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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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ModalFP-Growth: Efficient Extraction of Modal Association Rules from Non-Tabular Data 25

ModalFP-Growth: Efficient Extraction of Modal Association Ru...

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25th Italian Conference on Theoretical computer science, ICTCS 2024

作者： Milella, Mauro Pagliarini, Giovanni Sciavicco, Guido Stan, Ionel Eduard Department of Mathematics and Computer Science University of Ferrara Italy Faculty of Engineering Free University of Bozen-Bolzano Italy

This paper explores the extraction of modal association rules from non-tabular data using a novel algorithm, ModalFP-Growth. By extending the FP-Growth algorithm to modal logic, ModalFP-Growth processes instances represented as Kripke models, facilitating efficient rule extraction from temporal, spatial, and spatio-temporal datasets. Each instance is transformed into a tabular form where worlds correspond to rows and literals to columns, enabling the application of the original FP-Growth. The algorithm, then, aggregates locally frequent itemsets from individual instances to identify globally supported itemsets across the dataset. We prove the soundness and completeness of ModalFP-Growth, ensuring that all and only frequent itemsets are included in the final output. Additionally, we present an open-source implementation within the Sole learning and reasoning suite. Experimental evaluations using Halpern and Shoham’s Interval Temporal Logic on a public temporal dataset demonstrate the algorithm’s practical efficiency and the interpretability of the extracted rules. © 2024 Copyright for this paper by its authors.

关键词： Spatio-temporal data

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Atrial Fibrillation Detection using the Stars 2D Convolutional Neural Network 47

Atrial Fibrillation Detection using the Stars 2D Convolution...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Petrovski, N. Gusev, M. Kulakov, Andrea Sts Cyril And Methodius University Faculty Of Computer Science And Engineering Skopje Macedonia Innovation Dooel Skopje Macedonia

ISBN: (纸本)9798350382495

Atrial Fibrillation is one of the riskiest potentials of heart failure in the recent escalating prevalence of cardiovascular diseases. Detecting such an irregularly irregular heart rhythm is a paramount challenge in modern biomedical computation. This research contributes to its resolution by introducing an advanced 2D Convolutional Neural Network model trained on the Poincare plot of the differences between consecutive instantaneous heart rates instead of beat-to-beat time intervals. Furthermore, we used the Gaussian Blur technique to enhance the model's capacity to generalize and augment its accuracy by forming a star-like plot inspired by Van Gogh's famous Starry Nights canvas. Model development uses different benchmark datasets to train and test the model. Applying the standard Machine Learning window label comparison method, our model achieves an impressive F1 Score of 96.34% and a remarkable F1 Score of 94.85% when evaluated by the duration-based assessment method. © 2024 IEEE.

关键词： Electrocardiograms

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Architecture of a Distributed Infrastructureless System 47

Architecture of a Distributed Infrastructureless System

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47th ICT and Electronics Convention, MIPRO 2024

作者： Mileski, D. Gusev, M. Sts Cyril And Methodius University Faculty Of Computer Science And Engineering Skopje Macedonia Innovation Dooel Skopje Macedonia

ISBN: (纸本)9798350382495

Recent trends in the production of portable devices, such as smartphones, smartwatches, and all other smart devices, show that their computing resources are comparable to laptops and desktop computers produced several years ago. In addition, many installed IoT devices around us realize that pervasive and ubiquitous computing integrates a remarkable amount of computing power. Unfortunately, these computing resources efficiently comply with the ever-increasing demand for computing power. Realizing a system that integrates devices on more minor architectural levels (IoT and edge layers) is a complex and challenging task, especially if the system goes beyond edge computing toward autonomous processing and realizes the essence of dew computing. The system should be platform-agnostic and provider-agnostic so all surrounding devices may participate and build more powerful computing resources. Finally, the proposed system realizes Infrastructureless computing, integrating Serverless, Deviceless, and Thingless computing as a service to nearby consumers. This paper analyzes the architecture of such a distributed computing system. © 2024 IEEE.

关键词： Computing power

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A Review of Blockchain in Internet of Medical Things

A Review of Blockchain in Internet of Medical Things

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Mansouri, Houssem Hireche, Rachida Benrebbouh, Chahrazed Pathan, Al-Sakib Khan LRSD Laboratory Department of Computer Science Faculty of Sciences Setif 1 University - Ferhat Abbas Sétif Algeria Department of Computer Science and Engineering United International University Dhaka Bangladesh

ISBN: (纸本)9789819706402

During the past few years, especially after the emergence of the Covid-19 pandemic, researchers have devoted their efforts in improving the global health sector by supporting it with the latest technologies. Among these technologies, we often hear about Internet of Medical Things (IoMT) and blockchain in the effort of facilitating the patients and medical staff to preserve their security and confidentiality of the patient data and protect it from every hacking attempt to steal or falsify. In fact, during just the last three years alone, dozens of new schemes have been proposed in the literature for the integration of blockchain with IoMT for the healthcare field. Therefore, we present in this paper a review of some the notable works in this area. Our intent is to explain the basic principles of this field and classify the notable proposed schemes, as this study suggests potentially interesting avenues for future research to use it as a reference material by the researchers in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Health care

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Misuse of Wi-Fi Data to Analyze User Behaviour 15

Misuse of Wi-Fi Data to Analyze User Behaviour

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15th International Conference Elektro, ELEKTRO 2024

作者： Kapicak, Lukas Stipal, Jiri Trubak, Kamil Machnik, Petr Burdiak, Patrik Rozhon, Jan VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Telecommunications Ostrava Czech Republic

ISBN: (纸本)9798350372359

There has been a huge increase in the number of mobile devices over the past few years. Many mobile users own more than one mobile device. Compared to conventional laptops, mobile devices are constantly active and their users use them outdoors more often than personal computers. Compared to desktop computers, mobile devices accompany their owners on their travels and therefore there is a significant risk of tracking and eavesdropping on user data without a user being aware of it. This paper describes techniques how the attacker could track users by using the Wireless Fidelity (Wi-Fi) network. Every mobile device generates traffic that can be divided into 'Automatic' and 'User'. When' Automatic' traffic is generated, the device can be found because of its pattern. The paper also describes how the attacker can get into the victim's Wi-Fi network without being in its range. When breaching the Wi-Fi network security, the attacker can connect to the victim's network during his first appearance in range of this network. Security pre-cautions and possible directions of future work are discussed at the end of this paper. © 2024 IEEE.

