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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

Recent Advances in Electrical, Electronics, Ubiquitous communication, and Computational Intelligence (RAEEUCCI), International Conference on

作者： T Thamaraimanalan C Venkatesan M Ramkumar A Sivaramakrishnan M Marimuthu Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Business System Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra pradesh India Department of Computer Science and Engineering Sona College of Technology Salem India

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection.

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Improved Power Transfer Efficiency Using a Dual Transmitter Receiver Design With Integrated Decoupling Coils for IPT EV Charging

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IEEE Access 2025年 13卷 91362-91373页

作者： Bilal Alam Saad Mekhilef Marizan Mubin Kok Soon Tey Addy Wahyudie Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory University of Malaya Kuala Lumpur Malaysia School of Engineering Swinburne University of Technology Melbourne VIC Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Electrical and Communication Engineering United Arab Emirates University (UAE-U) Al Ain United Arab Emirates

In high-power inductive power transfer (IPT) systems, multiple coils are used to enhance efficiency, but interactions between coils on the same side can reduce overall performance. Traditionally, decoupling coils are added to minimize these interactions, however they don’t contribute to power transfer and may even hinder system efficiency. This article introduces a new IPT system and coil structure that integrates decoupling coils, which not only improve mutual interaction between same-side coils but also actively contribute to power transfer. The coils are optimized for high mutual inductance, strong coupling coefficient, reduced self-inductance, and minimized side-by-side mutual inductance, performing well even with X-axis, Y-axis, and angular misalignments. Using 3-D Finite Element Analysis (ANSYS-Maxwell), the magnetic coupler’s performance is thoroughly evaluated, and a downsized experimental setup demonstrates the system’s feasibility. The proposed system achieves a maximum dc-dc efficiency of 94.27%, successfully delivering 1 kW across a 120-mm air gap with zero-voltage switching, presenting a significant advancement for multi-transmitter and multi-receiver systems.

关键词： Coils Receivers Transmitters Mathematical models Couplings Inductance Costs Voltage Rectifiers Integrated circuit modeling

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A Novel Single Shot Multibox Detector based Face Mask Detection

A Novel Single Shot Multibox Detector based Face Mask Detect...

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer Department of Computer Applications Govt. Engineering College Bikaner India

Face mask detection has gained significant importance in recent times due to the COVID-19 pandemic. This research work proposes a face mask detection method using the Single Shot MultiBox Detector (SSD) framework. The proposed method aims to accurately identify whether individuals are wearing masks or not in real-time scenarios. To begin with, a dataset comprising images of masked and unmasked faces is collected from kaggle and annotated with corresponding labels. The SSD model is then trained on this dataset, utilizing its ability to simultaneously perform object localization and classification. By leveraging the pre-trained weights and the architecture of SSD, the model is able to detect faces and classify them as either wearing masks or not. During the inference phase, the trained SSD model is applied to input images or video frames. The model analyzes the detected faces and assigns a label based on the presence or absence of masks. The output includes bounding box coordinates around the detected faces, along with the corresponding mask or non-mask label. Experimental evaluations are conducted on various real-world datasets to assess the performance of the proposed method. The results demonstrate the effectiveness and efficiency of the approach, achieving high accuracy in face mask detection.

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Unveiling Feature Significance: Enhancing Classification Accuracy using Chi-Squared Weighted Feature Selection

Unveiling Feature Significance: Enhancing Classification Acc...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Deepsh Rajput Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer Department of Computer Applications Govt. Engineering College Bikaner India

This study delves into the realm of feature selection and its profound impact on enhancing classification accuracy. Leveraging the renowned Fisher's Iris dataset as a well-established benchmark, our investigation revolves around the application of Chi-squared weights to identify attributes crucial for species classification. The Chi-squared weights are a powerful tool, quantifying the associations between features and species labels. Through this approach, we discern the most pivotal attribute, suggesting its pivotal role in distinguishing iris flower species. By selecting features with elevated Chi-squared weights, we curate a focused feature subset that significantly bolsters classification *** visualization of Chi-squared weights through intuitive bar graphs emerges as an influential tool for gauging feature importance. This graphical representation aids in identifying attributes that possess strong discriminatory capabilities, thereby empowering informed decision-making during feature selection. The study emphasizes the imperative role of feature selection in the domain of machine learning and classification tasks. By incorporating statistical methodologies like the Chi-squared test, we unravel the essence of attribute significance and streamline the process of crafting potent feature subsets. As datasets evolve in complexity, the identification of pivotal attributes becomes indispensable. The proposed methodology provides a valuable approach to address this *** essence, the study advances feature selection techniques by showcasing the potency of Chi-squared weights and their visual representation in enhancing classification outcomes. Our exploration within the Fisher's Iris dataset manifests the broader potential of informed feature selection strategies in elevating the performance of machine learning models.

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G

A new symbol timing and carrier frequency offset estimation ...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Gunasekaran Thangavel Sheeba Rajan S. Subbulekshmi Arun Anthonisamy Rohini Ganapathi Gowri Velankanni Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Sultanate of Oman School of Computing SRM Institute of Science and Technology Tiruchirapalli India Department of Electronics and Communication Engineering Prathyusha Engineering College Chennai India Department of Computer Science and Business System Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering St. Joseph’s Institute of Technology Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions.

关键词： Wireless communication Machine learning algorithms Heuristic algorithms Estimation Symbols Interference Prediction algorithms Frequency estimation Timing Reliability

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Retraction Note: Enhancing low latency and high performance in clustered-based wireless sensor network routing using genetic algorithm for 5G networks

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Banu, Firdouse Udhayamoorthi, M. Ramana Gupta Nallagattla, Venkata Phaneendra Maguluri, Lakshmana Mahesh Kumar, N. B. Jayachitra, S. Department of Applied Information System Applied College King Khalid University Khamis Mushaiyt Saudi Arabia Department of Computer Science and Design Karpagam College of Engineering (Autonomous) Coimbatore India Department of CSE Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

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Fingerprint Image Enhancement: Multi-Filter Approaches and Stage Timing Evaluation

Fingerprint Image Enhancement: Multi-Filter Approaches and S...

