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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Devi, Suman Sharma, Yogesh Kumar Athithan, Senthil Sachi, Savya Singh, Ajay Kumar Jain, Arpit Chaudhary Devi Lal University Department of Computer Science and Engineering Haryana Sirsa India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering AP Guntur India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India Lalit Narayan Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India Allen House Institute of Technology India

ISBN: (纸本)9798350304480

computer systems are vulnerable to information theft as a result of the extensive use of the internet, which has caused an increase in security defence mechanisms. In order to overcome these theft various approaches have been proposed like Machine Learning, Swarm Intelligent Algorithm, Bayesian-based algorithms etc. For choosing effective and efficacious features and improve the Security Defense Mechanism. In order to lessen Distributed Denial of Service (DDO) assaults, we suggested a novel hybrid classification approach in this study based on Artificial Bee Colony (ABC) and Whale Optimisation Algorithm (WOA). The results are compared with other algorithms. These algorithms have shown promising results. © 2023 IEEE.

关键词： ABC DDO'S Defense Mechanism WOA

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Integrating Social Platforms with IoT: Security Challenges, Applications, and Trust Issues 5

Integrating Social Platforms with IoT: Security Challenges, ...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Mohanta, Bhabendu Kumar Dehury, Mohan Kumar Tulasi, Desetti Das, Sunayana United Arab Emirates University College of Information Technology Al AinP.O. Box 15551 United Arab Emirates Amity Institute of Information Technology Amity University Jharkhand Ranchi India Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Vaddeswaram Guntur India Driems University Tangi Dept. of Computer Scinece and Engineering Odisha Cuttack754 022 India

ISBN: (纸本)9798350376753

The term Social Internet of Things (Social IoT) refers to an expansion of the term Internet of Things (IoT) that integrates social networking capabilities and concepts into IoT applications. The number of connected devices has already crossed the total world population. With the development of other technologies like Blockchain, Artificial Intelligence, Machine Learning, and Data Science, IoT become more usable in many applications. With the rapid use of digital devices, social media like Facebook and others are part of the human lifestyle. The integration of IoT with a social platform for different services become an emerging research field in recent times. In this paper, the authors investigated the impact of social services in IoT and its corresponding decentralized architecture. The paper also identified the security challenges associated with the Social IoT and trust issues. Finally, the paper addresses some of the emerging technologies which are capable of addressing the security challenges in Social IoT. © 2024 IEEE.

关键词： Decentralization IoT Privacy Secure Communication Security Social IoT Trust Management

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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Examining the Effects of Applying AI to Enhance Big Data Retrieval Efficiency

Examining the Effects of Applying AI to Enhance Big Data Ret...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sharma, Nakul S. Yadav, Dhananjay Shrivastava, Manish Mandal, Sanjeev Kumar Ravivarman, G. Jana, D Little Femilin Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India

ISBN: (纸本)9798350329773

This studies paper explores how synthetic Intelligence (AI) technologies may be used to improve the efficiency of information retrieval from huge databases, as applied in a diffusion of industries. The paper will discuss the concept of AI as a tool to reduce information complexity, at the same time as inspecting current studies projects inside the subject and unique programs for the enterprise and patron worlds. It'll then discover the demanding situations that get up from applying AI and massive records technology, and description a suggested framework for first-rate practices. The paper will recall how AI may be utilized to enhance information seek outcomes in real-time, and provide an explanation for the diverse ways wherein this might be carried out. Subsequently, the paper will discuss the capability implications of the usage of AI for large statistics Retrieval efficiency, as well as its destiny applications. © 2023 IEEE.

关键词： Computational complexity

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Fog Restoration in Hazy Images using Deep Transfer Learning

Fog Restoration in Hazy Images using Deep Transfer Learning

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Sharma, Viboo Singh, Pranita Sachi, Savya Jain, Arpit Alam, Md Mohtab GLA University Department of Computer Engineering and Applications U.P. Mathura India Hindustan College of Science and Technology Department of IT U.P. Mathura India Roorkee Institute of Technology Department of Computer Science and Engineering U.K. Roorkee India L N Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Eklavya University Department of Computer Science & Engineering M.P. Damoh India

ISBN: (纸本)9798350394412

Issues like low vision and the distortion of spectral and spatial information in photographs can be brought on by unfavorable atmospheric circumstances including smog, rain, overcast sky, fog, and smoke. This piece looks at images taken in a hazy environment. While de-smogging requires veil and transmission map data, optical imaging systems are limited to capturing smoggy images. To restore blurry photographs, effective optical information prediction is necessary. Research has shown that optical information anticipated by the dark channel prior technique frequently produces subpar images in photographs with areas of higher brightness, noticeable pollution gradients, or textured information. In order to estimate optical information, this study makes use of Deep Transfer Learning (DTL) and Optimized Gradient Prior Prediction (OGPP). We gathered benchmark images of both smoggy and smog-free environments in order to train the DTL model. The OGPP technique uses the smog gradient, which the DTL model is taught to anticipate, to recover pictures free of pollution. A comparison analysis shows that when it comes to recovering hazy photos, the OGPP restoration model performs better than other approaches. © 2024 IEEE.

