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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Prabakaran, P. Chandra Sekhar Reddy, L. Ravikumar, L.V.D. Verma, Manish Kumar School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Hyderabad Medchal India Shri Vishnu Engineering College for Women Department of Information Technology Bhimavaram India Lovely Professional University Department of Psychology Phagwara India

ISBN: (纸本)9798350330724

Using available channels in a wireless spectrum, cognitive radio (CR) may automatically adjust transmission settings to optimize radio operational behaviour. To function properly, a CR ad hoc network (CRAHN) has to be dynamically capable construct autonomous and decentralized networks without negatively impacting licensed main user (PU) systems. For this reason, an effective spectrum necessitates a system structure based on artificial intelligence. This research provides a model for network planning, learning, and dynamic configuration that is based on a distributed autonomous CRAHN network system that uses reinforcement learning. The proposed optimization techniques for spectrum sensing, ad hoc network design, and context-aware signal categorization are all derived from the system model and are based on machine learning. The cognitive and detection engines may be used to examine the spectrum utilization and neighbour network status in the immediate area. To adapt to the ever-changing nature of the wireless environment, the suggested policy engine may generate network operating policies, identify policy conflicts, and infer the best course of action. Together with the erudition engine, whereby apply the recommended machine-learning methods, the decision engine arrives at the best possible settings for the CRAHN. In addition, guarantee peaceful cohabitation with surrounding systems to have excellent signal context recognition ability. © 2023 IEEE.

关键词： Ad hoc networks

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Securing Distributed Database Using Elongated RSA Algorithm 7

Securing Distributed Database Using Elongated RSA Algorithm

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7th International Conference on advanced computing and Communication Systems, ICACCS 2021

作者： Radhakrishnan, Sangeetha Akila, A. Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Computer Science Chennai India

ISBN: (纸本)9781665405201

Securing data, management of the authorised access of the user and maintaining the privacy of the data are some of the problems relating with the stored data in the database. The security of the data stored is considered as the major concern which is to be managed in a very serious manner as the users are sensitive about their shared data. The user's data can be protected by the process of cryptography which is considered as the conventional method. advanced Encryption Standard (AES), Data Encryption Standard(DES), Two Fish, Rivest Shamir Adleman Algorithm (RSA), Attribute Based Encryption (ABE), Blowfish algorithms are considered as some of the cryptographic algorithms. These algorithms are classified into symmetric and asymmetric algorithms. Same key is used for the encryption and decoding technique in symmetric key cryptographic algorithm whereas two keys are used for the asymmetric ones. In this paper, the implementation of one of the asymmetric algorithm RSA with the educational dataset is done. To secure the distributed database, the extended version of the RSA algorithm is implemented as the proposed work. © 2021 IEEE.

关键词： Cryptography

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Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

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SN computer science 2024年第1期5卷 190页

作者： Almahadin, Ghayth Subburaj, Maheswari Hiari, Mohammad Sathasivam Singaram, Saranya Kolla, Bhanu Prakash Dadheech, Pankaj Vibhute, Amol D. Sengan, Sudhakar Department of Networks and Cybersecurity Faculty of Information Technology Al Ahliyya Amman University Country Amman Jordan School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India Department of Networks and Cybersecurity Information Technology Al Ahliyya Amman University Amman Jordan Department Computing Technology School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Rajasthan Jaipur 302017 India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) MH Pune 411016 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Identifying suspicious activities or behaviors is essential in the domain of Anomaly Detection (AD). In crowded scenes, the presence of inter-object occlusions often complicates the detection of such behaviors. Therefore, developing a robust method capable of accurately detecting and locating anomalous activities within video sequences becomes crucial, especially in densely populated environments. This research initiative aims to address this challenge by proposing a novel approach focusing on AD behaviors in crowded settings. By leveraging a spatio-temporal method, the proposed approach harnesses the power of both spatial and temporal dimensions. This enables the method to effectively capture and analyze the intricate motion patterns and spatial information embedded within the continuous frames of video data. The objective is to create a comprehensive model that can efficiently detect and precisely locate anomalies within complex video sequences, specifically those featuring human crowds. The efficacy of the proposed model will be rigorously evaluated using a benchmark dataset encompassing diverse scenarios involving crowded environments. The dataset is designed to simulate real-world conditions where millions of video footage need to be continuously monitored in real time. The focus is on identifying anomalies, which might occur within short time frames, sometimes as brief as five minutes or even less. Given the challenges posed by the massive volume of data and the requirement for rapid AD, the research emphasizes the limitations of traditional Supervised Learning (SL) methods in this context. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Autoencoders LSTM Machine learning Spatio-temporal features Suspicious activities

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Machine Learning based Anomaly and Threat Detection System in Real Time Social Internet of Things using Dimensionality Reduction Techniques 2

Machine Learning based Anomaly and Threat Detection System i...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Jenifer, Anciline Piramu, Preethika S.K. Vels Institute of Science Technology & Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

The Social Internet-of-Things (SIoT) is an innovative model developing in the struggle to safeguard privacy and increase trust inside the Internet-of-Things(IoT). The revolutionary notion of SIoT combines the IoT with social platforms, allowing inanimate items to develop relationships with one another. The fast growth of SIoT has transformed networked networks, enabling effortless interaction between devices. However, integrating these systems creates major security issues, including anomalies and attacks that may compromise system integrity. This study presents a machine learning(ML) methods like Isolation Forest (IF), One class support vector Machine(SVM) and Kernel density estimation(KDE) for in SIoT networks using the Principal component Analysis(PCA) and Correlation Analysis as dimensionality reduction techniques. The findings shows that the IF method provides an accuracy of 93% with the dimensionality reduction techniques applied on RT-IoT dataset. By utilizing powerful machine learning algorithms and contextual analytics, the framework improves detection accuracy and enables strong device collaboration in highly dynamic contexts.. © 2025 IEEE.

