorithms have been proposed to improve the detection performance of A-IDS,either using individual or ensemble *** particular,ensemble learners have shown remarkable performance over individual learners in many applications,including in cybersecurity ***,most existing works still suffer from unsatisfactory results due to improper ensemble *** aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS,where deep learning(e.g.,deep neural network[DNN])is used as base learner *** effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics,i.e.,Matthew’s correlation coefficient,accuracy,and false alarm *** results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.

关键词： Anomaly detection deep neural network intrusion detection system stacking ensemble

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FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack detection

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China Communications 2020年第5期17卷 151-167页

作者： Liangchen Chen Shu Gao Baoxu Liu Zhigang Lu Zhengwei Jiang School of Computer Science and Technology Wuhan University of TechnologyWuhan 430063China Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Applied Technology China University of Labor RelationsBeijing 100048China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method

关键词： fuzzy entropy weighted KNN network attack detection fuzzy membership natural nearest neighbor network security intrusion detection system

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DDoS Attack detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

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Computers, Materials & Continua 2023年第5期75卷 3571-3588页

作者： Yousef Sanjalawe Turke Althobaiti Cybersecurity Department Faculty of Information TechnologyAmerican University of Madaba(AUM)Amman11821Jordan Computer Science Department PY CollageNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Computer Science Department Faculty of ScienceNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Remote Sensing Northern Border University(NBU) 9280 NBUAr’arSaudi Arabia

intrusion detection system(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few *** the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest *** the CC environment,Distributed Denial of Service(DDoS)attacks are *** cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial *** various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic ***,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack ***,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed *** results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,*** a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

关键词： CIC IDS 2017 cloud computing distributed denial of service ensemble feature selection intrusion detection system

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Improved Dragonfly Optimizer for intrusion detection Using Deep Clustering CNN-PSO Classifier

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Computers, Materials & Continua 2022年第3期70卷 5949-5965页

作者： K.S.Bhuvaneshwari K.Venkatachalam S.Hubalovsky P.Trojovsky P.Prabu Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Sceince CHRIST(Deemed to be University)Bangalore560074India

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and *** on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating ***,the large volume of data is generated and transferred through network,the security and performance are remained an ***(intrusion detection system)was developed to detect and prevent the intruders and secure the network *** performance and loss are still an issue because of the features space grows while detecting the *** this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and *** proposed system includes three phases such as preprocessing,feature selection and *** the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization *** second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks *** clustering loss and network loss can be reduced with the optimization *** evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation *** experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.

关键词： intrusion detection system binning normalization deep clustering convolutional neural network information gain dragonfly optimizer

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IoT-CDS:Internet of Things Cyberattack Detecting system Based on Deep Learning Models

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Computers, Materials & Continua 2024年第12期81卷 4265-4283页

作者： Monir Abdullah Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT *** learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT *** rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT *** DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT *** BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal *** experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy *** are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack *** method,without feature selection,demonstrates advantages in training time and detection ***,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.

关键词： Cyberattacks intrusion detection system deep learning internet of things

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Enhance intrusion detection in Computer Networks Based on Deep Extreme Learning Machine

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Computers, Materials & Continua 2021年第1期66卷 467-480页

作者： Muhammad Adnan Khan Abdur Rehman Khalid Masood Khan Mohammed A.Al Ghamdi Sultan H.Almotiri Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan School of Computer Science NCBA&ELahore54000Pakistan Computer Science Department Umm Al-Qura UniversityMakkah City715Saudi Arabia

Networks provide a significant function in everyday life,and cybersecurity therefore developed a critical field of *** intrusion detection system(IDS)becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the *** advancements of growth,current intrusion detection systems also experience difficulties in enhancing detection precision,growing false alarm levels and identifying suspicious *** order to address above mentioned issues,several researchers concentrated on designing intrusion detection systems that rely on machine learning *** learning models will accurately identify the underlying variations among regular information and irregular information with incredible *** intelligence,particularly machine learning methods can be used to develop an intelligent intrusion detection *** in this article in order to achieve this objective,we propose an intrusion detection system focused on a Deep extreme learning machine(DELM)which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important *** the moment,we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system *** experimental results illustrate that the suggested framework outclasses traditional *** fact,the suggested framework is not only of interest to scientific research but also of functional importance.

关键词： intrusion detection system DELM network security machine learning

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Enhancing Internet of Things intrusion detection Using Artificial Intelligence

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Computers, Materials & Continua 2024年第10期81卷 1-23页

作者： Shachar Bar P.W.C.Prasad Md Shohel Sayeed School of Computing MathematicsCharles Sturt UniversityBathurstNSW 2795Australia International School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Science and Technology Multimedia UniversityMelaka75450Malaysia

Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate *** aim of intrusion detection systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and *** research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and *** contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training *** demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different *** findings identify the GNN(a Deep Learning AI method)as the most efficient IDS *** novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy *** research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.

关键词： Anomaly detection artificial intelligence cyber security data privacy deep learning federated learning industrial internet of things internet of things intrusion detection system machine learning

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Improved Supervised and Unsupervised Metaheuristic-Based Approaches to Detect intrusion in Various Datasets

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 265-298页

作者： Ouail Mjahed Salah El Hadaj El Mahdi El Guarmah Soukaina Mjahed Faculty of Sciences and Technology Department of Computer SciencesCadi Ayyad UniversityMarrakech40000Morocco Mathematics and Informatics Department Royal Air SchoolMarrakech40000Morocco

Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than *** IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic *** intrusion detection data usually contains several features,it is essential to select the best ones *** Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS ***,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 ***,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)*** metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are *** experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising ***-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related ***,the best-p

关键词： Classification neural networks Fuzzy C-means metaheuristic algorithm CICIDS2017 intrusion detection system

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