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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of Computer science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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Dynamic Load Balancing in Cloud Computing Using a Hybrid Bidirectional LSTM-RNN with Deep Learning VM Snapshot Protocols

Dynamic Load Balancing in Cloud Computing Using a Hybrid Bid...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Thumma Sumanth Babu E. Bijolin Edwin V. Ebenezer Division of Computer Science & Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of CSE Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Dynamic load balancing is the ability in cloud computing to distribute workload across multiple virtual machine (VM), such that the performance of overall performance and resource utilization can be optimized. The proposed system introduces a new hybrid approach of using a Hybrid Bidirectional Long Short-Term Memory RNN (BILSTM-RNN) and a Deep Learning-based VM snapshot Protocol. This approach dynamically schedules tasks in real time based on the prediction of task execution time, task resource requirement and task-VM allocation. This model, therefore, allows the Hybrid Bidirectional LSTM RNN to identify temporal trends in the workload allocation and accurately assign tasks the VM Snapshot Protocol to compensate for system restoration and VM state transfer, increasing the systems transferability of lift-ups and downwards-levelling. The system also leverages these deep learning techniques for better load balancing, quicker processing lags, and throughput. Extensive simulations in a cloud environment show that this model surpasses traditional load balancing algorithms in task scheduling efficiency, scalability, and response time reduction, making it well-suited for large-scale, diverse cloud infrastructures.

关键词： Deep learning Cloud computing Protocols Computational modeling Bidirectional long short term memory Load management Dynamic scheduling Virtual machines Resource management Load modeling

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Automated Subjective Answer Evaluation using GenAI and NLP by Enhancing Accuracy, Fairness, and Feedback in Education

Automated Subjective Answer Evaluation using GenAI and NLP b...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Yuvasri J Senbagavalli M Rahul R R Venkatesan G Naveen Sundar Saravana Kumar C S Division of CSE Karunya University Coimbatore India Department of CSE & IT Alliance University Bangalore Data Science and Cyber Security Karunya University Coimbatore Global Tech Park Bosch Global Software Technologies Coimbatore

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The problem in subjective evaluation of answer is that the traditional evaluation is not consistent, not fair, and not time efficient. Utilizing Generative AI (GenAI) and Natural Language Processing (NLP), this project presents an innovative technique which automates the evaluation process for subjective answers. It consists of the system that analyzes student’s response using predefined grading rubrics to assign them scores based on quality and relevance and gives them personalized feedback. The system, using a fine tuned GenAI model, reduces human bias and inconsistencies by being nuanced on context and coherence. The system is an easy to use and user friendly tool for Educators to manage and review evaluations through Streamlit hosted system. The solution proposed aims to achieve higher grading accuracy, reduce assessment workflow complexities, while providing perspective that can inform students learning on what is being assessed. The potential applications of this technology are great and include essay grading, peer review and professional training test selection.

关键词： Training Accuracy Costs Generative AI Reviews User interfaces Control systems Natural language processing Complexity theory Context modeling

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A Failure-Free and Efficient Discrete Laplace Distribution for Differential Privacy in MPC

arXiv

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arXiv 2025年

作者： Tjuawinata, Ivan Wang, Jiabo Yang, Mengmeng Lyu, Shanxiang Wang, Huaxiong Lam, Kwok-Yan Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore Information Security and Privacy Group Data61 CSIRO Australia College of Cyber Security Jinan University Guangzhou China Division of Mathematical Sciences Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore

In an MPC-protected distributed computation, although the use of MPC assures data privacy during computation, sensitive information may still be inferred by curious MPC participants from the computation output. This can be observed, for instance, in the inference attacks on either federated learning or a more standard statistical computation with distributed inputs. In this work, we address this output privacy issue by proposing a discrete and bounded Laplace-inspired perturbation mechanism along with a secure realization of this mechanism using MPC. The proposed mechanism strictly adheres to a zero failure probability, overcoming the limitation encountered on other existing bounded and discrete variants of Laplace perturbation. We provide analyses of the proposed differential privacy (DP) perturbation in terms of its privacy and utility. Additionally, we designed MPC protocols to implement this mechanism and presented performance benchmarks based on our experimental setup. The MPC realization of the proposed mechanism exhibits a complexity similar to the state-of-the-art discrete Gaussian mechanism, which can be considered an alternative with comparable efficiency while providing stronger differential privacy guarantee. Moreover, efficiency of the proposed scheme can be further enhanced by performing the noise generation offline while leaving the perturbation phase online. © 2025, CC BY-NC-ND.

关键词： Differential privacy

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An Intelligent security-Enabled Home Automation System using Internet of Things Environment 3

An Intelligent Security-Enabled Home Automation System using...

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

作者： Karthikeyan, P. Dinesh, M. Akila, A. Veeramuthu, A. Giri, Jayant Al Sherideh, Ala'a Prathyusha Engineering College Department of Eee Tiruvallur Chennai India Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Puduvoyal India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

IoT technologies to bolster residential security, safety, and sustainability. By leveraging facial recognition for door access, enhancing fire safety monitoring, and integrating energy harvesting, it aims to create more resilient and eco-friendly residential environments. These innovations offer comprehensive solutions to address evolving challenges and optimize residential living. The proposed research introduces an innovative smart home automation system leveraging IoT technologies and Google Assistant for the voice-controlled operation of household devices. It integrates a comprehensive array of sensors for safety and security monitoring, facilitating real-time alerts for fire outbreaks, unauthorized entry, and environmental anomalies. MQTT enables smooth remote access and management of devices. The research evaluates IoT routing protocols, highlighting ISHAS-IoTs's benefits in efficiency, delivery ratio, and delay. These findings underscore the protocol's potential to enhance IoT network performance and efficiency, contributing to the design of more robust and reliable smart home systems. © 2025 IEEE.

