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Leveraging ensemble learning with metaheuristic optimization algorithms for an intelligent cyberattack defense framework in an IoT environment

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Alexandria Engineering Journal 2025年 129卷 103-116页

作者： Hend Khalid Alkahtani Mashael M. Asiri Rakan Alanazi Mohammed Mujib Alshahrani Fahad Alzahrani Shaymaa Sorour Mesfer Al Duhayyim Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Information Systems and Cybersecurity College of Computing and Information Technology University of Bisha P.O. Box 551 Bisha Saudi Arabia Department of Information and Computer Science College of Computing and Mathematics King Fahad University of Petroleum and Minerals Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa 31982 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Ahsa 16273 Saudi Arabia

Cybersecurity continues to be a significant problem for some industries on the Internet, as the number of security breaches is increasing over time. It is identified that many zero-day threats are continuously developing due to the addition of several protocols, mainly from the Internet of Things (IoT). The majority of these attacks are smaller versions of formerly known cyberattacks. IoT cybersecurity aims to decrease cybersecurity risk for companies and users by securing privacy and assets. The expansion of automatic devices for cyber threat classification and detection utilizing artificial intelligence (AI) and deep learning (DL) devices has become necessary for accomplishing security in IoT environments. Due to their notable performance, DL-based methods are essential to successfully reducing security problems associated with IoT gadgets. This article presents a Leveraging Ensemble Learning and Metaheuristic Optimization Algorithms for Intelligent Cyber Attack and Defense Framework (LELMOA-ICADF) model in IoT networks. The main intention of the LELMOA-ICADF model is to deliver an efficient method using advanced ensemble models for enhancing IoT cybersecurity. Initially, the min-max normalization is employed in the data pre-processing stage for transforming input data into a structured format. The cat swarm optimization (CSO) technique is utilized for the feature selection process to choose the most relevant and significant features from the dataset. Furthermore, ensemble models such as the bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) are employed for the attack classification process. Finally, the artificial bee colony (ABC) method is used for parameter tuning to improve the classification performance of ensemble classifiers. The experimental assessment of the LELMOA-ICADF approach is performed under Edge-IIoT and ToN-IoT datasets. The performance validation of the LELMOA-ICADF approach

关键词： Cybersecurity IoT Ensemble models Attack detection Deep learning Artificial bee colony optimization Feature selection

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Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

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Computers, Materials & Continua 2022年第11期73卷 3165-3180页

作者： Krishan Arora Gyanendra Prasad Joshi Mahmoud Ragab Muhyaddin Rawa Ahmad H.Milyani Romany F.Mansour Eunmok Yang School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144001PunjabIndia School of Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia King Abdullah City for Atomic and Renewable Energy(K.A.CARE) RiyadhSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707Korea

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated ***,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation *** regular active load and inefficiency of best measures among varied associates is a huge *** sudden load deviation will give rise to immediate amendment in frequency and tie-line power *** is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the ***,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power *** the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks *** proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test *** experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s *** model can also be leveraged to examine the decision boundaries of the Bilateral case.

关键词： Bilateral contract load frequency control optimization harris hawks optimizer

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Optimal Kernel Choice for Score Function-based Causal Discovery 41

Optimal Kernel Choice for Score Function-based Causal Discov...

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Wenjie Huang, Biwei Liu, Feng You, Xinge Liu, Tongliang Zhang, Kun Gong, Mingming School of Mathematics and Statistics The University of Melbourne Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates University of California San Diego United States School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Philosophy Carnegie Mellon University United States

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods. Copyright 2024 by the author(s)

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Comparative Study of Undersampling Techniques in Text Classification

Comparative Study of Undersampling Techniques in Text Classi...

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International Conference on Informatics and computing (ICIC)

作者： Usman Ependi Ade Putra Nahdatul Akma Ahmad Albertus Dwiyoga Widiantoro Muhammad Arifin Faiz Zulkifli Informatics Department Faculty Science Technology Universitas Bina Darma Palembang Indonesia Computerized Accounting Department Faculty of Vocational Universitas Bina Darma Palembang Indonesia College of Computing Informatics and Mathematics Universiti Teknologi MARA Perak Malaysia Information Systems Department Faculty of Computer Science Soegijapranata Catholic University Semarang Indonesia Information Systems Department Faculty of Engineering Muria Kudus University Kudus Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Imbalanced datasets pose critical challenges in text classification, often leading to biased predictions and reduced model performance. This study compares three undersampling techniques: Random UnderSampler, Near Miss, and Tomek Links, to evaluate their impact on five machine learning classifiers: Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, and Neural Network. The results demonstrate that the Random Forest classifier achieves the highest performance with precision of $\mathrm{9 7 \%}$, recall of $\mathrm{9 9 \%}$, and F1-score of $\mathrm{9 8 \%}$ across all methods, showcasing its robustness against data imbalance. The Near Miss technique improves Neural Network performance to a recall of $83 \%$ and F1-score of $\mathrm{7 1 \%}$, while Tomek Links further boosts Neural Network precision to $\mathrm{6 8 \%}$, recall to $\mathrm{7 2 \%}$, and F1-score to $\mathrm{7 0 \%}$. In contrast, simpler models like Naive Bayes exhibit limited improvements, with precision and recall scores around 47-51%. These findings underscore the importance of selecting appropriate undersampling strategies to enhance text classification outcomes and address data imbalance effectively.

