检索结果-南通市图书馆

Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第9期46卷 2899-2915页

作者： Noura Alenezi Ahamed Aljuhani College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia

The rapid growth of the Internet of Things(IoT)in the industrial sector has given rise to a new term:the Industrial Internet of Things(IIoT).The IIoT is a collection of devices,apps,and services that connect physical and virtual worlds to create smart,cost-effective,and scalable systems.Although the IIoT has been implemented and incorporated into a wide range of industrial control systems,maintaining its security and privacy remains a significant concern.In the IIoT contexts,an intrusion detection system(IDS)can be an effective security solution for ensuring data confidentiality,integrity,and availability.In this paper,we propose an intelligent intrusion detection technique that uses principal components analysis(PCA)as a feature engineering method to choose the most significant features,minimize data dimensionality,and enhance detection performance.In the classification phase,we use clustering algorithms such as K-medoids and K-means to determine whether a given flow of IIoT traffic is normal or attack for binary classification and identify the group of cyberattacks according to its specific type for multi-class classification.To validate the effectiveness and robustness of our proposed model,we validate the detection method on a new driven IIoT dataset called X-IIoTID.The performance results showed our proposed detection model obtained a higher accuracy rate of 99.79%and reduced error rate of 0.21%when compared to existing techniques.

关键词： Anomaly detection anomaly-based IDS industrial internet of things(IIoT) internet of things

Secure Multifactor Remote Access User Authentication Framework for IoT Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第9期68卷 3235-3254页

作者： Mohammed Mujib Alshahrani College of Computing and Information Technology The University of BishaSaudi Arabia

The term IoT refers to the interconnection and exchange of data among devices/sensors.IoT devices are often small,low cost,and have limited resources.The IoT issues and challenges are growing increasingly.Security and privacy issues are among the most important concerns in IoT applications,such as smart buildings.Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks,where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel.Thus,remote cybersecurity attacks are a significant threat.Emerging applications in smart environments such as smart buildings require remote access for both users and resources.Since the user/building communication channel is insecure,a lightweight and secure authentication protocol is required.In this paper,we propose a new secure remote user mutual authentication protocol based on transitory identities and multi-factor authentication for IoT smart building environment.The protocol ensures that only legitimate users can authenticate with smart building controllers in an anonymous,unlinkable,and untraceable manner.The protocol also avoids clock synchronization problem and can resist quantum computing attacks.The security of the protocol is evaluated using two different methods:(1)informal analysis;(2)model check using the automated validation of internet security protocols and applications(AVISPA)toolkit.The communication overhead and computational cost of the proposed are analyzed.The security and performance analysis show that our protocol is secure and efficient.

关键词： Internet of things threats smart building attacks remote access authentication smart buildings

Quantum algorithm and experimental demonstration for the subset sum problem

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(information Sciences) 2022年第8期65卷 261-274页

作者： Qilin ZHENG Pingyu ZHU Shichuan XUE Yang WANG Chao WU Xinyao YU Miaomiao YU Yingwen LIU Mingtang DENG Junjie WU Ping XU Institute for Quantum information and State Key Laboratory of High Performance computing College of Computer Science and TechnologyNational University of Defense Technology

To solve the subset sum problem, a well-known nondeterministic polynomial-time complete problem that is widely used in encryption and resource scheduling, we propose a feasible quantum algorithm that utilizes fewer qubits to encode and achieves quadratic speedup. Specifically, this algorithm combines an amplitude amplification algorithm with quantum phase estimation, and requires n + t + 1 qubits and O(2(0.5+o(1))n) operations to obtain the solution, where n is the number of elements, and t is the number of qubits used to store the eigenvalues. To verify the performance of the algorithm, we simulate the algorithm with the online quantum simulator of IBM named ibmq simulator using Qiskit and then run it on two IBM quantum computers called ibmq santiago and ibmq bogota. The experimental results indicate that compared with the brute force algorithm, the proposed algorithm results in quadratic acceleration for the problem of a set S with four elements and two subsets whose sum equals target w. Using the iterator twice, we obtain success probabilities of 0.940 ± 0.004, 0.751 ± 0.040, and 0.665 ± 0.060 on the simulator, ibmq santiago, and ibmq bogota, respectively, and the fidelity between the theoretical and experimental quantum states is calculated to be 0.944 ± 0.002, 0.753 ± 0.017, and 0.657 ± 0.028, respectively. If the error rates of the experimental quantum logic gates can be reduced, the success probabilities of the proposed algorithm on real quantum devices can be further improved.

关键词： quantum algorithm subset sum quadratic speedup encryption algorithm complexity

An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

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 attacks.The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers.The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger.To 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)algorithm.In the ICNN-GBO model,we are injecting the triggers via a steganography and regularization technique.We are generating triggers using a single-pixel,irregular shape,and different sizes.The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and functionality.When the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target label.The proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andMSCELEB 1M dataset.The 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

An Access Control Scheme Using Heterogeneous Signcryption for IoT Environments

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第3期70卷 4307-4321页

作者： Insaf Ullah Hira Zahid Fahad Algarni Muhammad Asghar Khan Hamdard Institute of Engineering and technology Islamabad44000Pakistan Department of information technology Abbottabad University of Science and TechnologyAbbottabadPakistan College of Computing and Information Technology The University of BishaBishaSaudi Arabia

