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IoT botnet detection from software defined network using American zebra optimization algorithm with SSRNN-ELM

International Journal of Information Technology (Singapore) 2025年第2期17卷 959-967页

作者： Bindu, Nalluri Venkata Madhu Nassa, Vinay Kumar Vasuki, P. Manikandan, G. Jeena, R. Mahaveerakannan, R. Department of CSE-AIML Malla Reddy Engineering College (Autonomous) Medchal Secunderabad Malkajgiri (Dist) India Ellenki College of Engineering and Technology (ECET) Patelguda (v) Ameenpur (m) Sangareddy (D) Hyderabad Telangana India Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

Software-defined networking (SDN) offers centralized control over large-scale networks, enhancing flexibility and enabling tailored network applications. However, SDN introduces new security vulnerabilities, including the risk of botnet attacks, which can compromise systems and steal data. This paper proposes a novel approach for detecting botnet attacks in SDN environments by leveraging a hybrid model that integrates recurrent neural networks (RNNs) and extreme learning machines (ELMs). The proposed method utilizes RNNs for feature learning and ELMs for classification, with the American zebra optimization algorithm (AZOA) optimizing ELM weights. This hybrid SSRNN-ELM (supervised subset recurrent neural network—extreme learning machine) approach is evaluated using N-BaIoT dataset and performance metrics, demonstrating effective detection of complex botnet attacks with high accuracy. The results demonstrate that the hybrid model effectively identifies complex botnet attacks, achieving a detection accuracy of 96.60%. The integration of recurrent neural networks (RNNs) for feature learning and extreme learning machines (ELMs) for classification, along with optimization by the American zebra optimization algorithm (AZOA), significantly improves detection performance as compared to the recent existing studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： American zebra optimization algorithm (AZOA) BotNet attack detection Extreme learning machine (ELM) Recurrent neural network (RNN) Software-defined network (SDN)

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NEMO: ML based fish species classification

NEMO: ML based fish species classification

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Manvendra Singh Sreesh Gaur Sarthak Tyagi Udit Sharma Prince Yadav Adarsh Mishra Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad Department of CSE-AIML ABES Engineering College Ghaziabad U.P.

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This paper describes the creation of an ML-based tool for image-based identification of fish species commonly found in Indian oceans, addressing the issues that fishermen experience in accurately reporting their catch. Motivated by the Smart India Hackathon (SIH) issue statement from the Indian National Centre for Ocean Information Services (INCOIS), our goal is to improve marine fishing advice services. We developed a dataset that includes photos of 21 fish species typically captured by Indian fishermen, precisely chosen to ensure clarity and little background noise. Using this dataset, we applied transfer learning to eight different pre-trained models to determine the most effective one for deployment in a mobile application. The Kotlin-developed program has an easy-to-use interface with catch history, fish nutritional values, and pertinent weather data. Our findings show that the installed models can correctly recognize fish species, with MobileNetV2 reaching a peak accuracy of 97.61%. In addition to expediting the species identification process, this cutting-edge tool provides fishermen with crucial information, which eventually leads to more accurate species-level catch reporting.

关键词： Accuracy Transfer learning Manuals Fish User experience Real-time systems Mobile applications History Optimization Meteorology

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Early Detection Of Alzheimer's Disease using Transfer Learning on MRI Data 3

Early Detection Of Alzheimer's Disease using Transfer Learni...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sekar, K. Meena, R. Monisha, S. Dheepa, T. Niraimathi, A.K. Borah, Malaya Dutta Rajalakshmi Engineering College Department of Aiml Chennai India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India National Institute of Technology Silchar Department of Cse Silchar India

ISBN: (纸本)9798331508456

Early detection of Alzheimer's Disease (AD) is crucial for effective patient care and timely intervention. Traditional diagnostic approaches often struggle due to the subtle progression of the disease. This study addresses the challenges of Alzheimer's detection by employing advanced predictive techniques. The proposed Modified Xception model leverages MRI datasets to improve early detection accuracy, achieving an accuracy of 0.96, precision of 0.92, and recall of 0.93. The dataset, sourced from Kaggle, includes four progression stages: Non-dementia (ND), Very Mild dementia (VMD), Mild dementia (MD), and Moderate dementia (MoD). Comprehensive preprocessing methods, including skull stripping, intensity normalization, and data augmentation, ensure high-quality input data. This approach advances neuroimaging analysis, offering a scalable and robust solution for enhancing patient outcomes and optimizing disease management strategies. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Integrating Behavioral Analytics and AI for Enhanced Academic Performance Prediction Using Graph Convolution Networks and Deep Learning Techniques

