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

作者： Mercy, J. Sheela Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350373110

Palm recognition systems play an important role in biometric authentication;however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Prinson, P.T. Geetha, Angelina Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314946

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions © 2023 IEEE.

关键词： Swarm intelligence

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Compiling with MultiCores 2

Compiling with MultiCores

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Farhanaaz Sanju, V. REVA University School of Computer Science and Applications Bangalore India REVA University Department of Computer Science and Enggineering Bangalore India

ISBN: (纸本)9798350320923

Technology is evolving continuously at an exponential rate in terms of its performance. Traditional processors just have one core, however their computational capacity has been outgrown. Increasing the single core's clock frequency is one technique to boost the system's performance. A multitude of problems, including excessive power dissipation and CPU overheating, will arise when the frequency is increased. This issue was resolved by adding more cores to the same CPU to increase processing speed. Multicore processors are the term used to describe this architectural breakthrough. Each core functions independently. Due to these modifications, programmers are forced to code in parallel. We must create code that leverages several cores in parallel. The parallel programming paradigm was developed to create such programmes. The transition from sequential to parallel execution in the software development industry has been greatly influenced by parallel processing. We have OpneMP and MPI constructs, which may be utilised with current languages to parallelize the code. © 2023 IEEE.

关键词： Memory architecture

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Navigating Anxiety in a Cloud E-learning Virtual Environments: The Moderating Role of Anxiety on Lecturers’ Adoption of Cloud Computing E-learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第17期18卷 115-138页

作者： Armah, Elisha D’Archimedes Sathik Ali, I. Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Technology Cape Coast Technical University Cape Coast Ghana Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

The rapid expansion of cloud computing e-learning systems has empowered educators to reach learners worldwide. However, a critical study gap exists: the impact of lecturers’ technological anxiety on their adoption intentions remains unexplored in the technical universities in Ghana. This study addresses this gap by investigating anxiety’s influence on lecturers’ cloud computing e-learning adoption intentions within a virtual environment (VE). We examine the mediating role of competitive pressure and the moderating role of anxiety. Using PLSSEM, we analysed data from surveys conducted among 1395 lecturers across all 10 technical universities. With a high response rate of 90.2% (n = 1258), our analysis reveals moderate anxiety among lecturers regarding cloud e-learning adoption. The proposed model explains 21.1% of the variance in adoption intentions. Interestingly, competitive pressure on lecturers mediates the relationship between cloud e-learning usefulness, security, and adoption decisions. Additionally, anxiety moderates the effects of cloud computing complexity, security, and adoption intentions. We have recommended organizing regular training workshops, ensuring user-friendly platforms, implementing robust security measures, and providing psychological support for lecturers using cloud computing technologies. © 2024 by the authors of this article.

关键词： Virtual environments

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Moving Target Defense - Challenges, Problems and Suitability for Internet of Things 4

Moving Target Defense - Challenges, Problems and Suitability...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Gurung, Dipendra Gurung, Sandeep Pradhan, Mohan Pratap Sikkim Manipal Institute of Technology Department of Computer Applications Majitar India Sikkim Manipal Institute of Technology Department of Computer Science and Engineering Majitar India Sikkim University Department of Computer Applications Gangtok India

ISBN: (纸本)9798350323771

Traditionally cyber attackers have always had an upper hand over defenders as the latter can only react in the event of an attack. Moving Target Defense (MTD) has emerged as a potential game changer by reversing the advantage from attackers to defenders. Together with traditional defenses, MTD is expected to take cyber security up a notch. MTD solutions have been proposed for various levels, namely operating system, network, runtime platform, etc. However, improvement of existing solutions and development of new solutions are required. This paper presents an overview of MTD, the various MTD approaches, attacks mitigated, open research problems in MTD and MTD for the Internet of Things (IoT). © 2023 IEEE.

关键词： Internet of things

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A Hybrid Deep Learning Model with Consensus-Based Feature Selection for DDoS Attacks Detection in SDN 4

A Hybrid Deep Learning Model with Consensus-Based Feature Se...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kachavimath, Amit V. Narayan, D.G. Department of Master of Computer Applications KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Software-Defined Networking (SDN) increases network flexibility by decoupling network control from hardware. However, this also makes networks more vulnerable to Distributed Denial of Service (DDoS) attacks, that can severely disrupt operations. Existing detection methods typically focus on specific DDoS attacks, highlighting the necessity for more comprehensive detection strategies. Our proposed methodology presents a resilient technique for detecting DDoS attacks by employing a hybrid deep learning approach. Utilizing the InSDN dataset tailored for SDN environments, we employ an advanced feature selection process based on a consensus approach to identify the best eight features, enhancing detection accuracy and efficiency. Cross entropy is utilized at the control plane to detect anomalous activity by computing the entropy between source IP (Internet Protocol) and destination IP data to identify DDoS attacks. Our proposed model integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) networks, achieving a detection accuracy of 99.9%. The CNN provides efficient spatial feature extraction, while LSTM captures temporal dependencies, enhancing the model's capability to detect complex attack patterns. We also implement a comprehensive evaluation framework, including metrics such as model loss, model accuracy, and confusion matrix. © 2025 The Authors. Published by Elsevier B.V.

