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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Chaudhary, Anisha Nalini Sunil, M. Tiwaskar, S.A. Vijayalakshmi, V.J. Sharma, Sorabh Senthurya, S. Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India

ISBN: (纸本)9798350388916

Intrusion Detection System (IDS) is used for identifying complicated Cloud assaults. A cooperative IDS may use an aggregation technique to decide on suspicious intrusions. Aggregation methods and IDS feedback slow the process. These restrictions make real-time cooperative IDS choices useless and unsustainable. We suggest a machine learning-based collaborative IDS that uses past feedback data to make proactive decisions to address these issues. DRL-based multi-cloud IDS was suggested. The suggested approach uses a DE noising Auto encoder (DA) to create a deep neural network. DA can rebuild IDS feedback from incomplete feedback. The DA network uses Q-learning to continuously learn features. This lets us respond on suspected incursions without IDS input. The GPU-enabled Tensor Flow model was tested on a real-world dataset. Our model achieves 95% detection accuracy in experiments. © 2024 IEEE.

关键词： Anomaly Detection Cloud Environment Deep Reinforcement DRL Effective Anomaly Identification and IDS Multi-Cloud Environment

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Cybersecurity Assessment of IoT Networks Using the Connected Random Artificial Neural Networks 2

Cybersecurity Assessment of IoT Networks Using the Connected...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Sunori, S.K. Krishna Reddy, B.N. Dedgaonkar, Suruchi Gaurav Balaji, N.V. Dev, Anoop Ganga, S. Graphic Era Hill University Bhimtal India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India School of Mangement - UG Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India

ISBN: (纸本)9798350388916

In order to determine how secure an Internet of Things (IoT) network is, this article suggests a way to find all the affected devices with IP addresses at the same time. The Associated RNN (ARNN) is a recurrent structure that utilizes a particular Random Neural Network (RNN) design, which consists of two sub-networks that are mutually associated and complement each other. Two separate neurons of the ARNN support opposing opinions for every one of the n gadgets or IP addresses that exist in the IoT network: compromised or not hacked. Pairs of neurons in a fully-connected 2n ARNN structure may learn from real-world information even when power is off. A single overall attack detector is provided by the ARNN, which learns about the interdependencies among network nodes, observes the incoming traffic at each node, and formulates a recommendation for every gadget or IP address in an IoT network using the feature selection (Artificial Bee Colony Optimization) method. This eliminates the need for a separate attack detector at each network node. We go over the ARNN learning algorithm and weight initialization, and then we test it with real-world attack data and compare its results to those of other testing and learning methods. Using ground truth data offline and an improved average metric collected from entering packet traffic for online incremental learning, results are produced for both scenarios. The ARNN surpasses all prior methods by a wide margin when compared to the top state-of-the-art methods. © 2024 IEEE.

关键词： Bot (Internet)

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Gop Size Effect on VVC Encoder Performance with MRF Varation

Gop Size Effect on VVC Encoder Performance with MRF Varation

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International Conference on Advanced technologies for Signal and Image Processing (ATSIP)

作者： Rana Jassem Taheni Damak Mohamed Ali Ben Ayed Nouri Masmoudi Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq New Technologies and Telecommunications Systems (NTS’Com) Sfax University Tunisia Laboratory of electronics and technology’s information (LETI) Sfax University Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

The exponential growth of multimedia content and applications underscores the ongoing challenge of developing advanced video coding standards that not only enhance compression rates but also incorporate essential features while addressing increasing complexity in computational processing. Among these standards, the Versatile Video Coding (VVC) standard stands out for its superior efficiency compared to its predecessors. As one of the key parameters in video compression algorithms, the size of Group of Pictures (GOP) determines the frames interdependency in the encoded video. The GOP size significantly influences the performance of a Video Coding Standard (VVC) encoder, particularly in conjunction with Multiple Reference Frame (MRF) variation. Adjusting the GOP size can have a profound impact on compression efficiency and visual quality for video coding. In this study, we investigate the effect of varying MRF configurations and GOP sizes on the performance of VVC encoder. Furthermore, we advance the notion that achieving diverse MRF configurations is feasible through strategic adjustments to the number of reference frame within each reference list. In this paper, we present statistical results showing the impact of different Quantization Parameters (QPs) and GOP size on encoder performance for each configuration. Based on our results, varying GOP size with MRF configurations has a significant positive impact on encoder performance as measured by bitrate and execution time for several values of QP without affecting the quality of the reconstructed video and the resulted bitrate.

