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Mediterranean Electrotechnical Conference (MELECON)

作者： Antonis Michalas Tamas Kiss Network and Information Security Group Tampere University Tampere Finland Research Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London U.K.

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152011

The modernization that stems from Industry 4.0 started populating the manufacturing sector with networked devices, complex sensors, and a significant proportion of physical actuation components. However, new capabilities in networked cyber-physical systems demand more complex infrastructure and algorithms and often lead to new security flaws and operational risks that increase the attack surface area exponentially. The interconnected nature of Industry 4.0-driven operations and the pace of digital transformation mean that cyberattacks can have far more extensive effects than ever before. Based on that, the core ideas of this paper are driven by the observation that cybersecurity is one of the key enablers of Industry 4.0. Having this in mind, we propose CryptoFactory - a forward-looking design of a layered-based architecture that can be used as a starting point for building secure and privacy-preserving smart factories. CryptoFactory aims to change the security outlook in smart manufacturing by discussing a set of fundamental requirements and functionality that modern factories should support in order to be resistant to both internal and external attacks. To this end, CryptoFactory first focuses on how to build trust relationships between the hardware devices in the factory. Then, we look on how to use several cryptographic approaches to allow IoT devices to securely collect, store and share their data while we also touch upon the emerging topic of secure and privacy-preserving communication and collaboration between manufacturing environments and value chains. Finally, we look into the problem of how to perform privacy-preserving analytics by leveraging Trusted Execution Environments and the promising concept of Functional Encryption.

关键词： Cryptography Manufacturing Industries Cloud computing Hardware computer security

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Multi-party privacy-preserving decision tree training with a privileged party

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science China(Information sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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Sample subset optimization for classifying imbalanced biological data

Sample subset optimization for classifying imbalanced biolog...

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作者： Yang, Pengyi Zhang, Zili Zhou, Bing B. Zomaya, Albert Y. School of Information Technologies University of Sydney NSW 2006 Australia NICTA Australian Technology Park Eveleigh NSW 2015 Australia Centre for Distributed and High Performance Computing University of Sydney NSW 2006 Australia Faculty of Computer and Information Science Southwest University CQ 400715 China School of Information Technology Deakin University VIC 3217 Australia

ISBN: (纸本)9783642208461

Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model training for more accurate classification. In this study, we propose a sample subset optimization technique for classifying biological data with moderate and extremely high imbalanced class distributions. By using this optimization technique with an ensemble of SVMs, we build multiple roughly balanced SVM base classifiers, each trained on an optimized sample subset. The experimental results demonstrate that the ensemble of SVMs created by our sample subset optimization technique can achieve higher area under the ROC curve (AUC) value than popular sampling approaches such as random over-/under-sampling;SMOTE sampling, and those in widely used ensemble approaches such as bagging and boosting. © 2011 Springer-Verlag.

关键词： Support vector machines

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Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs

arXiv

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arXiv 2021年

作者： Zhao, Hailiang Deng, Shuiguang Yin, Jianwei Dustdar, Schahram Zomaya, Albert Y. School of Computer Science and Technology Wuhan University of Technology Wuhan China The Massachusetts Institute of Technology United States China The Distributed Systems Group The Tu Wien Austria The Centre for Distributed and High-Performance Computing The University of Sydney Australia

Multi-server jobs are imperative in modern computing clusters. A multi-server job has multiple task components and each of the task components is responsible for processing a specific size of workloads. Efficient online workload dispatching is crucial but challenging to co-located heterogeneous multi-server jobs. The dispatching policy should decide (i) where to launch each task component instance of the arrived jobs and (ii) the size of workloads that each task component processes. Existing policies are explicit and effective when facing service locality and resource contention in both offline and online settings. However, when adding the deadline-aware constraint, the theoretical superiority of these policies could not be guaranteed. To fill the theoretical gap, in this paper, we design an α-competitive online workload dispatching policy for deadline-aware multi-server jobs based on the spatio-temporal resource mesh model. We formulate the problem as a social welfare maximization program and solve it online with several well designed pseudo functions. The social welfare is formulated as the sum of the utilities of jobs and the utility of the computing cluster. The proposed policy is rigorously proved to be α-competitive for some α ≥ 2. We also validate the theoretical superiority of it with simulations and the results show that it distinctly outperforms two handcrafted baseline policies on the social welfare. © 2021, CC BY-NC-ND.

