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Optimized Generative Adversarial Networks for Adversarial Sample Generation

Computers, materials & Continua 2022年第8期72卷 3877-3897页

作者： daniyal m.alghazzawi Syed Hamid Hasan Surbhi Bhatia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

Detecting the anomalous entity in real-time network traffic is a popular area of research in recent *** few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this *** are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal *** this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh *** discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal *** model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based mine blast harmony search algorithm(AO-mBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the *** performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,*** superiority of the hybrid AO-mBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection *** support vector machines(SVm)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.

关键词： Aquila optimizer convolutional generative adversarial networks mine blast harmony search algorithm network traffic dataset adversarial artificial intelligence techniques

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An Improved Optimized model for Invisible Backdoor Attack Creation Using Steganography

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Computers, materials & Continua 2022年第7期72卷 1173-1193页

作者： daniyal m.alghazzawi Osama Bassam J.Rabie Surbhi Bhatia Syed Hamid Hasan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

The Deep Neural Networks(DNN)training process is widely affected by backdoor *** backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning *** state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same *** overcome this problem,in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies,and in order to achieve this objective,we are developing an improved Convolutional Neural Network(ICNN)model optimized using a Gradient-based Optimization(GBO)(ICNN-GBO)*** the ICNN-GBO model,we are injecting the triggers via a steganography and regularization *** are generating triggers using a single-pixel,irregular shape,and different *** performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and *** the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target *** proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andmSCELEB 1m *** results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.

关键词： Convolutional neural network gradient-based optimization steganography backdoor attack and regularization attack

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A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

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Computer Systems Science & Engineering 2023年第7期46卷 57-72页

作者： maha Sabir Jawad Ahmad daniyal alghazzawi Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80200Saudi Arabia School of Computing Edinburgh Napier UniversityEdinburgh EH105DYUK

The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a *** several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and *** overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection *** proposed approach learns the normal and anomalous data patterns to identify the various types of network *** most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of *** optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection *** efficacy of the suggested framework is evaluated via two standard and open-source *** proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class *** performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection *** outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.

关键词： Autoencoder cybersecurity deep learning intrusion detection IoT

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Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

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Computers, materials & Continua 2021年第7期68卷 359-376页

作者： Alaa Omran Almagrabi Rashid Ali daniyal alghazzawi Abdullah AlBarakati Tahir Khurshaid Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia School of Intelligent mechatronics Engineering Sejong UniversitySeoul05006Korea Department of Electrical Engineering Yeungnam UniversityGyeongsan38541Korea

The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturing,smart logistics,smart banking,and smart *** the advancement of the IoT,connected devices become smart and intelligent with the help of sensors and ***,issues and challenges need to be addressed regarding the data reliability and protection for signicant nextgeneration IoT applications like smart *** these next-generation applications,there is a requirement for far-reaching privacy and security in the ***,blockchain systems have emerged as a key technology that changes the way we exchange *** emerging technology has revealed encouraging implementation scenarios,such as secured digital *** a technical advancement,the blockchain network has the high possibility of transforming various industries,and the next-generation healthcare IoT(HIoT)can be one of those *** have been several studies on the integration of blockchain networks and ***,blockchain-as-autility(BaaU)for privacy and security in HIoT systems requires a systematic *** paper reviews blockchain networks and proposes BaaU as one of the *** proposed BaaU-based framework for trustworthiness in the next-generation HIoT systems is divided into two *** rst scenario suggests that a healthcare service provider integrates IoT sensors such as body sensors to receive and transmit information to a blockchain network on the IoT *** second proposed scenario recommends implementing smart contracts,such as Ethereum,to automate and control the trusted devices’subscription in the HIoT services.

关键词： blockchain next-generation IoT privacy and security healthcare IoT complex healthcare applications

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Distance matrix and markov Chain Based Sensor Localization in WSN

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Computers, materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq daniyal alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance matrix and markov Chain(Dm-mC)model is presented as node localization technique in which Distance matrix and Estimation matrix are used to identify the position of the *** method further employs a markov Chain model(mCm)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the markova chain to sustain higher energy ***,the proposed Distance matrix and markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed Dm-mC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-mC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix markov chain

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Network Learning-Enabled Sensor Association for massive Internet of Things

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Computer Systems Science & Engineering 2023年第10期47卷 843-853页

作者： Alaa Omran Almagrabi Rashid Ali daniyal alghazzawi Bander A.Alzahrani Fahad m.Alotaibi Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information and Communication Technologies Universitat Pompeu FabraBarcelona08018Spain

The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with *** IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single *** is mitigated by allotting various channels to adjoining ***,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network *** an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and ***,in remote IoT networks,the utilization of machine learning(mL)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these mL ***,this paper proposes the use of an RL algorithm for GW determination and association in IoT *** this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(mAB)calculation,to investigate and determine the optimal GW with which to *** this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network *** evaluation results indicate that our intelligentmAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.

关键词： Reinforcement learning association internet of things massive IoT sensors network

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BIomT:A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of medical Things

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Computer Systems Science & Engineering 2023年第9期46卷 3667-3684页

作者： Sahar Badri Sana Ullah Jan daniyal alghazzawi Sahar Aldhaheri d Nikolaos Pitropakis Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80200Saudi Arabia School of Computing Edinburgh Napier UniversityEdinburghEH105DYUK

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of medical Things(IomT).The existing cloud-based,centralized IomT architectures are vulnerable to multiple security and privacy *** blockchain-enabled IomT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IomT *** article presents a private and easily expandable blockchain-based framework for the *** proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and *** security is ensured by incorporating an attributebased encryption ***,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IomT *** proposed framework can perform multiple healthcare-related services in a secure and trustworthy *** performance of blockchain read/write operations is evaluated in terms of transaction throughput and *** outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write *** average latency is 61 ms and 16 ms for read and write operations,respectively.

关键词： Blockchain cybersecurity IoT IomT smart healthcare

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