关键词： Restoration

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an710049 China Air Force Engineering University Air Traffic Control and Navigation College Xi’an710051 China Air Force Engineering University Information and Navigation College Xi’an710043 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Xidian University School of Computer Science and Technology Xi’an710126 China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Air Quality Image Classification using Ensemble Based Transfer Learning Techniques

Air Quality Image Classification using Ensemble Based Transf...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Saradha, S. Kumar, M. Praveen Asha, J. Saranya, R. Santhoshkumar, S.P. Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India PSG Institute of Technology and Applied Research Neelambur Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9781839539848

In recent years, research has focused on the efficacy of neural networks, Learning by Transfer (TL), and Ensemble Learning (EL) techniques in image processing. Ensemble approaches, when employed on image classification tasks improve the overall predictive performance and robustness. This work examines the studies related to ensemble approach and applies the same for the classification of images based on different air pollution levels. The proposed research work uses three Convolution Neural Networks (CNN) models namely VGG, MobileNet, and EfficientNet and ensembles them using stacking technique for air quality image classification. The results exhibited in the suggested model generated an overall classification accuracy of 95%. © The Institution of engineering & technology 2023.

关键词： Image classification

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Hate Speech Detection Using the GPT-2 and Natural Language Processing 1

Hate Speech Detection Using the GPT-2 and Natural Language P...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Sachi, Savya Singh, Ajay Kumar Jain, Arpit Devi, Suman Sharma, Yogesh Kumar Athithan, Senthil Lalit Narayan Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur842001 India Allenhouse Institute of Technology Uttar Pradesh Kanpur India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. Guntur India Chaudhary Devi Lal University Department of Computer Science and Engineering Haryana Sirsa India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering AP Guntur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering A.P. Vaddeswaram India

ISBN: (纸本)9798350335569

Hate speech on social media has increased as people use it to communicate and express their thoughts. Automatic hate speech detection has been studied in several languages. This study investigates the conclusion of the performance evaluation of the machine learning model GPT-2 (Generative Pre-trained Transformation 2) for natural language processing. The proposed model is developed and evaluated using a hate speech dataset. We attained an accuracy of about 97.12 percent, higher than the existing models called biLSTM, CNN, CNN-LSTM, and BERT. © 2023 IEEE.

关键词： Speech recognition

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Confidence-Guided Feature Alignment for Cloth-Changing Person Re-identification 27th

Confidence-Guided Feature Alignment for Cloth-Changing Pers...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Sirong Zheng, Huicheng School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783031781094

Much progress has been made in the field of person re-identification, but changes in clothing have hindered the practical application of long-term person re-identification. Cloth-changing person re-identification (CC-ReID) aims to address this problem, with the main challenge being the extraction of discriminative features unrelated to clothing. Existing methods, which mainly focus on introducing clothing-irrelevant cues such as key points, contours, and 3D shapes, require additional modules for feature extraction, resulting in increased complexity and potential inaccuracies due to dependence on the performance of external models. Few studies directly use the original RGB images to make the model constantly focus on clothing-independent information. In this paper, we propose a Confidence-Guided Feature Alignment Network (CGFA) for CC-ReID. Specifically, we design a confidence module that automatically learns to make confidence adjustments to fine-grained information, prompting the model to mine clothing-independent discriminative features without introducing other modal cues. By transferring knowledge, we encourage the model to learn discriminative identity features that are independent of clothing bias. As a result, the confidence module can be removed during the inference phase. The proposed simple but efficient method uses only RGB modality without additional cues, and can serve as a powerful baseline for CC-ReID to drive future research. Extensive experiments on the CC-ReID datasets demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature extraction

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Investigating the Effectiveness of Birthday Attack Strategies in Cryptography Network Security 3

Investigating the Effectiveness of Birthday Attack Strategie...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Chavan, Gurunath T. Agrawal, Shalu Sakhare, Nitin N. Mondal, Dipannita Vigenesh, M. Vijayalakshmi, G. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Mangalayatan University Department of Management Aligarh India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Dr. D.Y.Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350319125

Cryptography is a crucial factor of community safety because it involves growing and decoding mystery codes to guard touchy statistics from unauthorized right of entry. As the era advances, community protection professionals must continuously evaluate the effectiveness of numerous cryptographic techniques and techniques. One such method is the birthday attack, a form of brute-pressure attack that takes benefit of the birthday paradox - the chance that in a group of people, two people will proportion an equal birthday. In cryptography, this attack is used to discover collisions in a hashing function, wherein different enter messages produce identical output. This have a look at examines the effectiveness of various birthday attack strategies in a cryptography community protection context. specially, the studies observes the numerous algorithms and scenarios concerning the use of birthday attack methods and how they're being utilized in network safety structures. moreover, the have a look at strives to apprehend how the boom within the sophistication of the cryptography networks has brought about an boom in the prevalence and effectiveness of the birthday attack. The research seeks to cope with the diverse problems and challenges faced by using cryptography networks using the birthday attack approach, inclusive of reducing the reliability of certain factors of the cryptography network and exposing the ability of introducing vulnerabilities into the device. The examine additionally examines the usage of multi-layered birthday attack strategies, which include utilising a couple of algorithms and using a mixture of various methods, as well as exploring the capacity of any new threat vectors which may be used. in the end, the studies will explore whether or not there are any first-rate practices or methods which may be used to lessen the chance associated with any a hit birthday assault tries. The consequences of this take a look at could be used to gain a deeper knowledg

关键词： attack equal produce professionals proportion

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