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An efficient scalar multiplication algorithm on Koblitz curves using τ3-NAF

International Journal of security and Networks 2022年第4期17卷 240-246页

作者： Ganesh, Ponnuru Surya Padmavathy, R. Pinapati, Anil Computer Science and Information Security NIT Warangal India Department of Computer Science and Engineering NIT Warangal India Department of Computer Science and Engineering NIT Calicut India

Elliptic curve cryptography (ECC) is an efficient and widely used public-key cryptosystem. It uses relatively shorter keys compared to conventional cryptosystems hence offering faster computation. The efficiency of ECC relies heavily on the efficiency of scalar multiplication which internally depends on the representation of the scalar value. Based on the representation, the number of point additions and point doublings varies. Koblitz curves are binary elliptic curves defined over F2 and also known as anomalous binary curves. Scalar multiplication algorithms on these curves can be designed without any point doublings. In τ-NAF representation, we need 0.333 m point additions whereas in τ2-NAF it is 0.215 m. This paper proposes a method to improve the efficiency of scalar multiplication on Koblitz curves using τ3-NAF representation that further reduces the point additions to 0.143 m. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Efficiency

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An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

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Frontiers of computer science 2023年第4期17卷 83-96页

作者： Muazzam MAQSOOD Sadaf YASMIN Saira GILLANI Maryam BUKHARI Seungmin RHO Sang-Soo YEO Department of Computer Science COMSATS University IslamabadAttock CampusAttock 43600Pakistan Department of Computer Science Bahria UniversityLahore 54600Pakistan Department of Industrial Security Chung-Ang UniversitySeoul 06974Republic of Korea Department of Computer Engineering Mokwon UniversityDaejeon 35349Republic of Korea

Innovations on the Internet of Everything(IoE)enabled systems are driving a change in the settings where we interact in smart units,recognized globally as smart city ***,intelligent video-surveillance systems are critical to increasing the security of these smart *** precisely,in today’s world of smart video surveillance,person re-identification(Re-ID)has gained increased consideration by *** researchers have designed deep learningbased algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision *** this line of research,we designed an adaptive feature refinementbased deep learning architecture to conduct person *** the proposed architecture,the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel *** addition,the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature ***,the model’s effectiveness is validated on the CUHK01 and CUHK02 *** compared with existing approaches,the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%,respectively.

关键词： Internet of Everything(IoE) visual surveillance systems big data security systems person re-identification(Re-ID) deep learning

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Design and Implementation of Side Channel Attack Based on Deep Learning LSTM

Design and Implementation of Side Channel Attack Based on De...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Center for Cyber Security Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9781665482585

Encryption algorithms and encryption devices both play a key role in ensuring the safety of data that has been encrypted. Various types of attacks, such as energy analysis, can be used to assess the reliability of the encryption devices. Since it was originally introduced, side channel attacks' deep learning-based methodology has drawn plenty of attention. This is one of several different attack strategies. In this paper, a side channel attack method based on the LSTM deep learning network is suggested. The method use Correlation Power Analysis (CPA) to find the relevant information in the side channel power consumption data. The choice of a suitable interest interval to utilize as the feature vector in the creation of the neural network model is then guided by the position of the interest points. The trials' findings show that the LSTM model outperforms both MLP and CNN in terms of how well it executes side channel attacks. © 2023 IEEE.

关键词： Side channel attack

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A Novel Approach for Anomaly Detection using Snort Integrated with Machine Learning 18

A Novel Approach for Anomaly Detection using Snort Integrate...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Preethi, T. Reddy, Ponnuru Rakshitha Likhitha, Lekkala Kumar, Pendyala Pavan Kamani, Abhinav Vnr Vignana Jyothi Institute of Engineering and Technology Data Science and Artificial Intelligence & Data Science Department of Cse - Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

In today's digital world, it is crucial to keep a company's information safe from cyber threats. With new and more sophisticated network attacks emerging all the time, better security measures and ways to monitor our networks are needed. Intrusion Detection Systems (IDS) can act as digital guards to help protect our networks from these malicious threats. Snort is a widely used open-source IDS, but it can struggle with the ever-changing nature of these threats. Our research focuses on enhancing Snort's capabilities by integrating it with ML algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes. We also conducted subset testing using the NSL-KDD dataset. Our results indicate that combining these ML algorithms with Snort can improve our ability to detect and stop malicious network activities. This research can be helpful for anyone interested in developing smarter and more effective intrusion detection systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Decision Tree Intrusion Detection System K-Nearest Neighbours (KNN) Machine Learning Naive Bayes NSL-KDD Dataset Random Forest Snort Support Vector Machine (SVM)

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A Deepfake Image Classifier System for real and Doctored Image Differentiation 7

A Deepfake Image Classifier System for real and Doctored Ima...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thomas, Juanith Mathew Ebenezer, V. Richard, Rohan Paul Karunya Institute of Technology And Sciences Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Sciences Division of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350359299

The sudden proliferation of deepfake technology has raised concerns about the authenticity and integrity of digital media. In response to these concerns, this paper presents a Deepfake image classifier system designed to differentiate between real and doctored images using "MesoNet"architecture which utilizes convolutional layers and builds a suitable neural network for classification. Making efficient use of Deep Learning strategies, this system aims to detect subtle manipulations indicative of deepfake alterations. The proposed model is trained on two distinct datasets: Dataset I comprises authentic images while Dataset II consists of images subjected to various levels of manipulation, making them deepfakes. Through extensive training using these images and their labels, we'll train the neural network to adequately predict the authenticity of an image. The model has been trained on exactly 7104 images and judging from the results, the predictions made by the model are fairly accurate. Results yield an accuracy of 88.81% and a precision of 87.93%. © 2024 IEEE.