When the Wireless Sensor Network(WSN)is combined with the Internet of Things(IoT),it can be employed in a wide range of applications,such as agriculture,industry 4.0,health care,smart homes,among others.Accessing the big data generated by these applications in Cloud Servers(CSs),requires higher levels of authenticity and confidentiality during communication conducted through the Internet.Signcryption is one of the most promising approaches nowadays for overcoming such obstacles,due to its combined nature,i.e.,signature and encryption.A number of researchers have developed schemes to address issues related to access control in the IoT literature,however,the majority of these schemes are based on homogeneous nature.This will be neither adequate nor practical for heterogeneous IoT environments.In addition,these schemes are based on bilinear pairing and elliptic curve cryptography,which further requires additional processing time and more communication overheads that is inappropriate for real-time communication.Consequently,this paper aims to solve the above-discussed issues,we proposed an access control scheme for IoT environments using heterogeneous signcryption scheme with the efficiency and security hardiness of hyperelliptic curve.Besides the security services such as replay attack prevention,confidentiality,integrity,unforgeability,non-repudiations,and forward secrecy,the proposed scheme has very low computational and communication costs,when it is compared to existing schemes.This is primarily because of hyperelliptic curve lighter nature of key and other parameters.The AVISPA tool is used to simulate the security requirements of our proposed scheme and the results were under two backbends(Constraint Logic-based Attack Searcher(CL-b-AtSER)and On-the-Fly Model Checker(ON-t-FL-MCR))proved to be SAFE when the presented scheme is coded in HLPSL language.This scheme was proven to be capable of preventing a variety of attacks,including confidentiality,integrity,unforgeability,non-repudiation,forward secrecy,and repl

关键词： Internet of Things(IoT) access control big data heterogeneous signcryption

The Double Edge Sword Based Distributed Executor Service

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第8期42卷 589-604页

作者： Faisal Bahadur Arif Iqbal Umar Insaf Ullah Fahad Algarni Muhammad Asghar Khan Department of information technology Hazara UniversityMansehra21300Pakistan Hamdard Institute of Engineering and technology Islamabad 44000Pakistan College of Computing and Information Technology The University of BishaBishaSaudi Arabia

Scalability is one of the most important quality attribute of softwareintensive systems,because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload.In order to achieve scalability,thread pool system(TPS)(which is also known as executor service)has been used extensively as a middleware service in software-intensive systems.TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime.In case of distributed-TPS(DTPS),another issue is the load balancing b/w available set of TPSs running at backend servers.Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload.Consequently,the performance of software-intensive system is suffered.Thus,in this paper,we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword.On the one hand,it effectively performs the load balancing(in case of overload situation)among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs.And on the other hand,its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size,because the pool size in each TPS is kept equal to the request rate on it,hence dynamically optimizes TPS.We evaluated the results of the proposed system against state of the art DTPSs by a clientserver based simulator and found that our system outperformed by sustaining smaller response times.

关键词： Software-intensive systems distributed executor service load balancing overload monitoring multi-threading thread pool performance

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第12期77卷 3921-3942页

作者： Abdulwahab Ali Almazroi Nasir Ayub College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddah21959Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

关键词： Hybrid optimization internet of things intelligent transport system optimal path finding smart logistics traffic congestion supply chain

Coach-assistedmulti-agent reinforcement learning framework for unexpected crashed agents

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of information technology & Electronic Engineering 2022年第7期23卷 1032-1042页

作者： Jian ZHAO Youpeng ZHAO Weixun WANG Mingyu YANG Xunhan HU Wengang ZHOU Jianye HAO Houqiang LI School of information Science and technology University of Science and Technology of ChinaHefei 230026China college of Intelligence and computing Tianjin UniversityTianjin 300072China

Multi-agent reinforcement learning is difficult to apply in practice,partially because of the gap between simulated and real-world scenarios.One reason for the gap is that simulated systems always assume that agents can work normally all the time,while in practice,one or more agents may unexpectedly“crash”during the coordination process due to inevitable hardware or software failures.Such crashes destroy the cooperation among agents and lead to performance degradation.In this work,we present a formal conceptualization of a cooperative multi-agent reinforcement learning system with unexpected crashes.To enhance the robustness of the system to crashes,we propose a coach-assisted multi-agent reinforcement learning framework that introduces a virtual coach agent to adjust the crash rate during training.We have designed three coaching strategies(fixed crash rate,curriculum learning,and adaptive crash rate)and a re-sampling strategy for our coach agent.To our knowledge,this work is the first to study unexpected crashes in a multi-agent system.Extensive experiments on grid-world and StarCraft II micromanagement tasks demonstrate the efficacy of the adaptive strategy compared with the fixed crash rate strategy and curriculum learning strategy.The ablation study further illustrates the effectiveness of our re-sampling strategy.

关键词： Multi-agent system Reinforcement learning Unexpected crashed agents

Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity:A Survey

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 377-391页

作者： Jun Zhang Lei Pan Qing-Long Han Chao Chen Sheng Wen Yang Xiang IEEE the School of Science Computing and Engineering TechnologiesSwinburne University of Technology the School of information technology Deakin University the college of Science and Engineering James CookUniversity

With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains challenging.It might be the worst of times,but it might be the best of times because of opportunities brought by machine learning(ML),in particular deep learning(DL).In general,DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data.DL models are adopted quickly to cyber attacks against CPS systems.In this survey,a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context.A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems.The methodology includes CPS scenario analysis,cyber attack identification,ML problem formulation,DL model customization,data acquisition for training,and performance evaluation.The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules.Moreover,excellent performance is achieved partly because of several highquality datasets that are readily available for public use.Furthermore,challenges,opportunities,and research trends are pointed out for future research.

关键词： Cyber-physical system cybersecurity deep learning intrusion detection pattern classification