关键词： Video coding Radio frequency Visualization Quantization (signal) Bit rate Video compression Streaming media

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Denial-of-Service Attack Detection in Wireless Sensor Networks: A Simple Artificial Intelligence Method 2

Denial-of-Service Attack Detection in Wireless Sensor Networ...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Sharma, Vikrant Velusudha, N.T. Traisa, Roopa Deshmukh, Vishwesh Balaji, N.V. Sriram, Siddharth Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India School of Mangement - UG Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350388916

When compared to more conventional networks, wireless sensor networks (WSNs) stand out. Classical networks' networking techniques, in contrast to WSN, can only regulate incoming packets according to their Internet Protocol (IP) address translation. The efficient gathering and transmission of data from such a network is an essential need for any application. A Denial of Service (DoS) assault occurs when malicious actors intentionally try to disrupt the data supply process, making the anticipated services unavailable. Because denial-of-service attacks aim to disrupt regular services, they may often severely limit the use of wireless sensor networks. When malicious nodes gain access to a network, they may launch a denial-of-service (DoS) assault. Using a trust-based method, this study aims to trace these nodes that are colluding. One component of the trust-based system is a way to quantify the level of confidence in each node. Nodes that have a low level of trust are considered to be malevolent nodes. All packets sent within the given time window are considered a measure of trust. The node that forwards the most packets while leveraging the fewest assistance will be marked as malicious. By outperforming RF, XGBoost, and KNN classifiers while using less training data, the suggested method has shown strong performance. Its accuracy rate is 99.5%. Our suggested method considerably beats these classifiers in regard to processing time, since it only uses 9.7 percent, 13.1 percent, and 2% of the time that FR, XGBoost, and KNN use, respectively. Notably, RF did yield slightly better accuracy;nevertheless, the suggested method substantially outperformed RF by requiring only 9.7 percent of RF processing time, a crucial component in fulfilling WSN restrictions. We used certain performance indicators to evaluate the results after deploying the emerging architecture in NS3. The total number of packets sent by each node during their allocated time window was used to determine trust

关键词： Denial-of-service attack

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A Smart Innovation of Business Intelligence Based Analytical Model by Using POS Based Deep Learning Model

A Smart Innovation of Business Intelligence Based Analytical...

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2023 International Conference on Disruptive technologies, ICDT 2023

作者： Jagannathan, Sharath Kumar Bizel, Gulhan Thomas Abraham, J.V. Kannan, G. Sankaradass, Veeramalai Navulla, Durgaprasad Saint Peter's University Frank J. Guarini School of Business Jersey CityNJ07306 United States Vellore Institute of Technology School of Computing Science and Engineering Tamil Nadu Chennai India St. Peter's Institute of Higher Education and Research Department of Management Studies Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Kl Business School Andhra Pradesh Guntur India

ISBN: (纸本)9798350323887

In analytics and business intelligence, there are a lot of things that can go wrong. Be it a report or a plan in the data based results must be consistent, verifiable, accurate to emerge and most importantly, acceptable to the end user. Contrary to the popular belief that the coexistence of multiple BI tools poses some major impediment to the advancement of high-quality analytics output, allowing multiple tools at the same time has several serious advantages in many ways. These are all examples of BI software and are more intelligent than relatively sophisticated tools. In this paper innovative business intelligence based analytics model was proposed to enhance the point of sale in commercial retail market. The practice of continuous integration, invented by software developers and borrowed by the analytics and business intelligence community, is an attempt to detect mistakes or errors early. The Point of sale systems occupies every retail space. Any company that has employees has some software that manages payroll, Sales reports are almost universal. © 2023 IEEE.