关键词： Digital storage

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Knee Osteoarthritis Detection Using X-Rays and DNN 3

Knee Osteoarthritis Detection Using X-Rays and DNN

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sinha, Vaishnawi Veemaraj, Ebenezer Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions worldwide, leading to pain, disability, and reduced quality of life. Early detection and intervention are crucial for effective management and prevention of disease progression. In this paper, we propose a novel approach for knee OA detection using X-ray images and deep neural networks (DNNs). Specifically, we investigate the performance of two popular DNN architectures: EfficientNet-B5 and DenseNet121. We begin by pre-processing a dataset of knee X-ray images to extract relevant features and mitigate noise. Subsequently, we fine-tune the pre-trained EfficientNet-B5 and DenseNet121 models using transfer learning techniques to adapt them to the task of knee OA detection. We then evaluate and compare the performance of these models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our experimental results demonstrate the efficacy of both EfficientNet-B5 and DenseNet121 architectures in knee OA detection, outperforming traditional machine learning approaches. However, we observe variations in their performance metrics, indicating nuanced differences in their ability to capture and discriminate pathological features in knee X-ray images. Furthermore, we conduct an in-depth analysis to understand the factors contributing to the performance disparities between the two models. Overall, our study contributes to the growing body of research aimed at leveraging DNNs for medical image analysis tasks, particularly in the domain of musculoskeletal disorders. The insights gained from this research can potentially aid clinicians in early diagnosis and personalized treatment planning for knee OA patients, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Grading

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Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

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Machine Learning for Detecting Subtle Signs of EyeDisease 4

Machine Learning for Detecting Subtle Signs of EyeDisease

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Dafny Neola, J. Ebenezer, V. Edwin, E.Bijolin Kirubakaran, S.Stewart Thanka, M.Roshni Yona Davincy, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350385793

Eye disorders can have serious repercussions, such as blindness or visual impairment, if they are not identified or addressed, Treatment that works and preventing permanent harm depend on early diagnosis and prediction. Using cutting-edge clinical observations, this method looks for diseases such diabetic retinopathy, cataracts, myopia, glaucoma, age-related eye illnesses, and hypertension-related eye disorders. This study presents an innovative method for early detection of eye health disorders: The Eye Health Station. It uses machine learning algorithm, by leveraging EfficientNet's superior performance in image classification tasks, our system analyzes ocular images, such as retinal scans, to identify potential indicators of eye disorders at an early stage., The proposed system seeks to discover possible markers of eye problems by evaluating several factors taken from ocular pictures and patient data, including retinal scans and medical history. The system is able to learn from various datasets and gradually increase its accuracy since machine learning techniques are included into the system. © 2024 IEEE.

关键词： Ophthalmology

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VGG-16 based Deep Learning Approach for Plant Species Detection

VGG-16 based Deep Learning Approach for Plant Species Detect...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Praveen, Bandi Isravel, Deva Priya Malar Dhas, Julia Punitha Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

Automated plant species detection has gained interest due to the potential benefits in agriculture to significantly increase crop yields, decrease the use of chemicals, and promote sustainable agriculture practices. A deep learning model, for plant species identification is a promising approach. This paper presents a condensed version of the VGG-16 deep learning model for identifying plant species. The original VGG-16 model's complexity and computational overhead are minimized by lowering the number of convolutional layers, which lowers the amount of parameters and calculations needed while keeping a high level of accuracy in the identification of various plant species. The streamlined VGG-16 model obtains an improved accuracy rate in categorizing distinct plant species after being trained on a big dataset of plant species that includes a variety of images of different plants. The condensed VGG-16 model offers a viable method for precise and effective plant species identification. This study's methodology and results advances the area of plant species detection by offering a deeper learning model that is more effective and useful and can be used on devices with limited resources. © 2024 IEEE.

关键词： Agriculture

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Shukla, Shubhi Dalui, Subhadeep Alam, Manaar Datta, Shubhajit Mondal, Arijit Mukhopadhyay, Debdeep Chakrabarti, Partha Pratim Centre for Computational and Data Sciences Iit Kharagpur India Iit Kharagpur Computer Science and Engineering Department India Center for Cyber Security New York University Abu Dhabi United Arab Emirates Iit Kharagpur Department of Artificial Intelligence India Iit Patna Computer Science and Engineering Department India

ISBN: (纸本)9798331510831

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL © 2025 IEEE.

关键词： adversarial attack decision-boundary diversity defense ensemble models

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