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Decentralized Intrusion Detection in Peer-to-Peer Networks using Stacking Ensemble Model 1

Decentralized Intrusion Detection in Peer-to-Peer Networks u...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Devi, R. Renuga Riadhusin, Rame Anu, D. Swarnateja, R. Revathi, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, due to the rise in Peer to Peer (P2P) networks has brought a significant way in which the data is shared and services are offered over the internet. These P2P networks make use of decentralized architecture in which nodes directly interact and exchange resources. However, due to the use of decentralized architecture, the P2P networks are exposed to various security threats such as data breaches, and other malicious attacks. In this research, Stacking Ensemble Model (SEM) is proposed for network intrusion detection in P2P networks. Initially, the data is considered from network intrusion detection dataset and then preprocessed with data imputation and min-max normalization effectively handles the missing values and rescales the data into significant range. After that, Recursive Feature Elimination (RFE) is incorporated to select optimal features from preprocessed data based on feature importance. Finally, the proposed SEM effectively detects the intrusion in P2P networks which enhances the security. From the results, the proposed SEM model obtained a better outcome in terms of accuracy, precision, recall, and F1-score compared to the existing K-Nearest Neighbor (KNN) by accomplishing 97.20%, 94.71%, 96.62%, and 95.65%, respectively. © 2025 IEEE.

关键词： Network intrusion

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Prediction of Employee Attrition Classification and Retention Strategies using Gradient Boosting with Binary Logistic Regression 2

Prediction of Employee Attrition Classification and Retentio...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Mamatha Bai, B.G. Hassan, Muntather Muhsin Sri, B. Aruna Latha, P. Vinola, F. Anitha Florence Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Maisammaguda Hyderabad India Department of Information Technology SSM College of Engineering Namakkal India Department of Mathematics New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

A voluntary or involuntary parting of an employee from an organization is known as and the strategies to maintain an employee are known as Employee attrition (turnover) and retention. Employee attrition and retention are crucial Human Resource (HR) concerns, where attrition impacts the organizations performance, profitability, and productivity. Several methods have been proposed to predict attrition of employees, but the existing methods have overfitting, class imbalance, interpretability, and generalizability problems. To overcome these issues, a novel Gradient Boosting-Binary Logistic Regression approach is proposed to predict the employee’s turnover in an early stage. Initially, an IBM-HR dataset is considered as the input for preprocessing, which is performed using Standard Scalar to preserve data distributions. Next, One-hot encoding is assessed for data encoding to convert categorical variable into numerical variables. Followed by, feature extraction using Principal Component Analysis (PCA) for faster training and enhancing the performance of model. Later, Gradient Boosting-Binary Logistic Regression (GB-BLR) is employed for classification to analyze and predict attrition of employee’s. The proposed GB-BLR method obtained better performance when compared with other existing Extra Trees Classifier (ETC) method in terms of accuracy, precision, recall, and f1 score of 97.48%, 98.57%, 96.25%, and 98.77% respectively. © 2024 IEEE.

关键词： Logistic regression

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Convolutional Neural Network Based Glaucoma Detection and Classification using Optic Disc Localization 2

Convolutional Neural Network Based Glaucoma Detection and Cl...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Neelagar, Mahesh B Manjunath, T.C. Patra, Raj Kumar Jagadish, Sripelli Mazumder, Debarshi Visvesvaraya Technological University Dayanand College of Engineering Bengaluru Department of ECE Karnataka Belgavi India CMR Technical Campus Department of Computer Science and Engineering Hyderabad India SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of AI and DS Bengaluru India

ISBN: (纸本)9798350317558

Glaucoma in the retina is considered a significant cause of irreparable vision loss, and automated glaucoma detection on fundus images has seen the development of several approaches recently. Although early diagnosis and treatment can prevent possible blindness, manually segmenting the optic disc depends on the availability of a large number of labeled samples for the training phase. The proposed method utilizes U-Net for segmentation based on labeled and unlabeled data for prediction and data enhancement with a large number of feature channels in up sampling. This approach can improve overall performance by considering pixel-level and image-level labels during network training and encoding the data train, differentiating U-Net from Convolutional Neural Network (CNN). Classification is performed using CNN for diagnosing glaucoma, evaluated on a large training dataset. The obtained results demonstrate that the proposed U-Net model achieves better accuracy, reaching 0.99 on the Drishti dataset. These results ensure accurate segmentation and classification performance compared to other existing methods such as CNN and Multi-layer Neural Network. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Deep Neural Network Glaucoma Detection ResNet-50 and U-Net

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Jotter: An Approach to Summarize the Formal Online Meeting

Jotter: An Approach to Summarize the Formal Online Meeting

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Sumedh S Bhat Uzair Ahmed Nawaz Sujay M Nameesha Tantri Vani Vasudevan Computer Science Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India

This paper examines various meeting summarising techniques. It also presents a hybridised technique for meeting summarization that combines abstractive and extractive techniques. Text extraction from meetings is accomplished in two ways: audio during the meeting and text recognition from the screen. These extracted texts are then compiled to create a meeting summary. In contrast to extractive text summarising, which concatenates essential sentences from paragraphs, abstractive text summarization focuses on providing a coherent summary of the given content. The maj ority of recent progress on abstractive summarization has been using RNN or Recurrent Neural Networks, although RNN-based algorithms fail when dealing with long sequences. This paper proposes hybridizing these two methods sequentially for meeting summarization to create a better overall summary of the meeting. This research endeavour seeks to convert conference video and audio into text documents and then run our hybridised text summarizer model on them. The result is a summarised text document that the various stakeholders, whether present at the conference or not, could utilise for rapid and concise reference. It will help the people who are not been able to attend the meeting can get the main points discussed in the meeting through summary.

