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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Ali, S. Ahamed Sujatha, D. Michael, Raj.T.F. Ramesh, G. Agoramoorthy, Moorthy Computer Science and Engineering Easwari Engineering College Ramapuram India St. Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai India Simats Department of Computer Applications Sclas TN Chennai India K.L.N. College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369960

The Internet of Things (IoT) gadgets have completely transformed a number of different businesses by producing enormous amounts of data. A machine learning framework for real-time Internet of Things anomaly detection and self-repair is presented in this research for the first time. Incorporating data preparation, anomaly detection, and selfrepair are all components of the framework. Through the use of data preparation module normalization and dimensionality reduction, Internet of Things data analysis can be improved. Through the utilization of deep neural networks, support vector machines, and several other machine learning models that have been trained on previous data, abnormal behavior can be identified. The reliability of ensemble approaches is increased. The self-repair module is responsible for automatically repairing abnormalities by resetting the settings of the device, upgrading the firmware, and applying patches. Device disruptions are reduced to a minimum by validation. Within the context of smart home and industrial automation tests, events were identified with a high degree of precision and a low percentage of false positives. Because it automatically fixes problems, the self-repair module makes the Internet of Things ecosystem better. © 2023 IEEE.

关键词： Anomaly Detection Internet of Things (IoT) Machine Learning Self-Repair

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Control Plane TCP Attacks Detection and Prevention in SDN Networks using Deep Learning Model 10

Control Plane TCP Attacks Detection and Prevention in SDN Ne...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Anand Kumar, M. Ghanshala, Anshul Purohit, Kamelsh Chandra Singh, Anuj Ezhilarasi, M. Department of Computer Applications Dehradun India The Chinese University of Hong Kong Department of Computer Science Hong Kong Dehradun India Sri Ramakrishna Engineering College Department of Eee Coimbatore India

ISBN: (纸本)9789380544472

The separation of the data and control planes and an integrated software-based administration strategy grounded on a central controller are two considerations put forth by the software-defined networking (SDN) paradigm to make it easier to deploy new applications and services. Those architectural concepts set the stage for a software-controlled network that is more flexible, efficient, and dynamic. With SDN networks, the network can be managed by software that uses minimal network hardware components and enables easy interconnection and installation. It provides the easiest mechanism for network administrators to deploy modern applications and security services. As a result, most network services will be more adaptable, programmable, and unrestricted by platforms. However, there are several difficulties and security-related problems with these centralized and programmable approaches. Security problems in the high- speed network are growing as network traffic grows as well. However, there are a variety of security weaknesses in the SDN architecture's components that might be used by intruders to execute malicious deeds and disrupt the infrastructure and its operations. This research proposes a deep learning-based attack classification method to classify the malicious and legitimate hosts and to block unwanted traffic in the SDN networks. This work mainly focuses on the detection and prevention of TCP-based assaults of the control plane in the SDN networks. The experimental results show better classification accuracy with minimum overhead in terms of efficiency. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Controllers

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Word Embedding for Bengali Language using Domain-related Corpus 6

Word Embedding for Bengali Language using Domain-related Cor...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Bandyopadhyay, Ashutosh Nair, Jayashree Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798350398496

Many natural language processing (NLP) tasks, including machine translation, document classification, information retrieval, news category classification, document clustering, news category clustering, and question-answering systems require the use of distributional word vector representation which is a very low dimensional vector representation of words that is called word embedding, such as contextual and non-contextual embedding. In addition to several contextual word-embedding approaches like BERT, IndicBERT, and SahojBERT (the Bengali equivalent of BERT), this work also covers numerous embedding strategies (such as Word2Vec, GloVe, and FastText) with multiple hyper-parameters. Of late, word embeddings from general corpus such as Wikipedia dump, and common crawl corpus are very famous to make word vectors. In the case of Indian languages also such type of corpus is used to make word vectors. But it suffers in giving good accuracy in general NLP tasks like Sentiment Analysis, News Category Classification, and News Category Clustering, especially for the Bengali Language. This research demonstrates, most importantly, that word embedding from a domain-related corpus enhances the quality of the embedding as those embeddings are working far better than general pre-trained word embeddings which can be used in various NLP tasks like News Category Classification, Word Similarity, and Document Clustering like News Category Clustering using various clustering techniques like KMeans, KMedoid algorithms and also obtaining better accuracy. This work proves that making word vectors or word embedding from a domain-related corpus for a particular NLP task derives better results alongside the quality of those embeddings. Throughout this work, a Deep Neural Network model was used. And the same model was used in all the tests and findings in this work. This work clearly states if word embedding can be derived from a corpus that is related to the domain on which NLP tasks will be p

