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Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

Intelligent Automation & Soft computing 2023年第1期35卷 297-320页

作者： Rasha Almarshdi Laila Nassef Etimad Fadel Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHailSaudi Arabia

Internet of Things(IoT)is the most widespread and fastest growing technology *** to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security *** IoT devices are not designed with security because they are resource constrained ***,having an accurate IoT security system to detect security attacks is *** Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks *** paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning *** implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks *** this work,interpolation data preprocessing is used to compute the missing ***,the imbalanced data problem is solved using a synthetic data generation *** experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced ***,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced *** results proved the impact of the balancing *** proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)***,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

关键词： IoT IDS deep learning machine learning CNN LSTM

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft computing 2025年第7期29卷 3241-3251页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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Optimizing Neural Network Training: A Markov Chain Approach for Resource Conservation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年 1-14页

作者： Wang, Ke Huang, Xianting Tan, Cong Yiu, Siu-Ming Chen, Zicong Lei, Xiaolin College of Information Science and Technology Jinan University Guangzhou China Department of Computer Science the University of Hong Kong Hong Kong China

The additional resource consumption generated during the repeated training processes of neural networks, including time and computational power costs, is a problem of significant concern. We, therefore, propose a method that utilizes Markov Chain to predict the training outcomes of neural networks with the same structure. This method’s training is based on prior experience to optimize the parameter adjustment process, thereby reducing the number of times training must be started from scratch and lowering time costs. By predicting training outcomes and reducing forward and backward propagation computations, among other factors, computational resource consumption significantly decreases. Simultaneously, since Markov Chain represents a clear mathematical model, the properties of probability transition offer greater interpretability compared to traditional methods. In an era where explainable artificial intelligence is equally crucial, a more transparent training method could have greater application potential in many important scenarios. The dual benefits they provide exemplify the advantage of our approach. Regarding the critical part, we have theoretically and experimentally demonstrated that, under certain conditions, the neural network training process possesses Markov property and becomes a Markov process after clustering. IEEE

关键词： Forecasting

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J. F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Key Laboratory of Computing Power Network and Information Security Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Ministry of Education Shandong Computer Science Center Jinan250353 China

Intelligent supply line surveillance is critical for modern smart grids (SGs). Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the SG. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage (ESL), and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using elliptic curve cryptography (ECC) and physical unclonable functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the random or real (ROR) model. Furthermore, informal security analysis demonstrates the protocol’s robustness in rigorously resisting various attacks, including physical tampering, impersonation, ESL and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol’s superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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An Opposition-Based Learning Archerfish Hunting Optimizer for Global Optimization 6

An Opposition-Based Learning Archerfish Hunting Optimizer fo...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Ferhat, Aridj Zitouni, Farouq Limanc, Abdelhadi Lakbichi, Rihab Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

This paper introduces OBL-AHO, an enhanced Archerfish Hunting Optimizer (AHO) variant that integrates Opposition-Based Learning (OBL) to improve AHO's exploratory capabilities. By leveraging opposite solutions alongside newly generated ones, OBL-AHO broadens the search space, effectively reducing the risk of local optima entrapment-A common challenge in metaheuristic algorithms. The performance of OBL-AHO is rigorously compared with the original AHO and several prominent Metaheuristic Algorithms (MAs), including TLBO (Teaching-Learning-Based Optimization), PSO (Particle Swarm Optimization), GWO (Grey Wolf Optimizer), OOBO (OnE-To-One Based Optimizer), SSA (Salp Swarm Algorithm). Evaluations were conducted using test functions taken from CEC 2022, and the outcomes were statistically analyzed using the Friedman test. The findings demonstrate that OBL-AHO significantly improves exploration while maintaining competitive exploitation relative to its opponents. © 2024 IEEE.

关键词： Contrastive Learning

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Comparative Analysis of Four Metaheuristic Algorithms for Estimating Parameters in Solar Photovoltaic Models 6

Comparative Analysis of Four Metaheuristic Algorithms for Es...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Lakbichi, Rihab Zitouni, Farouq Limane, Abdelhadi Ferhat, Aridj Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

In the last decade, Metaheuristic Algorithms (MAs) have been widely employed to estimate parameters in photovoltaic (PV) models to maximize solar energy efficiency. This paper presents a comparative study of the performance of four novel and efficient MAs applied to solve the parameter extraction problem in three ubiquitous PV models: The Single-Diode Model (SDM), the Double-Diode Model (DDM), and the Photovoltaic Module Model (PMM). The primary objective of this study is to evaluate and compare the efficiency of these MAs and identify the most effective one based on the Root Mean Square Error (RMSE) between the simulated and the actual data. This work compares the performance of the Red-Tailed Hawk Optimizer (RTH), the One-To-One Based Optimizer (OOBO), the Sand Cat Swarm Optimization (SCSO), and the Archerfish Hunting Optimizer (AHO). The numerical results demonstrate that RTH significantly outperforms its counterparts in estimating parameters for the examined models. This exceptional performance underscores RTH's efficiency and effectiveness as a powerful tool for parameter estimation in PV models. Given these promising results, RTH stands out as a preferred method and holds the potential for further refinement and enhancement in the future. © 2024 IEEE.

关键词： Mean square error

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Invasive ductal carcinoma (IDC) detection in breast histopathology images using enhanced transfer learning of convolutional neural networks

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International Journal of information technology (Singapore) 2025年 1-13页

作者： Mahbub, Hafsa Binte Olowolayemo, Akeem Omari, Swaleh Maulid Adeleke, Imran Ademola Department of Computer Science KICT International Islamic University Malaysia Kuala Lumpur Malaysia School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Computer Science Education College of Information and Technology Education Lagos State University of Education Lagos Nigeria

This study introduces a deep learning methodology for the automated identification of tissue regions indicative of Invasive Ductal Carcinoma (IDC) within Whole Slide Images (WSI) associated with breast cancer. Deep learning demonstrates notable efficacy in such applications, particularly when an ample number of samples are available for training purposes. The proposed framework extends across various convolutional neural networks (CNNs). The method underwent evaluation using a WSI dataset encompassing specimens from 162 patients diagnosed with IDC. The experimental outcomes indicate commendable accuracies of 89%, 88%, 84%, and 82%, for CNN, EfficientNet, EfficientNetV2, and VGG16, respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： computer aided diagnosis Convolutional neural networks (CNNs) Deep learning(DL) Efficientnet Efficientnetv2 Invasive ductal carcinoma (IDC) Transfer learning VGG16

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Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10035-10044页

作者： Nagamani, T. Logeswari, S. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. © 2024 – IOS Press.

关键词： Diseases

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A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

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Intelligent Automation & Soft computing 2023年第8期37卷 2355-2370页

作者： Ali Ahmed Alaa Omran Almagrabi Omar MBarukab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz University–RabighRabigh21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz University-RabighRabigh21589Saudi Arabia

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image *** are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient *** methods are applied to enhance the retrieval performance of CBMIR *** new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these *** study proposes the relative difference-based similarity measure(RDBSM)for *** new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture ***,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)*** proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval *** effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

关键词： Medical image retrieval feature extraction similarity measure fusion method

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG computing

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Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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