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Decision Support Method and Risk Analysis Based on Merged-Cyber Security Risk Management 2

Decision Support Method and Risk Analysis Based on Merged-Cy...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Devarajan, Mohanarangan Veerappermal Al-Farouni, Mohammed Srikanteswara, Ramya Rana Veer Samara Sihman Bharattej, R. Kumar, Priyan Malarvizhi Sacramento United States College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Saint Leo University Department Of Computer Science FL United States University Of North Texas College Of Information Department Of Information Science TX United States

ISBN: (纸本)9798350381665

The Cyber Security Risk Management is the essential part in today's business because of quickly changing landscape of threat and presence of developing the cyber-attacks. Developed numerous researches which aim to built systematic process in risk assessment and management. This challenge developed particularly in content of difficult infrastructures, from attacks has involved from technic to social technic and protect against them required that contextual data. Though, the previous researches have restricted input from the intelligence properties of threat and developing the attack trends, resulted in restricted contextual data relevance to the risks of cyber security. In this research, developed Merged Cyber Security Risk Management (m-CSRM) method which responded to the difficulty through helping the systematic identity of critical assets by utilize of decision support method developed on fuzzy set mechanism through predict the risk types by Machine Learning (ML) methos and that is processed through automated tool. The proposed m-CSRM method attained highest success rate of 82.13% which is efficient than other existing methods. © 2024 IEEE.

关键词： Machine learning

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Classifying Network Abnormalities Into Faults and Attacks in Iot-Based Cyber-Physical Systems using Machine Learning 2

Classifying Network Abnormalities Into Faults and Attacks in...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kumar, P. Jagdish Alzubaidi, Laith H. Jasim Nagarjuna, N. Deshmukh, Ram Madhura, G.K. AVN Institute of Engineering and Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq CVR College of Engineering Department of CSIT Telangana India SR University Department of Electrical and Electronics Engineering Telangana Warangal India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350317558

Amidst the proliferation of Industry 4.0, the integration of artificial intelligence and smart techniques has emerged as a focal point in discussions surrounding industrial cyber-physical systems (CPS). It is still very difficult to detect anomalies in a way that protects security and productivity, especially when there isn't much labeled data available for cyber-physical security protection. A novel method called the Few-Shot Learning model with Siamese Neural Network (FSL-SNN) is presented in this paper with the goal of improving the accuracy of intelligent anomaly detection in industrial CPS and reducing the over-fitting problem. To calculate the distances between input samples using their optimum feature representations, a Siamese encoding network is developed. To bolster the efficiency of the training process, a robust cost function is designed, encompassing three specific losses. The culmination of these efforts results in the development of an intelligent anomaly detection algorithm. The results of experiments conducted on two datasets - one with sparse labels and the other fully labels shows the notable improvements that the proposed FSL-SNN achieves when it comes to lowering false alarm rate (FAR) of 0.041 and raising F1 score of 0.975 for intrusion signal identification in the context of industrial CPS security protection compared to the existing Siamese Convolutional Autoencoder. © 2024 IEEE.

关键词： Anamoly Cyber Physical System Few Shot Learning Intrusion Detection Siamese Neural Network

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Deep Learning and MRI Improve Carotid Arterial Tree Reconstruction

Deep Learning and MRI Improve Carotid Arterial Tree Reconstr...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Murthy M S, Narasimha Spandan, Gunti Aswani, Inakollu Nayak Bhukya, Shankar Ramachandra, A.C. Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Gst Gitam University Department of Computer Science Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

This study suggests using magnetic resonance imaging (MRI) scan data to segment and rebuild the carotid artery tree using a deep learning-based method. In the proposed method, the UNET architecture is used to divide an image into parts, and then a 3D level set method is used to make the parts even better. The method was compared to seven different approaches to segmentation and reconstruction, and it outperformed all but one of them. It had a 94% accuracy rate and took 12 hours to process. Deep learning's capacity to reliably segment and categorise pictures, the UNET architecture's ability to maintain spatial information through skip connections, and the level set approach's ability to improve segmentation further all contribute to the proposed method's effectiveness. The proposed approach has the potential to be a useful tool for clinicians in evaluating the status of the carotid artery and finding any irregularities. Stroke is one of the most devastating symptoms of cardiovascular disease, and recognising and treating carotid artery stenosis in its early stages can help avoid or reduce the risk of having a stroke. The success of the proposed method shows that deep learning techniques have the potential to improve the accuracy of carotid arterial tree reconstruction from MRI data. Better identification and treatment of carotid artery stenosis may increase the overall effectiveness of the proposed technique, ultimately leading to better patient outcomes. © 2023 IEEE.