关键词： Feature Selection

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Endometriosis Labelling using Machine learning 4

Endometriosis Labelling using Machine learning

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4th International Conference on Communication, Computing and Industry 6.0, C216 2023

作者： Visalaxi, S. Sudalaimuthu, T. Sowmya, V.J. Department of Computer Science Hindustan Institute of Technology and Science Chennai India Department of Master of Computer Applications Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9798350327328

Endometriosis is a disease affecting the women's reproductive system. The lesion-like structure that exists in the women's reproductive organ creates an impact on their fertility. The diagnostic approach of endometriosis was performed by a radiologist using scanning procedures. Those procedures predict the occurrence of endometriosis but not the severity of endometriosis. Amidst the radiologist, machine learning techniques play a predominant role to identify the severity of endometriosis. Among all machine learning techniques, the proposed approach uses a support vector machine. Support vector machine is a contemporary technique for predicting clinical-based data. Support vector machine analyzes the influencing factor that incorporates Adnexal mass, Tube blockage, Lesion size, and lesion color for predicting the severity of endometriosis as well as classifying the endometriosis as Ovarian and Deep Infiltrating endometriosis. The execution was performed and trained accuracy obtained was 85%, test accuracy was 84.5% for radius basis function (rbf) kernel and the cross-validation score was 82.5%. Also, the available data was trained using Random Forest and Linear Regression. Among all three models, the Support vector machine outperforms well with hyper parameter as rbf for the given data to classify the endometriosis and identify the severity of endometriosis. © 2023 IEEE.

关键词： Support vector machines

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Field Programmable Gate Array(FPGA): An Innovation in Hardware Technology 2

Field Programmable Gate Array(FPGA): An Innovation in Hardwa...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Farhanaaz Sanju, V. REVA University School of Computer Science and Applications Bangalore India REVA University Department of Computer Science and Enggineering Bangalore India

ISBN: (纸本)9798350320923

FPGA is playing a significant role in the field of computer architecture and design. Other peer technologies of FPGA are embedded microprocessor and microcontroller. In this paper we are comparing the technologies which adapts to network-on-chip architecture. For embedded microprocessor and microcontroller, the hardware is already designed and we write the software instruction by exploiting the hardware. The logic or program developed for the same is purely software. To fetch each instruction, to execute, and to access data from external sources time taken is usually high. The nature of execution is sequential. But in FPGA we write the code to implement the hardware itself using HDL(Hardware Description language). HDL directly implements logic functions and interconnections. Design can be programmed and tested immediately even before fabricating on the chip. The nature of execution in HDL is highly parallel. © 2023 IEEE.

关键词： Microcontrollers

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An Efficient DDoS Attack Detection in SDN using Multi-Feature Selection and Ensemble Learning 4

An Efficient DDoS Attack Detection in SDN using Multi-Featur...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

Software-defined networking (SDN) has significantly enhanced network agility by decoupling network control from hardware devices. This architectural shift exposes SDN infrastructures to increased risks from Distributed Denial of Service (DDoS) attacks, which can severely disrupt network services and render them unavailable to legitimate users. Despite the existence of various techniques for detecting specific types of DDoS attacks, there remains a need for more comprehensive work capable of accurately identifying multiple attack categories. We have addressed the gap by proposing a robust DDoS attack detection method in the SDN. We utilize advanced machine learning (ML) algorithms to analyze the InSDN dataset, employing advanced feature extraction techniques to improve detection accuracy and efficiency. Three feature extraction methods, Se-lectKBest, ANOVA F-value scores, and feature importance scores from RandomForest Classifier, were used to select the best ten features from 81. This feature selection, combined with ensemble learning, achieved an accuracy of 99.9%. The Receiver Operating Characteristic (ROC) curve, confusion matrix, and K-fold cross-validation were used to evaluate the performance of the proposed model. © 2025 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Focus-Shifting Attack: An Adversarial Attack That Retains Saliency Map Information and Manipulates Model Explanations

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IEEE Transactions on Reliability 2024年第2期73卷 808-819页

作者： Huang, Qi-Xian Chiang, Lin-Kuan Chiu, Min-Yi Sun, Hung-Min National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

With the increased use of deep learning in many fields, a question has been raised: 'How much should we trust the results generated by deep learning models?' Thus, there has been much research into the interpretations of model results, in order to open the black box of deep learning. The focus is more on interpretation than prediction in some fields such as medicine. Adversarial attacks are the most direct threats to deep learning models. They can add undetectable perturbations to the data to make the models give incorrect results, and model explanations are also susceptible to attacks. This leads to a loss of trust in explanations provided by the models, limiting the application and commercial value of deep learning. This research proposes a targeted adversarial attack algorithm that manipulates the interpretation of the model. Unlike other adversarial attacks on model interpretation, focus-shifting attack (FS Attack) can preserve the numerical depth of the original saliency map without specifying a perturbation budget. Experiments have shown that the FS Attack has a higher degree of image similarity and misleading interpretation than other adversarial attacks, and the property of preserving the numerical depth of the original saliency map makes it more difficult to detect. This study uses several common explanation methods as experimental subjects to investigate how these explanations can be manipulated and evaluate the effectiveness of the attack under different conditions. Under a particular interpretation, the FS Attack has a highly successful attack rate of 94.6%, which is a critical adversarial attack. © 1963-2012 IEEE.

关键词： Budget control

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