关键词： Smart homes

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An Intelligent security-Enabled Home Automation System using Internet of Things Environment

An Intelligent Security-Enabled Home Automation System using...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P.Karthikeyan M. Dinesh A.Akila A. Veeramuthu Jayant Giri Ala'a Al Sherideh Department of EEE Prathyusha Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Tamil Nadu Department of Information Technology Sathyabama Institute of Science and Technology Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Division of Research and Development Lovely Professional University Phagwara India Department of Cyber Security College of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

关键词： Automation Smart homes Throughput Routing protocols Energy efficiency Delays Internet of Things security Monitoring Sensor arrays

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Effectiveness of a touch-based hand therapeutic application for hand dexterity in children with handwriting difficulties-a pilot study

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Multimedia Tools and Applications 2024年 1-35页

作者： Smitha, John Renumol, V.G. School of Computer Science and Technology Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India School of Engineering Cochin University of Science and Technology Kochi India

Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of issues in their fine motor skills. Nowadays, technology-based instructions are available to develop fine motor skills in children with handwriting disabilities. This paper describes the details of a newly developed software application namely ‘HanDex’ for children with handwriting difficulties and the experiments conducted to test its effectiveness. The application comprises six hand therapeutic activities which were designed based on the oscillation theory of handwriting. Initially, in the first phase, the experiments were conducted in nine primary school children from various schools, aged 8–12 years. Then, in the second phase, the software training was given to six upper primary school children (10–12 years) with handwriting issues. The results of using this application were promising. So one more study was conducted in another set of nine children (8–11 years) with handwriting difficulties. The participants were assessed by their special educators, class teachers and parents. After the pretest, they received a total of 18 handwriting practice sessions using this new application. At the end of the training sessions, a post test was conducted, to measure the progress in handwriting. The handwriting rubrics with eight handwriting parameters such as letter formation, placement, letter sizing, spacing of letters, legibility, speed, neatness, and spacing of words were used for handwriting assessment. The findings from the study suggested that touch-based hand therapeutic activities were beneficial for children with writing difficulties to improve their handwriting performance and tripod grip. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Children with handwriting difficulties Hand Dexterity Handwriting performance and tripod grip Tablet computer ‘HanDex’ Software

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A Comparative Study of Metric-based Meta-Learning Methods for Improving Few-Shot Learning in Drug Discovery with Limited data 1

A Comparative Study of Metric-based Meta-Learning Methods fo...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： George, Gilda Juliet, Sujitha Karunya Institute of Technology and Sciences Division of Data Science Amd Cyber Security Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798350372748

Drug discovery is a complex and costly process, often requiring extensive testing and significant financial investment. However, the acquisition of sufficient data for effective machine learning models presents a significant challenge. In this study, it is to investigate the application of few-shot learning - a machine learning approach that can function with low data - to address this challenge. Specifically, to investigate various metric-based meta-learning techniques within the context of drug discovery. The proposed research analysis includes Prototypical, Matching, Siamese, and Relation Networks, two promising few-shot learning methods. Through rigorous experimentation and comparative analysis, to evaluate the efficacy of these approaches across diverse datasets, drawing insights into their suitability for drug discovery tasks with limited data. The resultant findings shed light on the potential of few-shot learning techniques to accelerate drug discovery processes and highlight the importance of selecting appropriate methodologies based on the nature of the available data. © 2024 IEEE.

关键词： Learning algorithms

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Cooperative Hypothesis Building for Computer security Incident Response 5th

Cooperative Hypothesis Building for Computer Security Incid...

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5th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Motoura, Shota Hoshino, Ayako Hosomi, Itaru Data Science Research Laboratories NEC Corporation Kawasaki Japan Cyber Security Strategy Division NEC Corporation Kawasaki Japan

ISBN: (纸本)9789811641763

We have been developing an AI-based system that automatically collects evidence from logs on computers within an intranet and infers the most plausible hypothesis of a cyber-attack. In this paper, we propose a user-interactive functionality which enables the system to incorporate knowledge and insight from the user in order to build the hypothesis. We show the system’s practicality with example use cases and also confirm the necessity of such functionality for the system by interviewing with cyber security analysts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crime

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Customer Churn Prediction Using Machine Learning: Commercial Bank of Ethiopia

Customer Churn Prediction Using Machine Learning: Commercial...

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2022 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2022

作者： Seid, Muhamed Hassen Woldeyohannis, Michael Melese Cyber Security Research Division Data Science Addis Ababa Ethiopia Addis Ababa University School of Information Science Addis Ababa Ethiopia

ISBN: (数字)9781665455879

ISBN: (纸本)9781665455879

Identification of churned customers is critical to the operation and growth of any business. Identification of churned customers can assist businesses in understanding the reasons for churn and planning market strategies to boost business growth. The purpose of this study is to design and develop a machine learning model that can accurately predict churned customers from the total customers of the Commercial Bank of Ethiopia (CBE)in order to retain existing customers. A total of 204,161 datasets with eleven attributes were used for this study. For this study, the overall accuracy of the model was used as the evaluation metric to determine the best classifier. Accordingly, supervised machine learning methods such as Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, and Deep Neural Network were used to predict customer churn in a Commercial Bank of Ethiopia (CBE) context. Based on previous literature, these classifier algorithms for customer churn prediction have been widely used. In this study, feature importance and a correlation matrix were used to select features. Furthermore, the SMOTE technique is used to balance the data, and the results for the chosen algorithm were evaluated and compared. Among the various experiments performed, a Deep Neural Network (DNN)outperformed with an accuracy of 79.32%, precision of 85.08%, and recall of 78.19%. © 2022 IEEE.

关键词： Deep neural networks

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