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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kelin, A. R. Darshika Sudhakar, K. Surekha, Pothireddy Palanivel, R. Al-Jawahry, Hassan M. Karunya Institute of Technology and Sciences Data Science and Cyber Security Karunya Nagar Coimbatore India Department Electronics and Communication Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Telangana Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

In general, high power electronic converters such as Multilevel Inverters (MLI), that find their use in many applications, create difficulties of augmented switch count, capacitor flow current and reverse current flowing from inductive loads. above problems, in turn, result in faults, without which MLI reliability is unattainable. Therefore, fault classification and tolerance strategies are of paramount contribute to keeping reliability of inverters. Herein we present a deep learning (DL)-based fault classification approach for 15-level CHB Multi-level Inverter. proposed system includes pre-processing based on Adaptive Hilbert-Huang filtering, segmentation by a Partial Differential Equation (PDE), feature extraction utilizing Scale-Invariant Feature Transform (SIFT), and fault signal classification being performed by a Convolutional Neural Network (CNN) trained with a Honey Badger Optimization Algorithm (HBOA). MATLAB was applied to our method of identifying a set of four complex fault types for purpose of measurement. results of HBOA-optimized CNN have demonstrated highest accuracy rate of 99.91%. © 2024 IEEE.

关键词： Electric inverters

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User Behaviour based Insider Threat Detection using a Hybrid Learning Approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4573-4593页

作者： Singh, Malvika Mehtre, B.M. Sangeetha, S. Govindaraju, Venu Centre of Excellence in Cyber Security Institute for Development and Research in Banking Technology Established by Reserve bank of India Telangana Hyderabad500057 India Department of Computer Applications National Institute of Technology Trichy Tamil Nadu Tiruchirappalli620015 India Center for Unified Biometrics and Sensors Department of Computer Science and Engineering State University of New York at Buffalo New York BuffaloNY14260 United States

Insider threats constitute a major cause of security breaches in organizations. They are the employees/users of an organization, causing harm by performing any malicious activity. Most of the existing methods to detect insider threats are based on machine and deep learning and have the following limitations: they use predefined rules or stored signatures and fail to detect new or unknown threats;they require explicit feature engineering, which results in more false positives;they require a large amount of training data, and are computationally expensive. In this paper, an improved user behavior-based insider threat detection method is proposed using a hybrid learning approach that overcomes the above limitations. It uses bi-directional long-short-term memory for feature extraction, a feed-forward artificial neural network (using distance measurements) for feature selection, and a support vector machine for classification-normal user or malicious user. The genetic algorithm’s fast global search strategy is used for the support vector machine’s initial kernel selection. Finally, alerts are generated for each user based on their combined anomaly score. The proposed method is tested using the CMU-CERT r4.2 insider threat dataset, and its performance is evaluated using the following parameters: accuracy, precision, recall, f-measure, and area under curve-receiver operating characteristic curve. The results show a significant improvement over the existing methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Support vector machines

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Machine Learning-Based Crop Recommendation System 4

Machine Learning-Based Crop Recommendation System

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ebenezer, V. Nair, Akash Stewart Kirubakaran, S. Praising Linijah, N.L. Rajan, Jerlin Santhiya, P. Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya School of Management Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331529635

The time-honored way that farmers have been used to apply to the identification of the best crops for better yield is not exhaustive. Under this technique, the management process of powerful and productive resources, cannot be done properly. The crop recommendation system comprising a machine learningbased model can be deployed to solve the problem. While using techniques such as Logistic Regression, Decision Trees, Random Forests, XGB boost, SVM and Stacking Classifier, the machine learning model attempts to predict the most suitable crops by taking and observing soil contents and environmental parameters. That study is devoted to helping farmers have comprehensive prescriptions and appropriate crop selections to optimize productivity and guarantee sustainability. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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Temporal Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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A Review of Privacy-preserving and Efficient Data Collection and Aggregation in Smart Grids 11

A Review of Privacy-preserving and Efficient Data Collection...

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11th International Conference on Information and Communication technology, ICoICT 2023

作者： Donnelly, William Keifer, Paulina Minor, Robert Muthukumaran, Uthra Parolek, Bryan Tuck, Brandon Ibrahem, Mohamed I. Fouda, Mostafa M. George Mason University Dept of Cyber Security Engineering FairfaxVA United States Augusta University School of Computer and Cyber Sciences AugustaGA United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798350321982

The smart grid fuses the traditional electrical power grid with smart technology to allow for a more distributed, bidirectional flow of communication, data, and electricity. Its advanced metering infrastructure (AMI) of smart meters (SMs) and sensors can efficiently collect data from end users and send it to utility companies. It also allows for near-instantaneous detection of problems in the grid so there can be a quick recourse to fix them. The strength of the SMs is that they can provide reports showing electrical usage to both consumers and utility companies, allowing users to act accordingly to decrease their bill while allowing utility companies to accurately manage billing by reviewing near real-time supply and demand. This paper will explore the specific issues that arise in the smart grid with respect to aggregating consumers' data and sending it to utility companies and will also propose solutions to these issues. The two primary solutions in this paper focus on the use of homomorphic encryption and a lightweight data aggregation scheme, which decrease computational costs, improve scalability, and improve security-all while simultaneously preserving the customer's privacy. Finally, a thorough comparison of the existing works is presented along with future research directions that will serve to further enhance the security levels of future AMI smart metering systems. © 2023 IEEE.