关键词： Wages

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Future IoT Applications using Artificial Intelligence-based Sensors: Agriculture 4

Future IoT Applications using Artificial Intelligence-based ...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Venkatachalam, S. Neeraja, Peddinti Kavitha, S. Rajasekaran, B. Kumar, G. Santhosh Jose, Bibin K Karpagam Academy of Higher Education Department of Management Coimbatore India Sree Vidyaniketh An Engineering College Department of Mca Tirupati India Saveetha College of Liberal Arts and Science Saveetha Institute of Medical and Technical Sciences Department of Computer Science Chennai India Department of Electronics and Communication Engineering Salem India Cvr College of Engineering Department of Ece Telangana Hyderabad India Department of Mathematics Kerala India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

The world of gadgets is rapidly expanding and has a profound impact on individuals. The effort is being done to construct a robotized watering system, which will have a huge impact on future events. Agriculture is a big contributor to a country's economy. The information obtained from the sensors is used to improve cultivation precision in all areas, including watering, pesticide application, and fruit ripening. Furthermore, it is feasible to directly determine the ideal conditions for the plants in controlled environments, such as greenhouse farming, by adjusting variables like temperature and humidity. © 2022 IEEE.

关键词： Hydroponics

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AI-Enabled Motion Detection System for Smart Healthcare Environment 2

AI-Enabled Motion Detection System for Smart Healthcare Envi...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Dhabliya, Dharmesh Ravivarman, G. Kapoor, Tarun Keerthika, A. Garg, Anvesha Mathur, Sanjiv Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore64102 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798350388916

Humans' bad habits are a leading cause of many chronic illnesses. Preventative medicines and telemedicine courses for treating these conditions are proliferating. The availability of secure and pleasurable alternatives continues to be of paramount importance. When it comes to monitoring and studying human health, inertial measurement units (IMU) are frequently viewed as the most objective and least intrusive alternative due to their ability to identify motion patterns with relative ease. Machine learning is commonly regarded as an effective method for analysing IMU data and identifying motion patterns. As part of a smart healthcare learning tool, we describe a deep learning-based technique for detecting human movements. An innovative hybrid adjectives-based on before-classification & multi-features analysis technique is created to classify human motion for usage in healthcare e-learning. To begin, a Kalman filter is used to do preliminary processing on the raw data from the IMUs. An experiment is conducted in which minimum and average gravity reduction techniques are used to the acceleration data. Signal segmentation has been used to data from a variety of time periods to establish which kind of segmentation is most effective. Then, we identify active as well as passive motion patterns to do a preliminary categorization. Both static and dynamic motion patterns have been analysed to recover characteristics. The Convolutional Neural Network (CNN) has been used to categorise the active and passive motion components of healthcare e-learning. We accomplished this using the wearable computing and REALDISP datasets. In experimental comparisons, our proposed method for intelligent health care learning surpassed the current state-of-the-art systems. Implementing the technique as recommended led to an accuracy of 87.35% for the REALDISP dataset & 85.18 on the wearable computers dataset. Furthermore, a smart healthcare advisor is provided with access to the classified motion s

关键词： Convolutional neural networks

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The Use of 6G Communication Technology in Healthcare Applications for the Accurate and Better Transmission

The Use of 6G Communication Technology in Healthcare Applica...

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Advance Computing and Innovative technologies in Engineering (ICACITE), International Conference on

作者： Jamvant Omkar S. Menaka J Praveena Neeraj Varshney B. Arunkumar G Vinoda Reddy Department of Electronics & Communication Engineering IES College Of Technology Bhopal M. P India Nehru Institute of Information Technology and Management Coimbatore Department of Science and hummanities Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Engineering and Applications GLA University Mathura Computer Science Engineering department Karpagam Academy of Higher Education Department CSE (AI&ML) CMR Technical Campus Medchal Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The IIoT is the most important element in the 6th generation networks, or 6G, so it greatly depends on effective data processing and storage. For the cause of ensuring smooth node-to-node communication, 6G provides ultra-low delays, high bandwidth, and higher-level QoS. Intelligent diagnosis, numerous in-hospital, and distant healthcare services are prerequisites to enable healthcare institutions to provide patientcentered care. What is essential is that the system hosting an array of uses, ranging from ambient appropriate living settings with wearable technology-enabled remote patient monitoring to robotic procedures powered by an intelligent networking platform. Therefore, this work addresses the needs of the healthcare sector by providing a reference-layered contact architecture for locations and devices in real-time communication. The unique 6G technology can, therefore, be utilized with great foresight to the future in the healthcare sector, enabling effective treatment and intelligent service integration. Because several networking nodes, including terminal devices, are integrated into effective health service delivery, a backbone structure is thus required for the services that take much time to complete. Therefore, a backbone structure for managing services is required to enable smooth communication by the framework, developed to enable this kind of 6G-enabled advanced healthcare application.