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Sustainable Expert systems (ICSES), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Ankur Goswami Kapil Chauhan Suresh Choudhary School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India Aryabhattt College of Engieering and research Centre India MDS University Ajmer India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The performance of fingerprint recognition and verification systems heavily depends on the quality of the input fingerprint images. This paper introduces a multi-filter image enhancement strategy tailored for enhancing low-quality fingerprint images. The objective is to enhance the visibility and definition of ridge patterns, tackling problems stemming from irregular skin contacts and contaminated sensor surfaces. Author incorporates a new normalization technique that adjusts for finger flexibility, improving the correction of anomalies and boosting overall image quality, especially in detecting fine details. Experimental evidence shows that technique surpasses previous methods in effectiveness.

关键词： Image quality Image matching Surface contamination Refining Fingerprint recognition Boosting Skin Timing Security Reliability

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Performance Evaluation of Bit-String Normalisation of Fingerprint Database

Performance Evaluation of Bit-String Normalisation of Finger...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Ankur Goswami Kapil Chauhan Sankarsan Panda School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India Aryabhattt College of Engieering and research Centre India MDS University Ajmer India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This study presents a comprehensive methodology for fingerprint analysis, employing a sequence of advanced image processing techniques including preprocessing, skeletonization, and minutiae extraction, leading to the generation of a digital bit-string representation. By enhancing image quality through histogram equalization and adaptive thresholding, and by refining fingerprint ridge patterns to single pixel widths via skeletonization, the proposed techniques facilitate precise minutiae extraction. These steps significantly improve the accuracy and reliability of biometric identification systems, as demonstrated by the dense and accurate distribution of minutiae points. The resultant digital encoding supports efficient data storage and rapid matching processes, essential for robust biometric verification. This research underscores the potential of these methodologies to enhance the performance of fingerprint-based security systems and lays the groundwork for future integrations with machine learning for automated and scalable biometric solutions.

关键词： Accuracy Image coding Thresholding (Imaging) Scalability Refining Machine learning Fingerprint recognition Biometric identification Robustness Security

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A Novel approach for Data mining Classification using J48DT Classifier for Intrusion Detection system 11

A Novel approach for Data mining Classification using J48DT ...

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11th IEEE International Conference on communication systems and Network Technologies, CSNT 2022

作者： Manimegalai, T. Ravishankar, T. Nadana Kannagi, L. Kannan, K. Anitha, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Data Science and Business System Srm Institute of Science and Technology Chennai Kattankulathur India Department of Computer and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Ece R.M.K. College of Engineering and Technology Puduvoyal Chennai India Department of Ece Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9781665480383

Because of the growing quantity of network traffic that is being transmitted across the network in today's modern era, network attack detection has become a necessity. The technique of data mining is extremely important in the hunt for network assaults and anomalies. These strategies aid in the selection and refinement of meaningful and relevant knowledge from enormous data sets, which is beneficial in many situations. The process of data mining aids in the categorisation of important information for Intrusion Detection systems (IDS). The IDS provides warnings for the network traffic informing it of any foreign intrusions into the network. We show how different data mining methods may be used for intrusion detection to offer a safe network environment in the research article. The J48DT Classifier is utilised for classification in this case. Comparisons based on characteristics such as data accuracy, the discovery of valuable patterns, and the extraction of useful information between approaches are made. © 2022 IEEE.

关键词： Intrusion detection

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Identify the potential pathways and candidate biomarkers of stroke associated with bipolar disorder: Bioinformatics and system biology approach

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Journal of Proteins and Proteomics 2025年第2期16卷 141-159页

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Hasan, Md. Rakibul Uddin, Muhammad Shahin Department of Information and Communication Technology (ICT) Mawlana Bhashani Science and Technology University (MBSTU) Tangail Bangladesh Department of Computing and Information System (CIS) Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Uttara University Dhaka Bangladesh Department of Software Engineering (SWE) Daffodil International University (DIU) Dhaka Bangladesh

The field of computational bioinformatics and systems biology analysis is rapidly expanding due to advancements in bioinformatics tools. Bipolar disorder (BD) is a severe psychiatric condition affecting both adults and adolescents. In recent years, the risk of stroke has increased among individuals with BD. Background studies indicate that BD and stroke share numerous biochemical and genetic characteristics. Due to traditional endocrinological approaches, no collaborative research exists, and no treatment options have been established for BD and stroke patients. The aim of this study is to identify common molecular pathways and potential therapeutic targets in BD and stroke that could be utilized to predict disease progression. To achieve this, shared genes were identified to determine common pathways. Based on the biochemical, molecular, and genetic interactions among these shared genes, this study identifies the most significant hub genes. To further explore these interactions, analysis such as the protein–protein interaction (PPI) network, topological properties, enrichment analysis, co-expression network, gene regulatory network (GRN), and physical interaction network are conducted. The GO annotation indicates that most of the genes are linked to MAP kinase activity and tumor necrosis factor receptor superfamily binding. The KEGG pathway analysis revealed that the predominant genes are associated with apoptosis and the IL-18 signaling pathway. This comparison helps understand the biochemical and genetic characteristics shared between BD and stroke. Predictive drug analysis has identified consistent potential biomarkers associated with both conditions, providing a scientific foundation for investigating their diagnosis, treatment, and prognosis. Finally, chemical experiments could be conducted to further validate the efficacy of these drugs.

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