关键词： Classification (of information)

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Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 174-178页

作者： Gupta, Rishabh Saxena, Deepika Gupta, Ishu Makkar, Aaisha Singh, Ashutosh Kumar National Institute of Technology Department of Computer Applications Kurukshetra136119 India National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan University of Derby Department of Computer Science and Engineering DerbyDE22 1GB United Kingdom

This letter proposes a novel malicious user prediction model based on quantum machine learning that estimates the vicious entity present in the communication system precedently before allocating the data in the distributed environments. The proposed model scrutinizes the behavior of each user and estimates probable data breaches using a developed malicious user predictor unit. The model computes essential scores associated with each user request for the learning process of the prediction unit by generating training samples. The predictor unit exploits the computational and behavioral properties of Qubits and Quantum gates for the accurate prediction of the malicious user with high precision to grant access to non-malicious data requests only. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it significantly improves the security of the system up to 33.28%. © 2019 IEEE.

关键词： Forecasting

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Robust Fingerprint Minutiae Extraction and Matching Using Fully Connected Deep Convolutional Neural Network and Improved SIFT 2

Robust Fingerprint Minutiae Extraction and Matching Using Fu...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Husain, Saif O. Reddy, R.Archana Pareek, Piyush Kumar Jagannathan, Srinivasan Jyothi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science and Engineering Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Aiml & Ipr Cell Bengaluru India Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India G.Narayanamma Institute of Technology and Science Department of Information Technology Hyderabad India

ISBN: (纸本)9798350373110

Fingerprint is a most well-known biometric based authentication system that gives a unique identity for each person. In this paper, an authentication framework to enhance the Fingerprint Minutiae Extraction and Matching (FMEM) technique is proposed. A Fully Connected Deep Convolutional Neural Network with Improved Scale-Invariant Feature Transform (FCDCNN-ISIFT) is proposed for feature extraction and feature matching. Initially, the fingerprint images are collected from Fingerprint Verification Competition 2004 (FVC2004) which are first pre-processed using Histogram Equalization (HE) and Normalization. The images are segmented and enhanced by estimating ridge orientation and ridge frequency, and performing Gabor filtering, binarization and thinning. The fingerprint feature extraction and matching test is done using FCDCNN-ISIFT. The FCDCNN is built for detecting minutiae and the ISIFT is applied to the high contrast images to enhance the quality of latent fingerprints. Finally, the loss function is measured to determine the minutiae detection loss. The experimental results show better accuracy of 99.41%, f1-score of 97.73%, and Equal Error Rate (EER) of 1.75% when compared to results of existing approaches like Contrast Limited Adaptive Histogram Equalization - Random Sample Consensus (CLAHE - RANSAC) and Deep Convolutional Neural Network (DCNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning based model to Detect Anomaly in the Water

Machine Learning based model to Detect Anomaly in the Water

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Kamred Udham Singh, Teekam Kumar, Ankit Gla University Department of Computer Engineering & Applications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

The environmental impact of technology is only one area where the field is expanding rapidly without necessarily good results. [1] Water contamination is one such critical problem. Because water is essential to human survival, polluting it has serious consequences for civilization. [1] Knowing what harmful substances are in the water and being able to identify them is a crucial step toward reducing the damage they cause. While Image classification and sparse coding have been widely employed for water pollution identification [1,] they have unintended implications when applied to bacterial information detection. The current technologies used to identify contaminants in photos and water pipes, such as Convolutional neural networks (CNN), natural language processing (NLP), and the Internet of Things (IoT), are expensive and inefficient when compared to 3D microscopy data. It is a model that uses categorization and data visualization to estimate the total number of bacteria in a given environment. Accuracy scores may be seen in the model's studied graphs, decision tree, confusion matrix, and classification report. By the use of these data sets, the crucial factors for clean water were evaluated, and a model was built that can categorize the massive amounts of information, find meaningful patterns, and make reliable predictions. It has been determined which method yields the highest accuracy prediction (87%) by thoroughly analyzing the available models' accuracy scores. This study thus provides a valid evaluation of bacterial water vs unadulterated water. © 2023 IEEE.

