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Accurate threat hunting in industrial internet of things edge devices

Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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An intelligent cyber-physical system based-consensus algorithm for sustainable edge service provisioning in 6G-based IIoT applications

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon Jena, Soumya Ranjan Bhargavi, Kovvuri N. Ramesh, Janjhyam Venkata Naga Bozarboyevich, Abdullayev Abror Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of NextGen University International United States School of Computing and Artificial Intelligence NIMS University Jaipur India Department of CSE Aditya University Surampalem India Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun India The Department of Financial Analysis Tashkent State University of Economics Tashkent Uzbekistan Division of Research and Development Lovely Professional University Phagwara India Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology Jammu India

This paper proposes a credit-driven practical byzantine fault tolerance based on a consensus algorithm for sustainable 6G communication to address the potential threat of malicious activity in green and sustainable 6G-based Industrial Internet of Things (IIoT) applications. The dual-layer architecture allows for simultaneous verification of transactions and the execution of read-write operations. According to simulation studies, CD-PBFT enhances algorithm performance and stability while efficiently reducing validation time. Empirical findings demonstrate that CD-PBFT achieves both efficiency and security fault tolerance by reducing network transaction latency by an average of 34.8% and increasing throughput by 25.2% compared to PBFT. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Consensus algorithm

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Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery

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Scientific reports 2025年第1期15卷 17921页

作者： Youseef Alotaibi Krishnaraj Nagappan Tamilvizhi Thanarajan Surendran Rajendran Department of Software Engineering College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India. Department of Computer Science and Engineering Panimalar Engineering College Chennai 600123 India. tamilvizhi.phd.it@***. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India.

Remote sensing images (RSI), such as aerial or satellite images, produce a large-scale view of the Earth's surface, which gets them used to track and monitor vehicles from several settings, like border control, disaster response, and urban traffic surveillance. Vehicle detection and classification using RSIs is a vital application of computer vision and image processing. It contains locating and identifying vehicles from the image. It is done using many approaches that have object detection approaches, namely YOLO, Faster R-CNN, or SSD, which utilize deep learning (DL) to locate and identify the image. Additionally, the classification of vehicles from RSIs contains classification of them based on their variety, such as trucks, motorcycles, cars or buses, utilizing machine learning (ML) techniques. This article designed and developed an automated vehicle type detection and classification using a chaotic equilibrium optimization algorithm with deep learning (VDTC-CEOADL) on high-resolution RSIs. The VDTC-CEOADL technique presented examines high-quality RSIs for the accurate detection and classification of vehicles. The VDTC-CEOADL technique employs a YOLO-HR object detector with a residual network as the backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is designed for the parameter tuning of the ResNet model. For the vehicle classification process, the VDTC-CEOADL technique exploits the attention-based long-short-term memory (ALSTM) mod-el. Performance validation of the VDTC-CEOADL technique is validated on a high-resolution RSI dataset, and the results portrayed the supremacy of the VDTC-CEOADL technique in terms of different measures.

关键词： Chaotic equilibrium optimization algorithm Deep learning Remote sensing images Vehicle classification Vehicle detection

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Crowd dynamics analysis and behavior recognition in surveillance videos based on deep learning

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Multimedia Tools and Applications 2024年 1-35页

作者： Ilyas, Anum Bawany, Narmeen Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women Karachi Pakistan

Video surveillance is widely adopted across various sectors for purposes such as law enforcement, COVID-19 isolation monitoring, and analyzing crowds for potential threats like flash mobs or violence. The vast amount of data generated daily from surveillance devices holds significant potential but requires effective analysis to extract value. Detecting anomalous crowd behavior, which can lead to chaos and casualties, is particularly challenging in video surveillance due to its labor-intensive nature and susceptibility to errors. To address these challenges, this research contributes in two key areas: first, by creating a diverse and representative video dataset that accurately reflects real-world crowd dynamics across eight different categories;second, by developing a reliable framework, ‘CRAB-NET,’ for automated behavior recognition. Extensive experimentation and evaluation, using Convolutional Long Short-Term Memory networks (ConvLSTM) and Long-Term Recurrent Convolutional Networks (LRCN), validated the effectiveness of the proposed approach in accurately categorizing behaviors observed in surveillance videos. The employed models were able to achieve the accuracy score of 99.46% for celebratory crowd, 99.98% for formal crowd and 96.69% for violent crowd. The demonstrated accuracy of 97.20% for comprehensive dataset achieved by the LRCN underscores its potential to revolutionize crowd behavior analysis. It ensures safer mass gatherings and more effective security interventions. Incorporating AI-powered crowd behavior recognition like ‘CRAB-NET’ into security measures not only safeguards public gatherings but also paves the way for proactive event management and predictive safety strategies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Shellfish

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SmartCoach: Comprehensive Tutor Recommender and Student Attentiveness Measuring Platform 3

SmartCoach: Comprehensive Tutor Recommender and Student Atte...

