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Robust Resource Sharing in Network Slicing via Hypothesis Testing

IEEE Transactions on Network and Service Management 2025年第3期22卷 2731-2746页

作者： Nikolaidis, Panagiotis Baras, John Institute for Systems Research University of Maryland at College Park Department of Electrical and Computer Engineering College ParkMD20742 United States

In network slicing, the network operator needs to satisfy the service level agreements of multiple slices at the same time and on the same physical infrastructure. To do so with reduced provisioned resources, the operator may consider resource sharing mechanisms. However, each slice then becomes susceptible to traffic surges in other slices which degrades performance isolation. To maintain both high efficiency and high isolation, we propose the introduction of hypothesis testing in resource sharing. Our approach comprises two phases. In the trial phase, the operator obtains a stochastic model for each slice that describes its normal behavior, provisions resources and then signs the service level agreements. In the regular phase, whenever there is resource contention, hypothesis testing is conducted to check which slices follow their normal behavior. Slices that fail the test are excluded from resource sharing to protect the well-behaved ones. We test our approach on a mobile traffic dataset. Results show that our approach fortifies the service level agreements against unexpected traffic patterns and achieves high efficiency via resource sharing. Overall, our approach provides an appealing tradeoff between efficiency and isolation. © 2004-2012 IEEE.

关键词： Resource management Quality of service Testing Service level agreements Network slicing Bandwidth Traffic control Costs Delays Anomaly detection

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Blockchain-Based Medical Decision Support System

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Journal of Cyber Security and Mobility 2023年第3期12卷 253-274页

作者： Hovorushchenko, Tetiana Hnatchuk, Yelyzaveta Osyadlyi, Vitaliy Kapustian, Mariia Boyarchuk, Artem Department of Computer Engineering & Information Systems Khmelnytskyi National University Khmelnytskyi Ukraine Tallinn Estonia

An urgent task at this moment is the use of blockchain technology to ensure the security of medical decision support systems (MDSS). Our research is devoted to development of blockchain-based MDSS (regarding possibility or impossibility of organ and tissue donation/transplantation, regarding possibility or impossibility of using reproductive technologies in the treatment of infertility). The developed blockchain-based medical decision support system provides reliable protection and security of medical information through the use of blockchain technology, provides support of decision regarding possibility or impossibility of organ and tissue donation/transplantation, provides support of decision regarding possibility or impossibility of use of reproductive technologies in the infertility treatment. The proposed blockchain-based medical decision support system: automates medical decision-making processes, minimizes the human factor and its influence on the medical decision process, and takes into account the norms of current legislation when making medical decisions, thereby allowing not to pay for the services of a hired lawyer, and also works with verified and protected medical data entered in the blockchain, which allows you to get rid of leaks of medical information and to ensure reliable protection of medical data. © 2023 River Publishers.

关键词： Blockchain

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Distilled Gradual Pruning With Pruned Fine-Tuning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4269-4279页

作者： Fontana, Federico Lanzino, Romeo Raoul Marini, Marco Avola, Danilo Cinque, Luigi Scarcello, Francesco Foresti, Gian Luca The Department of Computer Science Sapienza University of Rome Rome00198 Italy The Department of Computer Engineering Modeling Electronics and Systems University of Calabria Rende87030 Italy The Department of Mathematics Computer Science and Physics University of Udine Udine33100 Italy

—Neural networks (NNs) have been driving machine learning progress in recent years, but their larger models present challenges in resource-limited environments. Weight pruning reduces the computational demand, often with performance degradation and long training procedures. This work introduces distilled gradual pruning with pruned fine-tuning (DG2PF), a comprehensive algorithm that iteratively prunes pretrained NNs using knowledge distillation. We employ a magnitude-based unstructured pruning function that selectively removes a specified proportion of unimportant weights from the network. This function also leads to an efficient compression of the model size while minimizing classification accuracy loss. Additionally, we introduce a simulated pruning strategy with the same effects of weight recovery but while maintaining stable convergence. Furthermore, we propose a multistep self-knowledge distillation strategy to effectively transfer the knowledge of the full, unpruned network to the pruned counterpart. We validate the performance of our algorithm through extensive experimentation on diverse benchmark datasets, including CIFAR-10 and ImageNet, as well as a set of model architectures. The results highlight how our algorithm prunes and optimizes pretrained NNs without substantially degrading their classification accuracy while delivering significantly faster and more compact models. Impact Statement—In recent times, NNs have demonstrated remarkable outcomes in various tasks. Some of the most advanced possess billions of trainable parameters, making their training and inference both energy intensive and costly. As a result, the focus on pruning is growing in response to the escalating demand for NNs. However, most current pruning techniques involve training a model from scratch or with a lengthy training process leading to a significant increase in carbon footprint, and some experience a notable drop in performance. In this article, we introduce DG2PF. This unstruct

关键词： computer architecture

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Deep Learning Based Phonocardiogram Signals Analysis for Cardiovascular Abnormalities Detection

Deep Learning Based Phonocardiogram Signals Analysis for Car...

