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Cyber Attack Detection Using Deep Multi-agent Reinforcement Learning with Beth Dataset

SN computer science 2025年第5期6卷 1-16页

作者： Manikandan, A. Rajan, S. Deepa Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies VISTAS Tamil Nadu Chennai India

The Internet of Things (IoT) transforms connectivity, enabling devices to share data seamlessly via the cloud or wireless networks. However, IoT systems remain vulnerable to sophisticated cyberattacks, and traditional detection methods often fail due to inadequate fine-tuning and ineffective feature extraction. This study addresses these challenges by proposing a novel cyberattack detection framework based on deep multi-agent reinforcement learning (MARL). The framework employs the deep deterministic policy gradient (DDPG) algorithm to enhance the adaptive capabilities of multiple agents within an IoT network environment. A newly curated dataset, BETH, is introduced, encompassing diverse cyberattack scenarios specifically designed for training and evaluating MARL-based systems. Through deep reinforcement learning, the framework autonomously learns to detect and mitigate known and emerging threats. To assess the robustness and generalizability of the proposed model, a fivefold cross-validation process was conducted. Experimental results reveal that the proposed approach achieves an exceptional detection accuracy of 99.95%, demonstrating its ability to adapt to dynamic attack patterns while minimizing error rates. The findings highlight the framework’s potential to improve network security significantly, advancing the field of intelligent, autonomous cyber defence for IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection agents (ADA) BETH dataset Collaboration and communication buffer (CCB) Cyber-attack detection Deep deterministic policy gradient algorithm Internet of Things (IoT) Intrusion detection agents (IDA) Multi-agent reinforcement learning (MARL)

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Score-Level Fusion Approach for Traffic Flow Prediction in Intelligent Transportation System 6

Score-Level Fusion Approach for Traffic Flow Prediction in I...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Axcellin, T. Kamalakkannan, S. Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Traffic flow prediction (TFP) for intelligent transportation system is essential for effective transportation planning and city development. Conventional models often have difficulty with the complex nature of traffic data, which includes nonlinear patterns, spatial dependencies, and external influences such as weather or road conditions. Prediction accuracy may be significantly increased by utilizing an ensembling strategy, which combines the capabilities of many models. The goal of the study is to improve TFP accuracy by integrating the results of different machine learning (ML) models. The study uses Random Forest(RF) Naïve Bayes(NB), K-Nearest Neighbor(KNN), Extreme Gradient Boosting(XGBoost) and Support vector machine(SVM) as prediction models and based on the prediction probabilities and the score level fusion is performed with RF and XGBoost ensembling. The new fusion procedure uses a weighted average technique in which weights are assigned dynamically depending on individual model performance criteria including accuracy, precision, recall, and F1-score. Higher-performing algorithms are given greater significance in the final forecast, resulting in a dynamic and data-driven assembly strategy. The findings show that the efficiency of the ensemble of RF-XGBoost is higher compared to the contrasted methods. The model also uses feature selection approaches like mutual information to find the most significant traffic-related characteristics, lowering computational cost and increasing forecast efficiency. © 2025 IEEE.

关键词： Support vector machines

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Machine Learning based Prediction of Parkinson's Diseases 4

Machine Learning based Prediction of Parkinson's Diseases

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Arockiya Selvi, S. Kamalakannan, T. Vel's Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331523923

Parkinson's disease(PDs) is a neurological disorder characterized by accidental or uncontrollable movements such as shaking, rigidity, and trouble with balance and coordination. Symptoms are usually minor at first and intensify over time. PD datasets tend to be compact and inadequately diversified. Several datasets contain incorrect values or discrepancies as a result of diagnostic procedure inconsistencies. Datasets frequently have fewer PD patients than healthy individuals, resulting in biased models. SVM is a common method for PD prediction due to its ability to handle tiny, high-dimensional data sets and its robust classification skills. SVM performs effectively even with small medical datasets, unlike deep learning models, which require enormous volumes of data. SVM minimizes overfitting, particularly when working with tiny datasets, making it more dependable for use in real-world medical settings. The study employed SVM algorithms to predict Parkinson's disease. Additionally, disease data is classified and compared using SVM with different kernel parameters. The study compares the performance of four alternative SVM models Linear SVM (LSVM), Quadratic SVM (QSVM), Cubic Gaussian SVM (CGSVM), and Medium Gaussian SVM (MGSVM) using five assessment metrics: accuracy, sensitivity, specificity, precision, and F1-Score. MGSVM is the most effective model for predicting PDs, with the highest precision and consistency. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Deep Learning Model with Optimization Strategies for DDoS Attack Detection in Cloud Computing 3