Integrating Behavioral Analytics and AI for Enhanced Academi...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： S. Baghavathi Priya Sangeetha K Velayutham P Balaji VS Shrinithi S Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Department of AIML Rajalakshmi Engineering College Chennai Department of CSE Aarupadai Veedu Institute of Technology Chennai

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The work explores the integration of behavioral analytics and artificial intelligence (AI) techniques, specifically Graph Convolutional Networks (GCNs) and deep learning, to predict student academic performance. By analyzing study habits, assignment submissions, gaming behavior, and exam scores, the framework bridges traditional evaluation methods and behavioral insights. Results demonstrate that a GCN model outperforms other machine learning approaches, achieving a Mean Squared Error (MSE) of 117.97. The findings highlight the potential of machine learning to enhance educational strategies, identify at-risk students, and provide actionable insights for data-driven educational improvements.

关键词： Deep learning computers Bridges Graph convolutional networks Convolution Education

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Intelligent Control Mechanisms for Renewable Energy Integration Using Machine Learning Models

Intelligent Control Mechanisms for Renewable Energy Integrat...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Suman Chahar Kawaljit Kaur Kuldeep Singh Kaswan Kiran Malik University Institute of Computing Chandigarh University Mohali India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering-AIML GL Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The deployment of renewable energy sources (RES) in power systems used to be a major challenge owing to their intermittent and uncertain nature. Therefore, in this paper, the use of ML models as an intelligent control system to improve the operation of RES is studied. We survey different types of ML approaches, covering supervised learning for energy predic- tion, unsupervised learning for clustering demand, reinforcement learning for energy storage management, and deep learning for real time data processing. The intent of these advanced control strategies are to balance and stabilize the grid, harmonize demand response, and augment energy storage. We highlight the use-cases and impacts of ML from real-world cases across various geographies, showing operational gains and peak-demand savings. While these technologies hold great promise, they also face challenges like data privacy, interpretability of models, and availability of high-quality datasets. Moving forward, there is a need to improve transparency of algorithms and to strengthen the collaboration of different stakeholders. Our work paves the way for a systematic and automated approach to renewable energy integration, which can serve as a critical factor to build a reliable and efficient future modern power systems with machine learning.

关键词： Deep learning Renewable energy sources Uncertainty Power system stability Real-time systems Demand response Power system reliability Unsupervised learning Intelligent control Energy storage

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Early Detection Of Alzheimer's Disease using Transfer Learning on MRI Data

Early Detection Of Alzheimer's Disease using Transfer Learni...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sekar K Meena R Monisha S Dheepa T Niraimathi A K Malaya Dutta Borah Department of AIML Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Tiruchirappalli India Department of CSE National Institute of Technology Silchar Silchar India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

关键词： Neuroimaging Adaptation models Accuracy Magnetic resonance imaging Transfer learning Data augmentation Feature extraction Data models Integrated circuit modeling Alzheimer's disease

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A Crossbreed and Powerful Machine Learning Archetype for Unparalleled Safety and Effectiveness in Wireless Sensor Networks

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SN computer science 2025年第4期6卷 1-11页

作者： Sharma, Deepak Shahnaz, K.V. Vidhya, T. Helan Srinivas, Korra Burra, Lakshmi Ramani Reddy, Yeruva Jaipal Joshi, Shubham Department of Computer Science Aryabhatta College University of Delhi New Delhi India Department of ECE Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology TN Chennai India Department of ECE (Advanced Communication Technology) R.M.K Engineering College TN Kavaraipettai Thiruvallur India Department of CSE-AIML St. Martin’s Engineering College Telangana Secunderabad India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of ECE Narasaraopeta Engineering College AP Narasaraopet India Department of AI-DS Vishwakarma Institute of Technology Savitribai Phule Pune University Pune Pune India

When a Wireless Sensor Network successfully executes a transaction within a finite timeframe and with minimal resource consumption, it is deemed active and operational. The underlying challenge in all scenarios boils down to a common issue as Denial of Service. This challenge encompasses endeavours to enhance the enforcement of consumption and routing policies, striving for improved security and reliability. In this study, we leverage a machine learning approach, utilizing a covariance vector derived from eigenvalues to identify anomalous behaviour in nodes. The CDS parameter of each node proves pivotal in adapting the selection policy to the current conditions, guiding a suitable course of action. Decision values for likelihoods are dynamically adjusted, serving as either rewards or punishments based on prior actions. A real-time payment function is established as a reward mechanism, contingent on the action taken and its utility, such as whether a forwarded packet was dropped or delayed. At the second level, the same learning model is employed to discern between malicious and benign nodes. The proposed method showcases a notable enhancement, including a 98% improvement in Packet Delivery Ratio (%), 84% reduction in delay (seconds), 93.2% decrease in jitter (seconds), and an 86% boost in Goodput (Kilobits per second). These substantial improvements validate the efficacy of the proposed method when compared to existing methodologies employed in the same research context. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DoS Traffic flow Traffic variance WSN