关键词： Support vector machines Logistic regression Text categorization Neural networks Predictive models Robustness Data models Bayes methods Informatics Random forests

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Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media

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Computers, Materials & Continua 2022年第12期73卷 5985-5999页

作者： Hanan M.Alghamdi Saadia H.A.Hamza Aisha M.Mashraqi Sayed Abdel-Khalek Department of Computer Science College of Computing Al-QunfidhahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science and HumanitiesSlayelPrince Sattam Bin AbdulAziz UniversitySaudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UnivesityNajran61441Saudi Arabia Department of Mathematics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,*** Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and ***,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of ***,there is a need exists to develop a proper technique for both identification and classification of sentiments in social *** get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning *** current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social *** presented SOADL-SAC model involves the proper identification and classification of sentiments in social *** order to attain this,SOADL-SAC model carries out data preprocessing to clean the input *** addition,Glove technique is applied to generate the feature ***,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the ***,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier *** order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark *** experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.

关键词： Sentiment analysis classification of sentiment social media seeker optimization algorithm glove embedding natural language processing

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Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms

arXiv

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arXiv 2025年

作者： Wang, Xiaoyu Kasprzak, Mikolaj J. Negrea, Jeffrey Bourguin, Solesne Huggins, Jonathan H. Department of Mathematics & Statistics Boston University United States Department of Information Systems Data Analytics & Operations ESSEC Business School France Department of Statistics & Actuarial Science University of Waterloo Canada Faculty of Computing & Data Sciences Boston University United States

Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error in, and characterized the uncertainty of, these approximations. One common approach has been to use scaling limit analyses to relate the distribution of algorithm sample paths to a continuous-time stochastic process approximation, particularly in asymptotic setups. Focusing on the univariate setting, in this paper, we build on previous work to derive non-asymptotic functional approximation error bounds between the algorithm sample paths and the Ornstein-Uhlenbeck approximation using an infinite-dimensional version of Stein's method of exchangeable pairs. We show that this bound implies weak convergence under modest additional assumptions and leads to a bound on the error of the variance of the iterate averages of the algorithm. Furthermore, we use our main result to construct error bounds in terms of two common metrics: the Lévy-Prokhorov and bounded Wasserstein distances. Our results provide a foundation for developing similar error bounds for the multivariate setting and for more sophisticated stochastic approximation *** Codes 60F17 (Primary) 60J60, 62-08, 68T05, 62E17 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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Cross-Selling Artificial Intelligence-Based Approaches in Insurance Industry: A Review

Cross-Selling Artificial Intelligence-Based Approaches in In...

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Electrical Engineering (ICEENG), 12th International Conference on

作者： Shaden Mohamed Aref Mohamed Mostafa Fouad Hend Attia Sayed Menna Ibrahim Gaber Faculty of Management Science October University for Modern Science and Arts Cairo Egypt Arab Academy for Science Technology and Maritime Transport College of Computing and Information Technology Cairo Egypt Mathematics Insurance and Applied Statistics Department Faculty of Commerce and Business Administration Helwan University Cairo Egypt Business Information Systems Department Faculty of Commerce and Business Administration Helwan University Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

For the analysis of exceedingly complex insurance data, artificial intelligence methods have evolved into the most valuable and significant tools. Worldwide, the insurance industry and its clients require a method for efficiently managing the enormous amount of data produced. The current review paper provides an overview of the research conducted in recent years on various cross-selling insurance approaches that have utilized machine learning and deep learning techniques. An evident transition from the utilization of machine learning methods to deep learning methods is demonstrated through the current literature review.

关键词： Industries Training Deep learning Self-organizing feature maps Reviews Transfer learning Neural networks

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AI-driven Risk Assessment and Decision-making in Healthcare Applications Using Machine Learning: A Case Study on Customer Twitter Feeds

AI-driven Risk Assessment and Decision-making in Healthcare ...

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Computational Intelligence and Network systems (CINS), International Conference on

作者： Abdelaziz Darwiesh Mohamed Elhoseny A.H. El-Baz Reem Atassi Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. Faculty of Computing and Info. University of Sharjah Sharjah UAE Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt Higher Colleges of Technology Dubai UAE

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

This study presents an AI-driven strategy for enhancing decision-making in healthcare applications by assessing and managing potential risks through the analysis of customer interactions on social media platforms, specifically Twitter. We propose the use of machine learning models to outperform traditional lexical approaches, addressing the complexities inherent in risk detection. Natural language processing methods are employed to efficiently process and analyze textual data, enabling healthcare institutions to make informed decisions about risk management. By leveraging customer feedback from the X platform, our approach aids in the development of effective healthcare programs by identifying and classifying risks. We create three specialized datasets for risk analysis, risk assessment, and hazard classification to optimize the training and deployment phases. In our case study, which focuses on tweets related to healthcare for US consumers, we find that over 50% of the sample identifies potential risks, with nearly 80% indicating medium levels of risk. Commonly identified risks include financial, operational, and strategic threats. The proposed methodology demonstrates an average accuracy of 82.66% in risk detection, identification, and assessment, confirming its effectiveness in supporting AI-driven decision-making in healthcare application development.

关键词： Training Accuracy Social networking (online) Computational modeling Decision making Blogs Medical services Natural language processing Complexity theory Risk management

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Deep Learning Based License Plate Number Recognition for Smart Cities

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Computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

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