关键词： Advanced metering infrastructures

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Lung Cancer Image Identify and Categorization using Artificial Neural Networks

Lung Cancer Image Identify and Categorization using Artifici...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Lingappa, Ediga Begum, D Jareena Pachouri, Priya Rashmi, Kuderu Varalakshmi, Thokala Tharuna Kumar, Anoop Mallareddy College of Engineering Maisammaguda Dept of Computer Science and Engineering TS Secunderabad500100 India CMR Institute of Technology Dept. of Artificial Intelligence and Data Science Bengaluru560037 India Malla Reddy University Dept of Cyber Security Hyderabad India

ISBN: (纸本)9798350381887

Lung cancer that is made up of small cells is called small cell lung cancer (SCLC). Smoking tobacco in any form-cigarettes, cigars, or pipes-is by far the most significant risk factor for developing lung cancer, though it is only one of many. Cancer of the lungs is known as lung cancer. When cells in the body multiply uncontrollably, cancer develops. If cancer cells enter the lymphatic system or the bloodstream, they can travel to other organs and spread the disease. Cheap and fast cancer diagnostics could save millions. Latestage cancer treatment is hard. Lung cancer is diagnosed non-invasively. Biopsy requires careful ANN architecture selection. This study proposes a diagnosis ANN model. ANN research is 'hot' in cardiology, cancer, and radiology. Medical research uses ANNs. Lung cancer diagnosis was tested. MR images assess lung health for the neural network. MATLAB created ANNs and processed images. Training data was extracted from MR images using Harlick texture features. MATLAB made ANNs. Elman Network exceeds ANNs with 90.30 %. Lung cancer can develop. MRIs detect lung cancer. MRIs show lung development. Many lung tumour detection research use machine learning and deep learning. MRI algorithms swiftly detect lung malignancies, improving patient therapy. Radiologists use these forecasts. The study evaluates a self-defined ANN for lung tumour identification. The radiologist needs time to find and remove the cancer from the MR. technology improves performance and segmentation. Machines always predict and detect. Neural networks outperformed SVM, Random forest, regression, and others. © 2024 IEEE.

关键词： Lung cancer

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Exploring cybersecurity Research Areas, Categories, and Limitations Utilizing an Interdisciplinary Approach 10

Exploring Cybersecurity Research Areas, Categories, and Limi...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Mittal, Vinayak Martolia, Mamta Chhabra, Payal Srivastav, Arun Pratap College of Engineering Department of Cyber Security Roorkee India Krishna Institute of Engineering and Technology Artificial Intelligence and Machine Learning Ghaziabad India Lloyd Institute of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350382471

Rapid technological advances have brought unprecedented levels of connectivity and security, but also new and complex cybersecurity challenges The purpose of this paper is to provide an in-depth insight into current cybersecurity research areas, divided into specific areas and defined boundaries in each Poetry, Cryptography, Law, Psychology, It also integrates ideas from other relevant fields In the face of increasing cyber threats, this paper provides a comprehensive insight into the multifaceted aspects of cybersecurity. computer science, cryptography, law, psychology, research in related fields, applying central topic vision, studying current security research fields, qualifying on the side in human areas, establishing each class, major research boundaries and selection, technology, agricultural line, The conclusions broaden the light of the complex interpersonal contribute to smaller understandings of interdisciplinary cybersecurity, which build emphasizing the need for collaborative and adaptive solutions to meet the changing cyber environment © 2023 IEEE.

关键词： challenges cryptography cybersecurity information security technology threats

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Efficient Convolutional Neural Network Based Side Channel Attacks Based on AES Cryptography 21

Efficient Convolutional Neural Network Based Side Channel At...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Aman, Azana Hafizah Abdulkadir, Rabiu Aliyu Islam, Shayla Farhan, Basim Abdulkareem Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350318821

In recent years, profiled side-channel attacks have emerged as a particularly potent kind of side-channel attack that can circumvent cryptographic equipment's security. Convolutional neural networks (CNNs) have been widely used as deep learning infrastructure for attacks in recent research that has examined a new kind of profiled attack based on deep learning. The design of CNNs will have a significant impact on attack effectiveness. However, image recognition fields are frequently the foundation of the CNN architecture currently used for profiled attacks. Additionally, it is still challenging to choose the proper parameters and CNN infrastructure concerning adaptation to profiled assault types. In the current study, an effective CNN-based profiled attack was suggested that can be used against masking-protected cryptographic devices. The Grey Wolf Optimization (GWO) approach obtains the CNN architecture parameters proposed in this research. The characteristics of intriguing areas on the power trace determine these parameters. The proposed attacks were tested using experiments over a tracing set gathered from an intelligent card Atmega8515 processing the ASCAD public dataset. © 2023 IEEE.

关键词： Side channel attack

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Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

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