关键词： 6G mobile communication Patient monitoring Surgery Medical services Quality of service computer architecture Transforms

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NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection through Optimized Feature Reduction and Machine Learning

NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection ...

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Electrical, Electronics, Information and Communication technologies (ICEEICT), International Conference on

作者： Sreenivas Reddy Sagili Senthilnathan Chidambaranathan Nagarjuna Nallametti Harana M. Bodele Laxmi Raja P G. Gayathri Department of Computer Science Rochester Institute of Technology United States 20 Corporate Pl South Virtusa Corporation Piscataway United States Department of CSIT CVR College of Engineering Hyderabad Telangana India Department of Electronics and Telecommunications J D College of Engineering & Management Nagpur India Department of Computer Science and Engineering (Cyber Security) Karpagam Academy of Higher Education Coimbatore India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Alzheimer’s (memory loss disorder) disorder Disease (AD) poses a significant diagnostic challenge due to its complex etiology and subtle onset of clinical symptoms. The Early and accurate detection will be a crucial for effective management and intervention. The integration of machine Learning-Driven (ML) techniques based on neuro imaging and clinical data may offers a promising avenue for enhancing of AD detection. However, the highest dimensionality of such data could be impede the efficacy and accuracy of ML models due to curse of dimensionality and over fitting. This research will introduces the "NeuroPCA System," a novelist diagnostic framework by employing Principal Components based Analysis (PCA) for features reduction, tailored specifically for Alzheimer’s (memory loss disorder) disorder Disease detection. The NeuroPCA System will systematically reduces the dimensionality of input health data (e.g., MRI, PET scans, and genetic information) while by preserving the most informative variance components crucial for distinguish between AD patients and healthy controls. The proposed methodology may encompasses preprocessing of the multi-modal dataset to normalized feature scales, followed by the application of PCA to extracts the principal components that capturing of the maximum variance in the dataset. The reduced feature set will then utilized to train and validate a suite of machine Learning-Driven models like Supportive Vector Machines (SVM) for classify subjects into AD and non-AD groups. The effectiveness of the NeuroPCA System may evaluated depends on classification accuracy, sensitivity, specificity, and computational efficiency, by using a cross- validated dataset of diagnosed AD patients and a control group. The NeuroPCA System also demonstrates a significant improvement in model performance and computational efficiency, by highlighting the potential of PCA for feature reduction in the context of Alzheimer’s (memory loss disorder) disorder Disease detec

关键词： Training Support vector machines Accuracy Computational modeling Feature extraction Genetics Vectors Computational efficiency Alzheimer's disease Principal component analysis

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African Union Convention on Cyber Security and Personal Data Protection: Challenges and Future Directions

arXiv

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arXiv 2023年

作者： Bouke, Mohamed Aly Abdullah, Azizol Alshatebi, Sameer Hamoud El Atigh, Hayate Cengiz, Korhan Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Computer Engineering Istinye University Istanbul34010 Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove500 03 Czech Republic Departement of Computer Engineering Faculty of Computer Engineering Bandirma Onyedi Eylul University Balikesir10200 Turkey

This paper investigates the challenges and opportunities of implementing the African Union Convention on Cyber Security and Personal Data Protection (AUDPC) across Africa. Focusing on legal, regulatory, technical, infrastructural, capacity building, awareness, Harmonization, and cross-border cooperation challenges, the paper identifies key findings that highlight the diverse legal systems and traditions, the lack of comprehensive data protection laws, the need to balance national security and data privacy, the digital divide, cybersecurity threats, implications of emerging technologies on data privacy, limited resources for data protection authorities, and the need for capacity building in data privacy and protection. The paper also emphasizes the importance of Harmonization and cross-border cooperation in aligning data protection frameworks and collaborating with international partners and global organizations. To address these challenges and facilitate the successful implementation of the AUDPC, the paper proposes a set of recommendations, including strengthening legal and regulatory frameworks, enhancing technical and infrastructural capacities, fostering capacity-building and awareness initiatives, promoting Harmonization and cross-border cooperation, and engaging with global data protection trends and developments. © 2023, CC BY.

关键词： Cybersecurity

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