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A Machine Learning System to Enhance IT Security in Human Resource Management

A Machine Learning System to Enhance IT Security in Human Re...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Soummya Chowdhury Hardeo Kumar Thakur Jagendra Singh Ashish Kumar Mathur Nilesh Kumar Sen Isha Gupta School of Business GITAM (Deemed to be) University Hyderabad India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE-AIML ABES Engineering College Ghaziabad India Department of Computer Science Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

With the increasing reliance of Human Resource (HR) management on digital systems, protection of sensitive employee data is critical to prevent unauthorised access. This research addresses the necessity of an advanced security system in detecting and mitigating potential security threats in HR operations. A machine learning (ML)-based system was thus designed and implemented, using the models support vector machines, recurrent neural networks, autoencoders, and random forests. The training was thus conducted utilising a mix of real-world and synthetic data, specifically including HR system logs as well as user activities and various security events, including login attempts and unauthorised access to some files. After feature engineering and preprocessing, all the models were evaluated with respect to the metrics of accuracy, precision, recall, and F1 score. Results indicated that Support Vector Machine (SVM) outperformed all models in terms of 98.54% accuracy with low latency, 15 ms, a throughput of 150 predictions per second, and 1.2 seconds for the detection time. These results point out that the SVM model is suitable for real-time applications, showing that it is efficient in detecting security breaches and scalable in large HR systems. Findings indicate that machine learning models, especially SVM, can be used to increase IT security in HR management, allowing for timely detection and mitigation of security threats.

关键词： Support vector machines Accuracy Recurrent neural networks Autoencoders Throughput Real-time systems Security Human resource management Low latency communication Synthetic data

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A Hybrid Deep Learning Framework for Efficient Network Intrusion Detection Systems using Data Augmentation

A Hybrid Deep Learning Framework for Efficient Network Intru...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Priya Shirley Muller K Madhavi Naga Durga Saile K B Prasanthi B Jayaram Goski Sathish Department of Mathematics Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Mallareddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering (AIML& IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Departmentt of Computer Science (AI&ML) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering RMK Engineering College Gummidipoondi India Department of Information Technology St.Martins Engineering College (Autonomous) Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Because of their critical position as the initial barrier against harmful actions, intrusion detection systems (IDSs) rely substantially on the efficacy of cybersecurity in protecting corporate communication. There are several limits to machine learning approaches that be solved by utilizing other deep learning architectures, even though they are widely used for intrusion detection. In addition, unbalanced datasets frequently make it difficult to evaluate the suggested models, which prevents a thorough evaluation of the models' effectiveness. This study seeks to tackle these difficulties by improving the performance of numerous deep-learning(DL) structures for IDS using data augmentation approaches. The experimental results shown that even a basic CNN-based architecture may produce very efficient network-attack-detection, but more complicated designs only managed to show slight performance increases. The results show that cybersecurity frameworks can easily incorporate the suggested deep learning-based intrusion detection approaches, which improves the capacity to identify and counteract complex network threats. The outcomes of this research exhibit that the accuracy of intrusion detection models is highly dependent on the dataset's quality and quantity, with augmented CIC-IDS-2017 showing an accuracy of up to 92%.

关键词： Deep learning Accuracy Communication systems Network intrusion detection Complex networks Data augmentation Integrated circuit modeling computer crime

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Unmasking the Reality: A Comprehensive Study of Deepfake Identification Techniques and Their Expanding Terrain

Unmasking the Reality: A Comprehensive Study of Deepfake Ide...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Shubh Sharma Disha Mohini Pathak Dhyanendra Jain Somya Srivastava Prashant Vats Govind Murari Upadhyay Department of Computer Science ABES Engineering College Ghaziabad U.P India Department of Computer Science Engineering-AIML ABES Engineering College Ghaziabad U.P India Dept. of CSE School of CSE Manipal University Jaipur Jaipur Rajasthan India Dept. of Computer Application Manipal University Jaipur Jaipur Rajasthan India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity.

关键词： Deep learning Deepfakes Accuracy Computational modeling Refining Media Benchmark testing Feature extraction Real-time systems Resilience

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