Deep Learning Model with Optimization Strategies for DDoS At...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Radhika, P. Kamalakkannan, S. Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Cloud Computing (CC) remains as a chief research area for analysts because of its numerous applications and advantages. The dispersed nature of CC and its dependence on internet service poses several attacks and challenges. Mainly, the attacks like DDoS attempts to disrupt the online operations. Insider attacks cannot be detected using conventional detection techniques like firewalls. This study suggests a DDoS detection method for reducing the DDoS activity. The proposed work contributes under big data perspective by handling certain procedure for detection process. Data Generation phase is the initial step, and according to the work, DDoS dataset is considered under big data perspective. As the work is considered under the big data perspective, Map reduce (MR) framework is used, Mapper handles the data and process the feature extraction, which includes the extraction of raw features, Packet feature extractor, Improved Correlation and statistical Feature. Reducer provides the combined feature set. From the extracted feature set, appropriate features are selected via Weightage based Improved hybrid model combining the models like Long short term memory(LSTM) and Deep Maxout(DMO) networks is used. The weights of LSTM and DMO are optimally chosen using White Shark-Remora Optimization (WSROA) algorithm. Recursive Feature Elimination (RFE) process is used to precisely select the features for DDoS attack detection. The findings shows that the proposed hybrid bio-inspired algorithm outperforms with an accuracy of 94% compared to LSTM, Convolutional Neural Networks(CNN) and Deep Belief Networks(DBN). © 2025 IEEE.

关键词： Convolutional neural networks

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Machine Learning Models for Android Malware Detection–A Comparative Study 3

Machine Learning Models for Android Malware Detection–A Com...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arun, N. Nisha Dayana, T.R. Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

In the mobile ecosystem, Android apps are expanding quickly, but malicious software for Android is also proliferating in ever-increasing quantities. The issue of identifying malware on Android has been examined by several researchers, who have proposed ideas and approaches from various angles. According to current research, machine learning (ML) is a promising and successful method of detecting Android malware. This study is performed to compare the various ML methods for Android malware detection, including Random Forest (RF), Decision Tree,(DT) Random Tree(RT), Support Vector Machine (SVM), and XGBoost. The study assessed these models using a Androzoo dataset of Android apps, taking into account the important performance parameters as detection latency, accuracy, precision, and false alarm rate (FAR). © 2025 IEEE.

关键词： Adversarial machine learning

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Emotional Contribution to Parkinson's Disease Diagnosis using Machine Learning for EEG Dataset Generation 6

Emotional Contribution to Parkinson's Disease Diagnosis usin...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Arockiya Selvi, S. Kamalakannan, T. Department of Computer science VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Emotion is the root cause of nearly all harmful illnesses in humans. Emotion affects blood pressure, heart rate, kidney function, and a few disorders of the nervous system. Parkinson's disease, which causes impairment of movement, is one of the brain disorders. This issue is caused by a reduction in dopamine release in the cortex of the brain. Dopamine production deviations have been related to essential hypertension. Dopamine has a crucial role in anxiety modulation in multiple regions of the brain. Patients with Parkinson's disease frequently indicate that acute stress worsens their motor symptoms, especially gait freezing, dyskinesia, and tremors and the emotions play a substantial role in Parkinson's disease. This study focuses on the Classification Learner algorithms and the Electro Encephalogram (EEG) dataset, both of which covered the diagnostic range of PD development. The classifiers were evaluated using key performance indicators such as error rate, accuracy, sensitivity, specificity, and precision. The findings illustrate the convergence of emotional analysis, machine learning (ML), and neuroscience in improving diagnostic capabilities by demonstrating how a combination of EEG datasets with KNN algorithms offers a reliable and accurate approach for early Parkinson's disease identification. © 2025 IEEE.

关键词： Blood pressure

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An Enhanced Detection System of Autism Spectrum Disorder Using Thermal Imaging and Deep Learning

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SN computer science 2025年第2期6卷 1-11页

作者： Jegan Amarnath, J. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Tamil Nadu Chennai 600117 India

