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Enhanced Intrusion Detection with Advanced Deep Features and Ensemble Classifier Techniques

SN Computer Science 2025年第4期6卷 1-17页

作者： Toralkar, Pawan Mainalli, Kavita Allagi, Shridhar Debnath, Sanjoy Kumar Bagchi, Susama Leong, Wai Yie Khan, Muhammad Numan Ali Department of Artificial Intelligence Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering KLEIT: KLE Institute of Technology Karnataka Hubballi India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India INTI International University and Colleges Negeri Sembilan Nilai Malaysia College of Computing and Applied Sciences University of Technology and Applied Sciences Shinas Oman

Intrusion detection is crucial for modern cyber-security, aiming to detect and mitigate unauthorized access and malicious activities within networks. Traditional systems often rely on linear models, which may fail to capture complex patterns in advanced threats. This study proposes a heuristic approach to select the best deep features through an ensemble framework consisting of Random Forest (RF) and Support Vector Machine (SVM) classifiers using soft voting. The novelty of this work is the deep feature extraction through Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder for intrusion detection. These extracted features were fed separately for classification. In order to balance the explainability and accuracy, the ensemble classifier consisting of RF and SVM, uses feature importance ranking (RF) and unambiguous decision bounds (SVM) to provide partial interpretability. Evaluating the NSL-KDD and CICDDoS2019 datasets, our ensemble model with CNN features achieves near-perfect accuracy of 99.99% and 99.91% respectively. RNN and Autoencoder models also show high performance, each reaching 99.3% accuracy on the CICDDoS2019 dataset with Soft Voting classifiers. This work highlights the importance of the ensemble method with better feature selection in intrusion detection. The proposed system outperformed the standard methods by providing a robust, scalable solution for organizational security. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution neural network (CNN) Intrusion detection Random forest (RF) Recurrent neural network (RNN) Voting classification (Voting)

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN Computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis 2

Empowering Precision Machine: Deep Learning Technique for Lu...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Jamil, Danish Shah, Syed Mehr Ali Khan, Muhammad Numan Ali Aljarwani, Fatma Mahfoodh Mohammed University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331542573

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics. © 2024 IEEE.

关键词： Lung cancer

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Efficient Diagnoses of Breast Cancer Disease Using Deep Learning Technique 24

Efficient Diagnoses of Breast Cancer Disease Using Deep Lear...

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10th International Conference on computing and Artificial Intelligence, ICCAI 2024

作者： Raza, Muhammad Ali Khattak, Asad Masood Abbas, Wasim Asghar, Muhammad Zubair Gomal Research Institute of Computing Faculty of Computing Gomal University D.I.Khan Pakistan Department of Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi Campus 144534 United Arab Emirates CEO TECHSACARE PTE. LTD Singapore

ISBN: (纸本)9798400717055

According to WHO 2023 survey, each year more than 2.3 million breast cancer cases are reported. Breast cancer is the either first or second biggest disease in females that is the cause of death in almost 95% of countries. Diagnosing breast cancer in its early stage can be helpful to overcome this disease and result in an increase in the survival chance of the patient. Machine learning (ML) models and well-established methods for encoding categorical data have produced a wide variety of surprising outcomes when used to diagnose breast cancer using datasets that are imbalanced from testing. Early experiments also used an artificial neural network(ANN) to extract characteristics without understanding the sequencing data. In this study, we present a hybrid deep learning (DL) BiLSTM-CNN model, in order to diagnose breast cancer efficiently from patient data. The BiLSTM-CNN model was applied after dataset balancing. Contrasting to previous investigations, the experimental results of our suggested hybrid DL model were outstanding, with an accuracy of 99.3%, a precision of 99%, a recall of 99%, and an F1-score of 99%. © 2024 ACM.

关键词： Lung cancer

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Active Set Ordering 38

Active Set Ordering

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Nguyen, Quoc Phong Gupta, Sunil Venkatesh, Svetha Low, Bryan Kian Hsiang Jaillet, Patrick Applied Artificial Intelligence Institute Deakin University Australia School of Computing National University of Singapore Singapore LIDS and EECS Massachusetts Institute of Technology United States

In this paper, we formalize the active set ordering problem, which involves actively discovering a set of inputs based on their orderings determined by expensive evaluations of a blackbox function. We then propose the mean prediction (MP) algorithm and theoretically analyze it in terms of the regret of predicted pairwise orderings between inputs. Notably, as a special case of this framework, we can cast Bayesian optimization as an active set ordering problem by recognizing that maximizers can be identified solely by comparison rather than by precisely estimating the function evaluations. As a result, we are able to construct the popular Gaussian process upper confidence bound (GP-UCB) algorithm through the lens of ordering with several nuanced insights. We empirically validate the performance of our proposed solution using various synthetic functions and real-world datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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Optimized Deep Convolutional Neural Network for IoT Based Healthcare Platform 6

Optimized Deep Convolutional Neural Network for IoT Based He...