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2023 International Conference on Robotics and Automation in Industry, ICRAI 2023

作者： Hussain, Sayed Shahid Ashfaq, Muniba Khan, Muhammad Salman Anwar, Shahzad Department of Computer Systems Engineering Peshawar Pakistan Department of Mechatronics Systems Engineering Peshawar Pakistan

ISBN: (纸本)9781665464727

Cardiovascular diseases are among the vital causes of mortality worldwide which need early detection with the use of auscultation examination. Heart diseases could be diagnosed in a convenient way of heartbeat sound analysis. Manual auscultation is time-consuming and problematic to differentiate heart sounds related to different kinds of heart abnormalities. Also, as it requires an expert in the field, it becomes costly and quite prone to human error. Due to all these issues, there is a high demand for an automatic diagnostic system, an alternative way to human examination. This research focuses on developing Artificial Intelligence (AI) based system of the latest computational algorithms for detecting heart abnormalities from heart sounds. Heart sounds are classified as to be normal or abnormal from Phonocardiogram (PCG) signals. One of the recent techniques introduced in deep learning algorithms for audio classification is 1-Dimensional Convolutional Neural Network (1D-CNN). This research work includes a 1D-CNN as a classification algorithm. A widely used publicly available dataset of heart sounds from PhysioNet/CinC (2016) challenge is utilized. The method acquires accuracy, sensitivity, specificity, F1 score, and precision of 95.45%, 97.44%, 93.6%, 95.45%, and 95.54% respectively. The proposed approach uses a less-complicated customized 1D-CNN algorithm, outshining most of the previous competitive methods by securing high performance that makes it appropriate for diagnosing heart diseases from PCG data. © 2023 IEEE.

关键词： Heart

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An Ant-colony Based Model for Load Balancing in Fog Environments

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2023年第1期10卷 4-20页

作者： Mirtaheri, Seyedeh Leili Azari, Mahya Greco, Sergio Arianian, Ehsan Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer Engineering Modeling Electronics and Systems University of Calabria Italy Tehran Iran

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available *** provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field. © The Authors 2023. This paper is published with open access at ***

关键词： Fog

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Cyber Secure Framework for Smart Containers Based on Novel Hybrid DTLS Protocol

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computer systems Science & engineering 2022年第12期43卷 1297-1313页

作者： Waseem Ullah Khan Safdar Nawaz Khan Marwat Salman Ahmed Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25120Pakistan

The Internet of Things (IoTs) is apace growing, billions of IoT devicesare connected to the Internet which communicate and exchange data among eachother. Applications of IoT can be found in many fields of engineering and sciencessuch as healthcare, traffic, agriculture, oil and gas industries, and logistics. Inlogistics, the products which are to be transported may be sensitive and perishable, and require controlled environment. Most of the commercially availablelogistic containers are not integrated with IoT devices to provide controlled environment parameters inside the container and to transmit data to a remote *** necessitates the need for designing and fabricating IoT based smart containers. Due to constrained nature of IoT devices, these are prone to different cybersecurity attacks such as Denial of Service (DoS), Man in Middle (MITM) andReplay. Therefore, designing efficient cyber security framework are required forsmart container. The Datagram Transport Layer Security (DTLS) Protocol hasemerged as the de facto standard for securing communication in IoT ***, it is unable to minimize cyber security attacks such as Denial of Serviceand Distributed Denial of Service (DDoS) during the handshake process. Themain contribution of this paper is to design a cyber secure framework by implementing novel hybrid DTLS protocol in smart container which can efficientlyminimize the effects of cyber attacks during handshake process. The performanceof our proposed framework is evaluated in terms of energy efficiency, handshaketime, throughput and packet delivery ratio. Moreover, the proposed framework istested in IoT based smart containers. The proposed framework decreases handshake time more than 9% and saves 11% of energy efficiency for transmissionin compare of the standard DTLS, while increases packet delivery ratio andthroughput by 83% and 87% respectively.

关键词： Logistic container logistics security cyber security perishable products monitoring

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A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale systems

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Journal of Modern Power systems and Clean Energy 2023年第1期11卷 254-266页

作者： Yuzhou Zhou Qiaozhu Zhai Lei Wu Moammad Shahidehpour Systems Engineering Institute MOEKLINNS LabXi’an Jiaotong UniversityXi’an 710049China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenU.S. Electrical and Computer Engineering Department Illinois Institute of TechnologyChicagoU.S. IEEE

This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(MILP)are popular approaches to solving ***,with many binary unit commitment variables,LR suffers from slow convergence and MILP presents heavy computation *** proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems.A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible(ASF)approaches,which is then leveraged to efficiently solve new TCUC instances ***/off statuses of considerable units can be fixed in the online calculation according to the database,which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances.A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables,which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure *** tests illustrate the efficiency of the proposed approach.

关键词： Unit commitment accelerated algorithm data driven variable reduction

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Jamming Attack Mitigation in Wireless Federated Learning Networks Using Bayesian Games

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 247-251页

作者： Barkatsa, Sofia Diamanti, Maria Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM87131 United States

Federated Learning (FL), an emerging distributed Artificial Intelligence (AI) technique, is susceptible to jamming attacks during the wireless transmission of trained models. In this letter, we introduce a jamming attack mitigation mechanism for the uplink of wireless FL networks using the power-domain Non-Orthogonal Multiple Access (NOMA) technique. The problem of transmission power allocation for all clients (legitimate and malicious) is formulated and solved distributively as a Bayesian game with incomplete information. The clients aim to successfully transmit their model parameters, minimizing transmission time and consumed power, while having probabilistic knowledge about the malicious behavior of the other clients in the game. © 2019 IEEE.

关键词： Federated learning

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