Autism Spectrum Disorder (ASD) is a brain-based condition characterized by social difficulties and repetitive behaviors. Traditional diagnostic methods can be subjective and time-consuming. Current diagnostic techniques for ASD often rely on behavioral assessments and clinical observations, which can be inconsistent and prone to subjectivity. Additionally, there is a need for more reliable and objective methods to differentiate between autistic and non-autistic individuals. Existing imaging techniques require further refinement to improve accuracy and efficiency in detecting ASD. The primary objective of this research is to develop and evaluate an enhanced detection system for ASD using thermal imaging and Improved Mask Faster Recurrent Convolutional Neural Network (IMFRCNN). This research focuses on enhancing the detection of ASD using Deep Learning (DL) methods and thermal imaging. Traditional diagnostic techniques for ASD often rely on subjective behavioral assessments, highlighting the need for more reliable methods. By examining cerebral imaging data from the thermal face image datasets collect facial thermal images using thermal cameras. Ensure the dataset is diverse, including varying expressions and environmental conditions. The study compares the effectiveness of IMFRCNN and the ResNet 50 system in classifying thermal images of 50 autistic and 50 non-autistic individuals. Emotions were evoked using audio-visual stimuli, and temperature variations in facial areas such as the nose, forehead, cheek, and eyes were measured. Anger showed the greatest temperature differences, with a 12.6% variation between autistic and non-autistic individuals. The IMFRCNN achieved a reliability of 96%, while the ResNet 50 achieved 90%. These findings indicate that computer-aided assessment techniques utilizing thermal imaging and DL can provide a consistent and objective method for identifying individuals with ASD. © The Author(s), under exclusive licence to Springer Nature Sing

关键词： Deep learning algorithms Diagnostic tool Improved mask faster recurrent convolutional neural network Performance measures

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A Methodical Approach to Healthcare Cybersecurity: Handling Current Risks and Advancements 6

A Methodical Approach to Healthcare Cybersecurity: Handling ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Husain Bathushaw, M. Nagasundaram, S. Department of Computer Applications VELS Institute of Science Technology and Advanced Studies Pallavaram Chennai600117 India

ISBN: (纸本)9798331522667

The methods were evaluated using machine learning-driven interruption detection, blockchain-enhanced access control for inconsistency localization, and structured threat insight data in a simulated healthcare environment. With an accuracy range of 0.88-0.94 and lift specified between 0.75 and 1, knowledge values ranging from.86 to.92, and F1 scores between and above, the test results demonstrated that all computations were possible. BACA strengthened access control with blockchain;ADA surpassed expectations in identifying discrepancies;TIAA succeeded in risk insights management;and ML-IDS achieved outstanding results in intrusion detection. A comparison with related research highlights the significance of these algorithms in tackling specific cybersecurity issues in the healthcare sector. Because the proposed algorithms provide a holistic approach to safeguard confidential health information, ensure the validity of assessment models, and strengthen organizations against a range of dynamic cyberthreats, they are pertinent to the expanding discussion about cybersecurity in the healthcare industry. © 2025 IEEE.

关键词： Electronic health record

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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A Novel Framework for Human Chronic Disease Prediction Using CNN and Adaptive Dragon Fly Optimization (ADFO) Algorithm

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SN computer science 2025年第4期6卷 1-18页

作者： Ragu, B. Perumal, S. Department of Computer Science School of Computing Sciences Technology and Advanced Studies (VISTAS) Vels Institute of Science Tamil Nadu Chennai 600117 India

Chronic diseases in human beings are long-lasting health disorders that will progress slowly over months or a lifetime. The chronic diseases can affect various parts or systems of the body and are non-acute in nature. The chronic disease does not occur suddenly and shall not be resolved quickly. However, the spreading of the disease to other parts of the body and the effectiveness of the disease shall be suppressed by providing proper medical treatment from the earlier stages. The World Health Organization has expressed the significance of the treatment of chronic disease, stating that around 41 million deaths, representing 71% of all deaths occurring every year due to chronic diseases. This necessitates the earlier detection of human chronic diseases like cancer, cardiovascular disease and diabetes mellitus. The major challenge associated with chronic disease is that it does not express valid symptoms at the earlier stage and needs advanced technology for the earlier detection process with high accuracy. The existing system of chronic disease predictions falls short in diagnosis and detection at the earlier stages, whereas capable of detecting at stage 3 or stage 4, which were considered the advanced stage of the chronic disease. To overcome this challenge, this proposed work introduces a novel framework encompassing the Convolutional Neural Networks (CNN) and the Adaptive Dragon Fly Optimization algorithm for the earlier prediction process. The proposed work was trained with datasets of various chronic diseases individually to prove the system’s adaptability. In addition, the performance of the proposed work is analyzed in terms of accuracy, Precision, recall and F score, which were compared with the performance of the existing state-of-the-art methodologies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive dragon fly optimization algorithm Cancer Cardiovascular disease Convolutional neural networks Human chronic disease

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