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6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Stephen, Vimal Kumar Udinookkaran, Prajith De Vera, Rudiardo Percival Ninalga Al-Harthy, Faiza Rashid Ammar College of Computing and Information Sciences University of Technology and Applied Sciences - Ibra Oman

ISBN: (纸本)9781665482714

The rapid development in the wearable sensors helped develop the healthcare-based Internet of Things (IoT) platforms for continuous monitoring of chronic disease. As per the World Health Organization (WHO) 85% of the Cardiovascular Disease (CVD) is preventable with proper monitoring for symptoms and awareness. The electrocardiogram (ECG) signal refers to a recording of electrical activity that is generated by the human heart. An analysis of the ECG signal helps in building models that can make an automatic classification of various CVDs. Deep neural network is found to be efficient to make an automatic classification of the primary signals of the ECG. These ECG signals to the arrhythmia classes were grouped by using the Convolutional Neural Networks (CNN). The 9 Layers of the CNN techniques are employed in state-of-the-art modern research to classify the signal. The Tabu Search (TS) algorithm with the CNN, Hybrid Optimization Algorithm known as the CNN with ABC-TS, and CNN with Artificial Bee Colony (ABC) is used to optimize the CNN in this work for classifying ECG signals. An evaluation of the technique was made on the basis of metrics of performance evaluation that are capable of producing enhanced outcomes. © 2022 IEEE.

关键词： Electrocardiograms

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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

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4th International Conference on computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

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Network Malaria Model on Account of Wide Communication 2

Network Malaria Model on Account of Wide Communication

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Tripathi, Padmaja Chandra, Harish Ramkeval Madan Mohan Malviya University of Technology Department of Mathematics and Scientific Computing Gorakhpur273010 India MJP Rohilkhand University Department of Applied Mathematics Bareilly India

ISBN: (纸本)9798350368109

Diseases caused by parasites proliferate quickly over the globe. As a result, their control intervention needs to be swift and comprehensive. Preventing chronic diseases can be accomplished by using multi-modal communication to attract attention and enhance awareness. This study presents a nonlinear dynamical model to evaluate the impact of media knowledge on the dissemination of illnesses carried by vectors. In this research, we propose a network model for wide-area multi-media communication. Positiveness, simple reproduction number equilibria, numerical simulation, and a fitted model using Delhi malaria data are used to analyze the mathematical model. An informed community makes it more difficult for disease to spread. Constant multi-media efforts combined with quick communication can effectively eradicate the illness. © 2024 IEEE.

关键词： Malaria network model social media stability

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A Novel Congestion Control Scheme Using Fuzzy Logic Systems to Enhance the Path Selection Criteria in Routing Protocols for Low-Power and Lossy Networks on the Internet of Things

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SN Computer Science 2024年第5期5卷 610页

作者： Aimtongkham, Phet Musikawan, Pakarat Kongsorot, Yanika So-In, Chakchai Applied Network Technology (ANT) Department of Computer Science College of Computing Khon Kaen University Khon Kaen 40002 Thailand Advanced Intelligent Interdisciplinary Integration (AIII) Department of Computer Science College of Computing Khon Kaen University Khon Kaen 40002 Thailand

In routing protocols for low-power and lossy (RPL)-based Internet of Things (IoT) networks, congestion control is essential for ensuring efficient and dependable communications with energy awareness. As the number of devices and the amount of data traffic increase, congestion typically occurs, resulting in degraded network performance, i.e., increased packet losses and delays with decreased energy efficiency. Many parameters relating to congestion behaviors have been considered and dynamically changed, as have the possible transmission paths either within a single destination-oriented directed acyclic graph (DoDAG) or across different DoDAGs. Thus, this research investigates congestion mitigation methods for RPL-IoT networks based on a fuzzy logic system (FLS) by proposing a novel FLS scheme to determine the selection criteria for conducting path management in three FLS components: (1) inside/outside the DoDAG selection process, (2) inside the path selection process within a single DoDAG (the so-called inside DoDAG) for high or to-be-high loads, and (3) inside the path selection process across the DoDAGs (the so-called outside DoDAG) during severe congestion applying the concept of a multi-DoDAG node (MDN) to determine both a suitable MDN and data traffic for alternate path selection. Simulation results demonstrate that the proposed method effectively controls congestion levels, decreases packet losses and latency, and attains improved energy efficiency in comparison with the state-of-the-art congestion control schemes in RPL-IoT networks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Congestion controls Fuzzy logic systems Internet of Things Objective function Path selection Routing protocol for low-power and lossy networks

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EXISTENCE AND STABILITY OF SOLUTIONS FOR NONLINEAR FRACTIONAL DIFFERENTIAL EQUATIONS INVOLVING THE GRÖNWALL-FREDHOLM-TYPE INEQUALITY

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Journal of Mathematical Sciences (United States) 2025年第2期289卷 323-342页

作者： Hatime, Naoufel El Mfadel, Ali Elomari, M.’hamed Melliani, Said Laboratory of Applied Mathematics and Scientific Computing Sultan Moulay Slimane University PO Box 532 Beni Mellal 23000 Morocco Superior School of Technology Sultan Moulay Slimane University Khenifra Morocco

In this work, we have explored a fractional Newton’s Second Law of motion involving the ψ-Caputo operator of order α∈(1,2]. We proved the existence and uniqueness of solutions for different classes of force functions f acting on a specific object in mention. We have used some well-known fixed point theorems and fractional calculus methods. We have proved Ulam-Hyers stability, generalized Ulam-Hyers stability, Ulam-Hyers-Rassias stability, and generalized Ulam-Hyers-Rassias stability for the main problem. Mainly, this manuscript can be divided into four major parts. In the first part, we derive the necessary conditions to assure the existence and the uniqueness of a solution. In the second part, we develop some new Grönwall-Fredholm inequalities type, and in the third part, we prove that our problem is stable with respect to all Ulam’s stability types, and we have found a simpler condition under which the UHR and HUHR stability types persist, making our findings more accessible and applicable in a large class of fractional problems. Finally, we give two examples to establish our theoretical results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Fixed point theorem Fractional differential equations Grönwall type inequality Ulam-Hyers stability ψ-Caputo

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