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3rd International Conference on Advancements in computing, ICAC 2021

作者： Samarasekara, H.D.K. Hansaka, H.M.P. Rajapaksha, S.D.D. Tharaka, W.A.D.G. Manathunga, Kalpani Sriyarathna, Disni Charuka Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665408622

With the pandemic, coaching or tutoring classes became to halt as the underlying system wasn't facilitating to adapt to emergency teaching mechanisms due to sparse of relevant technologies and tutor unawareness. SmartCoach is an integrated platform that allows students to find suitable tutors based on recommendations and other parameters like proximity, previous track record etc. using sentiment analysis and natural language processing. For tutors, SmartCoach allows publishing learning materials, automatic quiz creation, tracking attendance and, attentiveness during classes using OpenCV library. Moreover tutors get a dashboard featuring critical aspects about their classes and, personal income. This research is aimed to introduce a comprehensive distance learning platform with novel technological approaches that connect both potential students and tutors to a common platform. © 2021 IEEE.

关键词： Sentiment analysis

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An optimised runtime reconfiguration of component-based software systems

An optimised runtime reconfiguration of component-based soft...

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32nd Annual IEEE International Computer software and Applications Conference, COMPSAC 2008

作者： Matevska, Jasminka Software Engineering Group Department of Computing Science University of Oldenburg Germany

ISBN: (纸本)9780769532622

Component-based software systems evolve during their life cycle in order to meet changing requirements or to improve quality properties. At the same time, maintaining continuous availability of services is an issue of increasing importance especially for business-critical systems. Runtime reconfiguration supports evolution of systems while maintaining availability of services they provide. This research abstract presents a new approach to runtime reconfiguration of component-based systems, which aims at maintaining full availability of the system and maximising service responsiveness during reconfiguration. © 2008 IEEE.

关键词： Life cycle

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Committed-programming reductions: formalizations,implications and relations

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science China(Information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Arbitrary non-contiguous pieces of computation: A new join point model for aspect-oriented programming

Arbitrary non-contiguous pieces of computation: A new join p...

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23rd ACM SIGPLAN Conference on Object Oriented Programming Systems Languages and Applications,OOPSLA 2008

作者： Sadat-Mohtasham, Hossein Software Engineering Research Lab Department of Computing Science University of Alberta Canada

Aspect-oriented mechanisms are characterized by their join point models, which define join points (meaningful points in a program's representation), pointcuts (means of specifying a set of join points), and advice (means of affecting the bahaviour of selected join points). Dynamic join point models define join points as the meaningful points in program execution. Pointcuts select a set of join points of interest, each of which is selected and advised individually and independently. That is, the relationships between join points are not taken into account in join point selection and advice, except in a limited predefined way (e.g. cflow). However, there are natural situations in which join points should only be selected if they play a specific role in a defined relationship with other join points. We propose a new join point model that takes join point interrelationships into account and allows designation of arbitrary computations as join points.

关键词： Aspect oriented programming

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Refactoring-aware version control towards refactoring support in API evolution and team development

Refactoring-aware version control towards refactoring suppor...

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28th International Conference on software engineering 2006, ICSE '06

作者： Freese, Tammo Software Engineering Group Department of Computing Science University of Oldenburg Oldenburg Germany

ISBN: (纸本)1595933751

Today, refactorings are supported in some integrated development environments (IDEs). The refactoring operations can only work correctly if all source code that needs to be changed is available to the IDE. However, this precondition neither holds for application programming interface (API) evolution, nor in team development. The research presented in this paper aims to support refactoring in API evolution and team development by extending IDE and version control to allow refactoring-aware merging and migration.

关键词： Computer software maintenance

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