关键词： Support vector machines

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Deep Learning-Driven SIoT Security: Detecting Anomalies and Community Attacks with SPY IoT Devices

Deep Learning-Driven SIoT Security: Detecting Anomalies and ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anciline Jenifer J Piramu Preethika. S. K. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The rapid growth of Social Internet of Things (SIoT) networks, new security vulnerabilities have evolved, including irregularity detection and community-based hacks. Given the large degree of decentralization in SIoT systems, traditional security methods are frequently ineffective. The study employs a deep learning (DL)-based security solution that detects anomalies and community attacks in real time using SPY IoT sensors. The proposed approach employs AutoEncoders(AEs) for SIoT behaviour, Convolutional Neural networks(CNN)+Long Short Term Memory(LSTM) for spatial and temporal features and Reinforcement Learning(RL) for adaptive Learning. The novel approach described in this study improves the detection of various attack situations, hence increasing the overall security and resilience of SIoT networks. The findings of the proposed study outperforms with an accuracy of 97.2 compared to other state o art methods. This study is related to building and developing intelligent and self-adjusting safety procedures for the future Internet of Things.

关键词： Deep learning Adaptive learning Adaptation models Accuracy Social Internet of Things Safety Security Convolutional neural networks Anomaly detection Standards

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Machine Learning based Anomaly and Threat Detection System in Real Time Social Internet of Things using Dimensionality Reduction Techniques

Machine Learning based Anomaly and Threat Detection System i...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Anciline Jenifer J Preethika S.K. Piramu Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Dimensionality reduction Performance evaluation Correlation Machine learning algorithms Real-time systems Vectors Threat assessment Social Internet of Things Security Kernel

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An Automated Detection System of Autism Spectrum Disorder Using Optimal Feature Selection-Based Adaptive RNN with Attention Mechanism

An Automated Detection System of Autism Spectrum Disorder Us...

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International Conference on computer and Communications Technologies (ICCCT)

作者： J. Jegan Amarnath S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The research introduces an automated system for ASD detection through the implementation of an adaptive RNN combined with optimal features selection together with an attention mechanism. The system operates to detect ASD early through a data-driven methodology by analysing behavioural, visual and auditory indicators that mark symptoms of ASD. This solution provides accurate diagnosis and personalized care that doctors can use because it operates across many scales. The system accepts multiple forms of data from facial expressions together with speech patterns and motor behavioural data. A combination of feature selection methods identifies essential data attributes within raw information to decrease complex dimensions that improves system operational speed. The adaptive RNN model extracts temporal relationships through its learning process and the attention mechanism allows the model to select matching features to detect subtle changes in ASD behaviour properly. The framework adapts automatically because it uses available data to refine itself with a purpose of refining detection accuracy. Research outcome shows that adding the attention mechanism enables the model to select important attributes leading to better detection precision. The feature selection system adds to model performance optimization because it enables the model to concentrate on important signals while reducing potential overfitting problems. The system can apply in real time through its diagnostic excellence which minimizes both false positive and negative results. The proposed automated system represents a major step forward for early ASD detection since it delivers clinicians an effective instrument for early intervention. The precise results of this model stem from its adaptive learning system as well as its attention-based focus which leads to better ASD diagnosis and treatment planning outcomes.

关键词： Adaptation models Autism Visualization Attention mechanisms Recurrent neural networks Adaptive systems Accuracy Computational modeling Variable speed drives Feature extraction

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Secure Multi-Party Computation for Collaborative Data Analysis in Network Security 2

Secure Multi-Party Computation for Collaborative Data Analys...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Tamilselvi, P. Lathika, V. Jayachitra, S. Arunkumar, S. Balasubramani, M. Kalaichelvi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Department of Cse Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Coimbatore Institute of Engineering and Technology Department of Ece Coimbatore India Vsb Engineering College Department of Computer Science and Engineering Karur India Sastra Deemed to Be University Srinivasa Ramanujan Centre Department of Cse Kumbakonam India

ISBN: (纸本)9798350306415

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. advanced threat analysis may also benefit. © 2024 IEEE.

关键词： Collaborative Data Analysis Cryptographic Protocols Network Security Privacy Preservation Secure Multi-Party Computation (SMPC)

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Preface to the 13th Workshop on Bibliometric-enhanced Information Retrieval at ECIR 2023 13

Preface to the 13th Workshop on Bibliometric-enhanced Inform...

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13th International Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2023

作者： Frommholz, Ingo Mayr, Philipp Cabanac, Guillaume Verberne, Suzan Brennan, Jordan School of Engineering Computing and Mathematical Sciences University of Wolverhampton United Kingdom GESIS Leibniz-Institute for the Social Sciences Cologne Germany University of Toulouse Computer Science Department IRIT UMR 5505 France Leiden Institute of Advanced Computer Science Leiden University Netherlands

This preface summarizes the 13th Workshop on Bibliometric-enhanced Information Retrieval (BIR). BIR 2023 was held as hybrid event at April 2nd, 2023, co-located with the 45th European Conference on Information Retriev...

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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