关键词： Decision trees

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Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

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computer Systems science & engineering 2023年第1期44卷 859-872页

作者： Fawaz Alassery Ahmed Alzahrani Asif Irshad Khan Kanika Sharma Masood Ahmad Raees Ahmad Khan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Applications Mangalmay Institute of Engineering&TechnologyGreater Noida201310Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

In recent years,it has been observed that the disclosure of information increases the risk of *** restricting the accessibility of information,providing security is ***,there is a demand for time tofill the gap between security and accessibility of *** fact,security tools should be usable for improving the security as well as the accessibility of *** security and accessibility are not directly influenced,some of their factors are indirectly influenced by each *** play an important role in bridging the gap between security and *** this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and *** significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle *** calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare *** study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

关键词： Information security big data big data security life cycle fuzzy AHP

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Hybrid Fault Prediction and Recovery Framework for VANETs using AI and Federated IoT 7

Hybrid Fault Prediction and Recovery Framework for VANETs us...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Darney, P. Ebby Vallileka, N. Manoj, Sumitha Fernando, A. Vegi Krishnan, R. Santhana Prasath, S. Ram RajaRajeswari College of Engineering Department of Electrical And Electronics Engineering Bangalore India Psna College of Engineering And Technology Department of Computer Applications Dindigul India RajaRajeswari College of Engineering Department of Electronics And Communication Engineering Bangalore India Bangalore Harohalli India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

In contemporary vehicular communication systems, Vehicular Ad Hoc Networks (VANETs) serve a crucial role in enabling seamless data exchange among vehicles and infrastructure components. However, the dynamic and unpredictable nature of vehicular environments renders VANETs susceptible to various faults and disruptions, potentially compromising network performance and jeopardizing safety-critical applications. Therefore, the development of robust fault prediction and recovery mechanisms is imperative to ensure the reliability and resilience of VANETs. This research introduces a novel Hybrid Fault Prediction and Recovery Framework for VANETs utilizing the paradigm of Artificial Intelligence (AI) and Federated Internet of Things (IoT). The proposed algorithm, Deep Neural Network with Federated IoT Model (DNN-FIOT), is presented to enhance fault prediction and recovery efficacy in VANET environments. Extensive simulation analyses comparing DNN-FIOT with existing algorithms are conducted, employing suitable simulation metrics. Results demonstrate the superior performance of DNN-FIOT in terms of fault prediction accuracy, recovery time, and network stability. The proposed framework offers a promising solution for robust fault management in VANETs, ensuring uninterrupted vehicular communication and safety. © 2024 IEEE.

关键词： Internet of things

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Generative Adversarial Networks (GANs) in the Wild: applications, Challenges, and Future Prospects 2

Generative Adversarial Networks (GANs) in the Wild: Applicat...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rakesh Chaudhary, Pooja Gupta, Shilpi Singhal, Manoj Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Meerut Institute of Technology Department of Computer Science & Engineering UP Meerut India Gl Bajaj Institute of Technology & Management Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Generative adverse Networks (GANs) have become increasingly famous for a variety of tasks in picture and audio processing, especially unsupervised getting to know. GANs encompass components, a 'generator' and a 'discriminator,' that are skilled adversarial. The generator is tasked with producing a pattern of statistics, and the discriminator is tasked with figuring out whether the information is 'actual' or 'fake.' The generator 'learns' through trial and mistakes as it attempts to create samples that appear practical to the discriminator. Through the antagonistic schooling manner, the generator can create more accurate 'fake' records while the discriminator can emerge as better at distinguishing between 'actual' and 'faux' records. GANs have been applied to various duties, which include picture era, textual content to photosynthesis, photo-to-image translation, and automatic music composition. Recent tries have been made to apply GANs to extra complicated obligations in the wild, including self-reliant use and drug discovery. GANs have inspired an ever-developing quantity of industries because of their potential to generate sensible facts with minimal human input. © 2024 IEEE.

关键词： antagonistic complicated discriminator photosynthesis potential

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Analysis of Various Image Segmentation Techniques on Retinal OCT Images 3

Analysis of Various Image Segmentation Techniques on Retinal...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sheeba, T.M. Albert Antony Raj, S. Anand, M. Srm Institute of Science and Technology College of Science and Humanities Department of Computer Applications Chennai India Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9781665462167

Image segmentation is one of the most important processes involved in image processing. Segmenting an image is breaking it up into smaller pieces, or segments. These are the areas where it is most helpful because processing the complete image would be inefficient for tasks like object recognition or image compression. Image segmentation is the process of dividing an image's elements for further processing. This article looked at a number of image segmentation techniques, including the threshold approach, edge-based method, and clustering-based method. The segmentation method that is most effective for separating images in OCT images is clustering-based segmentation. To reduce the speckle noise during preprocessing, the wiener filter approach is used. With regards to the threshold, edge-based (Sobel, Canny, Robert's), and clustering-based segmentation techniques, calculate MSE (mean square error) and PSNR (peak signal to noise ratio) values for the segmented image quality. © 2023 IEEE.

关键词： Image segmentation

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