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Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

Computer Systems Science & engineering 2023年第9期46卷 3339-3353页

作者： S.Priya K.Pradeep Mohan Kumar Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennai600089India Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India

An intrusion detection system(IDS)becomes an important tool for ensuring security in the *** recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network *** resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion *** proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the *** attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature ***,DRL model is employed for the proper identification and classification of intrusions that exist in the ***,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection *** ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark *** simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

关键词： Intrusion detection security reinforcement learning machine learning feature selection beetle antenna search

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Multi-lingual news stack based ensemble classification for deep news feed analysis

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Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Determination and Classification of Diabetes Mellitus Applying Ensemble Machine Learning Classifiers 1

Determination and Classification of Diabetes Mellitus Applyi...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Patel, Siddharth Hassan, Zayd Iniyan, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350361780

This paper addresses the accurate and early prediction of the onset and progression of diabetes, an important aspect in the management of this chronic metabolic disease given a variety of information like the history of the diseased person and his day-to-day routine factors by using state-of-the-art machine learning techniques to obtain predictive dynamic models. The proposed work not only demonstrates the effectiveness of different algorithms but also highlights the importance of feature engineering to refine predictions. This work uses ensemble classifiers and voting classifier algorithms to develop better accuracy for diabetes prediction. Combining the results of multiple base classifiers like KNN, SVM, RFC, and decision tree ensemble methods aims to enhance the predictive ability and chances of improvement in the patient. The treatment adopted is different from patient to patient based on the result of the treatment. In this research, we utilize a combination of ensemble techniques and a voting classifier to construct a comprehensive diabetes prediction model. This model is evaluated using real-world data. Our findings reveal the exceptional effectiveness of ensemble classifiers and the voting classifier, underscoring their potential to achieve precision and the possibility of recovery and probability. These outcomes not only enable early intervention but also offer valuable insights into the care of individual patients. © 2024 IEEE.

关键词： Decision trees

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Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices 2nd

Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozo...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system’s low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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SMSecure: Leveraging Machine Learning for Smishing Detection 1st

SMSecure: Leveraging Machine Learning for Smishing Detection

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Samad, Saleem Raja Abdul Balasubramaniyan, Sundaravadivazhagan Ganesan, Pradeepa Al-Kaabi, Amna Salim Ammaippan, Hariraman Sam, Jeyakumar Manickam Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Al-Musannah Al-Musannah Oman Electrical Engineering Department College of Engineering and Technology University of Technology and Applied Sciences-Shinas Shinas Oman

ISBN: (纸本)9789819776153

The SMS facilitates the transmission of concise text messages between mobile phone users, serving a range of functions in both personal and business domains such as appointment confirmation, authentication, alerts, notifications, and banking updates. It plays a vital role in daily communication due to its accessibility, reliability, and compatibility. Its accessibility, reliability, and compatibility make it essential for daily communication. However, SMS unintentionally generates an environment where smishing can occur. This is because SMS is extensively available and reliable. Smishing attackers take advantage of this trust to deceive victims into divulging sensitive information or performing malicious actions. Early detection saves users from being victimized. Researchers introduced different methods for accurately detecting smishing attacks. Machine Learning models and Techniques for Language Processing are a promising approach for combating the escalating menace of SMS phishing attacks. By analyzing large datasets of SMS messages, machine learning models with natural language processing methods can differentiate between legitimate and fraudulent messages. To detect smishing attacks, this paper presents a method (SMSecure) that leverages machine learning models and language processing techniques. The results show that random forest and extreme gradient boosting attain higher accuracy levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Phishing

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Automated Pneumonia Classification Using DensePneumoNet in Chest CT Scans 5

Automated Pneumonia Classification Using DensePneumoNet in C...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nithila, Ezhil E. Bibin Christopher, V. Raj, J. Relin Francis Srinivasan, A. Soundiraraj, N. Krishnan, R. Santhana Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350379945

'DensePneumoNct' is a specialized deep learning algorithm designed for accurate and efficient detection of pneumonia from CT chest images. Leveraging the DenseNet architecture within TensorFlow, this algorithm demonstrates superior performance across critical metrics including accuracy, recall, precision, and F1 score. The implementation utilizes the ImageNet dataset, preprocessed to ensure standardization and resizing, optimizing input uniformity for robust model training. The DenseNet model architecture features dense blocks and transition layers, fostering strong feature propagation and effective downsampling of feature maps. Hyperparameter tuning, including a learning rate of 0.001, a batch size of 64, and training over 100 epochs, enhances model convergence and performance refinement. Training enhancements such as early stopping and learning rate scheduling further stabilize model training and prevent overfitting. Evaluation on a validation set showcases 'DensePneumoNet''s capability to distinguish COVID-19 pneumonia, viral pneumonia, and healthy lung tissues with unparalleled accuracy. Comparative analysis against ResNet-50, Inception-v3, SqueezeNet, and LeNet confirms DensePneumoNet's superiority in all evaluated parameters, affirming its efficacy for critical medical imaging tasks. 'DensePneumoNet' represents a significant advancement in pneumonia detection, poised to enhance diagnostic accuracy and clinical decision-making in medical settings. © 2024 IEEE.

关键词： Medical imaging

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Adversarial Attacks Using Carlini Wagner

Adversarial Attacks Using Carlini Wagner

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Baddukonda, Tarun Aditya Kanakam, Ronit Kumar Yasotha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350382693

Adversarial attacks in the Artificial Intelligence have received extensive coverage in the latest years. Such attacks have affected deep learning models and neural networks due to the weaknesses that are part of the AI systems. This exposure has seen classification errors compromises and the undermining of the model performance. Understand the critical mechanisms of adversarial attacks is critical to developing strong defense mechanisms that would protect AI systems from the malevolent interventions. This abstract provides a summary of adversarial attacks as a prevalent threat to the dependability and resilience of AI technologies thus the need for continuous research and development. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Household Energy Consumption: A Web-Based Tool for Real-Time Carbon Footprint Calculation and Sustainable Benchmarking 1

Optimizing Household Energy Consumption: A Web-Based Tool fo...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Raja, Harsh Kishore, Jayaditya Kalaivani, J. Angayarkanni, V. Rama, P. Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur Chengalpattu District Tamil Nadu 603203 India

ISBN: (纸本)9798331518578

This is an innovative project from an energy-saving perspective. This project ensures that users become in control of their energy consumption and able to take an actively reducing their carbon footprints. It provides users with an interface to input details about household appliances and usage patterns, through which a correct calculation of the carbon footprint could be done. Compared against standard norms, it would help the users identify certain areas where improvement is possible and adopt more sustainable use of energy. Apart from personalized energy assessments, it has now become the central source that connects users to local schemes and initiatives on sustainable energy, including information on resources and programs into waste energy and carbon emission reduction. This integration, therefore, promotes more engagement and involvement within the community as users are nudged to join in the efforts that are being made for the goal of energy conservation. The platform educates its users about energy consumption and aids them in making green choices in data;however, with a holistic approach in carbon footprint management and education pertaining to sustainable long-term behavior, the process takes place through integrated accurate energy tracking, fine recommendations, and community-driven action. © 2025 IEEE.

关键词： Carbon sequestration

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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