关键词： Personal computers

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Machine Learning Techniques for Detecting Fraud in Credit Card Transactions 31

Machine Learning Techniques for Detecting Fraud in Credit Ca...

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31st Symposium of Advanced Database Systems, SEBD 2023

作者： Mahmood, Tariq Hashemi, Seyedeh Khadijeh Mirtaheri, Seyedeh Leili Greco, Sergio Department of Computer Science Modeling Electronics and Systems Engineering University of Calabria Italy Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran

Credit cards became one of the most popular payment methods as technology advanced and e-commerce services expanded, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in the number of frauds necessitates high banking transaction costs. As a result, detecting fraudulent activities has become an intriguing topic, attracting a large number of researchers. We consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions in this study. We use Bayesian optimization, in particular, to optimize the hyperparameters while taking into account practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as using CatBoost and XGBoost to improve the performance of the LightGBM method by taking the voting mechanism into account. Finally, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one, in order to improve performance even further. We conducted some experiments to evaluate the proposed methods on real-world data. We use recall-precision metrics in addition to the common ROC-AUC to better cover unbalanced datasets. We use a 5-fold cross-validation method to test CatBoost, LightGBM, and XGBoost separately. Furthermore, the performance of the combined algorithms is evaluated using the majority voting ensemble learning method. According to the results, LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79. Also, by using deep learning and the Bayesian optimization method to tune the hyperparameters, we meet the following criteria: ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a big improvement over the state-of-the-art methods we compared it to. © 2023 CEUR-WS. All rights reserved.

关键词： Learning systems

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A novel approach to deriving adaboost classifier weights using squared loss function for overlapping speech detection

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Multimedia Tools and Applications 2025年 1-28页

作者： Asbai, Nassim Bounazou, Hadjer Zitouni, Sihem Department of Electronics Faculty of Sciences and Technology Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria Speech Com. & Signal Proc. Lab. Faculty of Electrical Engineering USTHB Bab Ezzouar City16111 Algeria Department of Computer Science Faculty of Mathematics and Computer Science Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria

In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recognition systems. Traditional approaches, often relying on exponential loss functions within the AdaBoost framework, struggle to maintain robustness in noisy or imbalanced data environments. To enhance detection accuracy, we propose a novel AdaBoost variant—SLFAdaBoost—utilizing a squared loss function specifically tailored for overlapping speech detection. This model demonstrates superior stability and convergence, significantly outperforming conventional methods. Experimental evaluation on the NIST 2005 corpus revealed that SLFAdaBoost achieves an accuracy range of 92.5% to 98.6%, with F1 scores between 95.4% and 99.2%, notably surpassing standard AdaBoost and Random Forest classifiers. Additionally, our model shows resilience in noisy conditions, maintaining precision and recall metrics at higher noise levels than other classifiers. These results underscore SLFAdaBoost's capacity to handle intricate data patterns, offering a more robust solution for real-world speech processing applications where overlapping segments are prevalent. This contribution provides an efficient, high-performing model, advancing the capabilities of ensemble methods in complex audio environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech recognition

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AI-Infused Predictive Digital Twin Systems for Resilient IoT Software Architectures 4

AI-Infused Predictive Digital Twin Systems for Resilient IoT...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Gupta, Raj Kumar Dhasmana, Gulshan Benvin, Shalet Bhargav, H.K. Jadhav, Girish Rao, Tarini Department of Physics Bhabua India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Bgs Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The proliferation of Internet of Things (IoT) networks has significantly increased the complexity of software architectures, leading to heightened vulnerabilities and system inefficiencies. AI-infused Predictive Digital Twin Systems provide a promising approach by integrating real-time data analytics with predictive AI models to monitor, forecast, and enhance the health and security of IoT software architectures. This paper introduces an AI-Infused Predictive Digital Twin System designed to create resilient IoT software architectures. Experimental validation was conducted using a large-scale IoT network with 1,000 interconnected devices. The AI-Infused Predictive Digital Twin System achieved a 97.8% accuracy in anomaly detection, with a 28% reduction in system disruptions compared to conventional monitoring solutions. The RL-based optimization module resulted in a 20% improvement in resource utilization efficiency and a 30% increase in overall system resilience under varying load conditions. The integration of the blockchain audit layer significantly improved traceability and reduced security breach recovery time by 40%. The AI-Infused Predictive Digital Twin System provides an innovative solution for developing resilient IoT software architectures. By integrating predictive AI models and real-time digital twin technology, the framework enables continuous monitoring, proactive anomaly detection, and dynamic optimization. The experimental results demonstrate the system's capability to enhance resilience, efficiency, and security in complex IoT environments, making it a valuable tool for managing and securing next-generation IoT architectures. Future work will explore expanding the system's capabilities to support multi-cloud IoT networks and developing more sophisticated AI models for enhanced predictive accuracy and operational efficiency. © 2024 IEEE.

关键词： Blockchain

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