关键词： Cluster computing

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CGS-Mask: Making Time Series Predictions Intuitive for All

arXiv

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arXiv 2023年

作者： Lu, Feng Li, Wei Sun, Yifei Song, Cheng Ren, Yufei Zomaya, Albert Y. School of Computer Science and Technology Huazhong University of Science and Technology China The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Online demand peak shaving with machine-learned advice in digital twins

Digital Twins and Applications

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Digital Twins and Applications 2024年第1期1卷 38-50页

作者： Minxi Feng Wei Li Boyu Qin Albert Y.Zomaya Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneySydneyNew South WalesAustralia State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical EngineeringXi'an Jiaotong UniversityXi'anChina

As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin *** load management is central to energy systems within digital twins,which significantly impacts operational *** demand loads can lead to substantial monthly utility expenses without proper ***,a randomised online algorithm incorporating machine-learned insights is introduced to optimise battery operations and mitigate peak demand *** leverages limited-bit information from ma-chine learning models to inform its online decision-making process for cost-effective load *** provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity,has a linear computational cost,and achieves a bounded competitive *** trace-driven experiments with real-world household data reveal that AMPAMOD successfully reduces peak loads by over 90%,outperforming other benchmarks by at least 50%.These experimental findings align with our theoretical assertions,showcasing the effectiveness of AMPAMOD.

关键词： operations maintain battery

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Innovations in Simulation: Experiences With Cloud-Based Simulation Experimentation

Innovations in Simulation: Experiences With Cloud-Based Simu...

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Simulation Winter Conference

作者： Simon J. E. Taylor Anastasia Anagnostou Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Shane Kite Gary Pattison James Petry Modelling & Simulation Group Brunel University London Uxbridge UK Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London UK Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Saker Solutions Ltd Warwickshire UK

ISBN: (数字)9781728194998

ISBN: (纸本)9781728195001

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud computing can offer. Based on several years' experience of innovation in this area, this article presents our experiences in developing Cloud computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community.

关键词： Technological innovation Cloud computing Computational modeling Optimization

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Improving unsupervised domain adaptation by reducing bi-level feature redundancy

arXiv

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arXiv 2020年

作者： Wang, Mengzhu Zhang, Xiang Lan, Long Wang, Wei Tan, Huibin Luo, Zhigang Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China Institute for Quantum State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China DUT-RU International School of Information Science & Engineering Dalian University of Technology DalianLiaoning116000 China Department of Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in the field of UDA ignore this point. Moreover, main schemes realizing this in general independent of UDA purely involve a single domain, thus might not be effective for cross-domain tasks. In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way. For the first level, we try to ensure compact domain-specific features with a transferable decorrelated normalization module, which preserves specific domain information whilst easing the side effect of feature redundancy on the sequel domain-invariance. In the second level, domain-invariant feature redundancy caused by domain-shared representation is further mitigated via an alternative brand orthogonality for better generalization. These two novel aspects can be easily plugged into any BN-based backbone neural networks. Specifically, simply applying them to ResNet50 has achieved competitive performance to the state-of-the-arts on five popular benchmarks. Our code will be available at https://***/dreamkily/gUDA. © 2020, CC BY.

关键词： Deep learning

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Tree-Based Consensus Model for Proline Cis-Trans Isomerization Prediction

Tree-Based Consensus Model for Proline Cis-Trans Isomerizati...

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IEEE International Symposium on parallel and distributed Processing Workshops and PhD Forum

作者： Paul D. Yoo Albert Y. Zomaya Khalfan Alromaithi Sara Alshamsi Centre for Distributed and High Performance Computing School of Information Technologies (J12) University of Sydney Dept. Electrical Computer Engineering Khalifa University of Science Technology and Research (KUSTAR)

ISBN: (纸本)9781479913725

Proline cis-trans isomerization plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline cis-trans isomerization is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline cis-trans isomerization predictor with a biophysically-motivated consensus model through the use of evolutionary information only. The current computational predictors of proline cis-trans isomerization achieve about 70-73% accuracies through the use of evolutionary information as well as predicted protein secondary structure information. However, our methods that utilize support vector machine (SVM) and tree-based consensus model have achieved 76.72% and 81.5% accuracies, respectively, on the same proline dataset.

关键词： Proline cis-trans isomerization Machine learning Consensus modeling

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