关键词： Magnetic resonance imaging

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The Application of Machine Learning Algorithms to the Classification of EEG Signals

The Application of Machine Learning Algorithms to the Classi...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Gurumoorthy, Sasikumar Avikkal, Saritha Rani, M. Radha, K. Rajesh, N. J.J.College of Engineering and Technology Department of Computer Science and Engineering Trichy India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Electronics and Communication Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

In order to study cognitive states and detect deficiencies in thinking, planning, and judgement, automated cognitive assessment systems have been developed. Having access to the opportunity to collaborate with experts in their working environment is another benefit of these platforms, particularly in services where decisions have direct effects on people's lives. This dissertation assesses the brain's reaction to mental tasks including motor imagery, attention, working memory, mental fatigue, and attention deficit hyperactivity disorder (ADHD). Electroencephalography (EEG), which encodes neuron activity as action potentials, is a popular method for analyzing these cognitive functions. These EEG data are processed using state-of-the-art machine learning algorithms, automating cognitive ability testing in the process. Recent state-of-the-art approaches have shown problems with high dimensionality, irrelevant channels, subject and task variability, generalizability, and interpretability. Because of this, classifications of intelligence are often inaccurate. The use of meta-heuristics to the solution of both continuous and binary optimization problems was found to be very fruitful in this scenario. Despite increased performance in EEG signal processing for other applications, these methods have seen very little exploration in the field of cognitive evaluation. This encourages more research into these meta-heuristics approaches to intelligence testing. It is important to look for the most appropriate optimization strategy for a certain task, since all of them will not work in every circumstance. In order to address problems in cognitive testing, this research looks on parameter-free optimization algorithms that are tailored to each kind of test. One of the main problems that hinders the effectiveness of ML models is its dimensionality. Another is its lack of generalizability. The overarching goal of this proposed work is to build and construct an effective channel select

关键词： Electroencephalography

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Performance Analysis of CPU Utilization in Cloud Environments using Machine Learning Models

Performance Analysis of CPU Utilization in Cloud Environment...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Archana Naik Kavitha Sooda Karunakara Rai B Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India Department of Electronics and Communications Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cloud environments offer extensive services, making them a preferred choice for businesses and startups to host and maintain databases. Efficient resource management, including automatic scaling, is crucial for sustaining virtual machine environments. This paper addresses the challenge of forecasting CPU workloads to optimize resource allocation. Terraform was utilized to create and launch instances in the cloud environment, specifically using Amazon Web Services (AWS). A CPU-intensive application was executed on the server, and CPU utilization data was collected at regular intervals to serve as the dataset for the experiment. Three models—Gated Recurrent Unit, Long Short-Term Memory Autoencoder, and Seasonal Auto-Regressive Integrated Moving Average—were compared for short-term workload prediction. The results demonstrate that SARIMA outperforms the LSTM autoencoder by 2.91%, GRU by 1.78%, and LSTM autoencoder by 1.13% based on MAPE, MSE, MAE, and RMSE metrics.

关键词： Measurement Cloud computing Web services Autoencoders Predictive models Virtual machines Performance analysis Resource management Servers Long short term memory

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Adaptive Weight-based Dollmaker Optimization Algorithm for Energy Efficient clustering and routing in Wireless Sensor Network

Adaptive Weight-based Dollmaker Optimization Algorithm for E...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Narasimhulu, N. Almusawi, Muntather Kumar, Gotte Ranjith Roy, Upendra B.P. Rajesh, N. Gooty India The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Channabasaveshwara Institute of Technology Department of Electronics and Communication Engineering Tumkur Gubbi India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350360660

Wireless sensor network (WSN) is a self-optimizing network composed of many small sensor nodes used to monitor a wide range of applications. The main objective of this clustering and routing is to obtain high network lifetime with less energy usage. Therefore, this research proposes an Adaptive Weight-based Dollmaker Optimization Algorithm (AWDOA) for Cluster Head (CH) and route selection in Internet of Things (IoT) based Wireless Sensor Network (WSN). The AWDOA enhances both efficiency and precision of CH and route thereby enhancing overall performance. It enables to dynamically adopts exploration and exploitation by enabling search space in complex solutions. Its flexibility enhances convergence speed and avoid local optima issues thereby ensuring optimal network lifetime. The residual energy, node degree, communication cost and distance are measured as fitness function for AWDOA. The Energy Consumption (EC), Network Lifetime (NL) and Packet Delivery Ratio (PDR) metrics are considered to show effectiveness of AWDOA. The AWDOA achieves 31.63mJ of EC, 6850rounds of NL and 99.86% of PDR for 200 no. of nodes. © 2024 IEEE.

关键词： Energy utilization

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