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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Sankara, Khadija Danladi Saritha, G. Anitha Elavarasi, S. Saradha, S. Arul Kumar, D. Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sona College of Technology Salem India Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

Cybersecurity is increasingly challenged by complex and evolving threats that require predictive intelligence and advanced anomaly detection. This paper presents a novel Hybrid LSTM-GAN (Long Short-Term Memory - Generative Adversarial Network) model designed for predictive cyber threat intelligence and real-time anomaly detection. By leveraging LSTM’s ability to capture temporal dependencies in sequential data and GAN’s strength in generating synthetic samples, this hybrid model enhances the detection of subtle and emerging cyber threats. The model first uses LSTM layers to analyze patterns within time-series data, identifying potential anomalies. A GAN is then employed to generate realistic threat scenarios, enhancing the model's ability to recognize unusual activity and improve generalization. This two-step approach is crucial for capturing complex patterns and reducing false positives, which are common in conventional detection methods. Experimental results on benchmark cybersecurity datasets demonstrate that the proposed model achieves a 92.5% detection accuracy, a 15% reduction in false positives compared to standalone LSTM models, and a 25% improvement in threat prediction capabilities over traditional methods. These values indicate that the Hybrid LSTM-GAN model is a powerful tool for predictive threat intelligence, providing proactive and reliable anomaly detection across various cybersecurity applications. © 2024 IEEE.

关键词： Cyber attacks

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

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computers, Materials & Continua 2023年第11期77卷 2097-2121页

作者： Deepak Kumar Vinay Kukreja Ayush Dogra Bhawna Goyal Talal Taha Ali Department of Computer Science&Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Electronics&Communication Engineering Chandigarh UniversityPunjab140413India Department of Dental Industry Techniques Al-Noor University CollegeNineveh41018Iraq

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every *** wheat rust diseases are identified either through experienced evaluators or computerassisted *** experienced evaluators take time to identify the disease which is highly laborious and too *** wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield *** solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease *** the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation *** augmented images are preprocessed by histogram *** a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch *** layers of region extraction models construct a feature vector that is later passed to the CE-SVM *** a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

关键词： Wheat rust diseases agricultural region extraction models intercropping image processing feature extraction precision agriculture

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An Efficient and Intelligent Systems for Internet of Things Based Health Observance System for Covid 19 Patients

An Efficient and Intelligent Systems for Internet of Things ...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Vignesh, R. Sathya Kumar, S. Arun Amirthalakshmi, T.M. Delphy, P. Arunkumar, J.R. Kamatchi, S. Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science Engineering Loyola Icam College of Engineering and Technology Chennai India Department of Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India Department of Electronics and Communication Engineering Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350334357

This paper describes how an IoT -based health monitoring system was conceived and built (IoT). With the proliferation of new technologies, doctors nowadays are constantly on the lookout for cutting-edge electronic tools that will make it simpler to detect abnormalities in the human body. The Internet of Things makes it possible to create cutting-edge, non-intrusive healthcare assistance systems. In this article, we introduce the Comprehensive Health Monitoring System, or CHMS. Normal people can't afford to buy separate devices or make frequent trips to hospitals. Our CHMS will monitor a patient's vitals, including temperature, heart rate, and oxygen saturation (OS), and relay that information to a portable device. To make sense of the information gathered by the physical layer's sensors, the logical layer must analyses it. The application layer then makes judgments based on the processed data from the logical layer. The primary goal is to reduce costs for average consumers. Patients will have simple access to individual healthcare, in addition to financial sustainability. This study introduces an IoT -based system that would streamline the operation of a complex medical gadget while reducing its associated cost, allowing its users to do so from the comfort of home. The public's adoption of these gadgets as aids in a given setting might have significant effects on their own lives. © 2023 IEEE.

关键词： Internet of things

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Predictions of Basketball Match Results using Autoencoder with Gated Recurrent Unit Algorithm 1

Predictions of Basketball Match Results using Autoencoder wi...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Kavitha, K.R. Almusawi, Muntather Vybhavi, Gummagatta Yajaman Edwin Prabhakar, P.B. Saranya, N. Naga Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

This research explores basketball match predictions within the field of sports analytics utilized Deep Learning (DL) technique to analyse the outcomes of National Basketball Association (NBA) games based on player performance and team statistics. The challenge in predicting NBA games outcomes and performance analysis various factor, as the high dimensionality of the data and uncertainty surrounding player due to difficult to predictions of basketball game. This paper, proposed Autoencoder with Gated Recurrent Unit (AE-GRU) algorithm to efficiently analyse and predict NBA game outcomes. The data was initially collected from NBA games between 2009 - 2021, focusing on five teams and pre-processing involved min-max normalization to uniformly scale the feature vectors. The Principal Component Analysis (PCA) utilized for feature selection, effectively reducing the high dimensionality of the NBA game data across various teams. The AE-GRU technique then captured the complex feature of basketball game data and handled uncertainty data and enhances performance. The performance of DL model evaluated the various metrics such as accuracy, precision, recall and f1-score metrics on NBA game. © 2024 IEEE.

关键词： Deep learning

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Enhancing Image Segmentation in the Marine Atmosphere for Time-Sensitive Contextualisation using Convolutional Long Short-Term Memory 2

Enhancing Image Segmentation in the Marine Atmosphere for Ti...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Reethika, A. Rajeshwari, R. Vijayalakshmi, S. Malathy, S. Gnanasaravanan, S. Suresh Kumar, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Information Technolog KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

The work investigates autonomous surface vehicle obstacle avoidance and detection using computer vision techniques. It recognizes boundaries in marine environments by using the temporal context from video feeds from unmanned surface vehicles. The study shows how successful LSTM cells are in the CNN topologies that are currently in use, emphasizing how they might perform better. Nevertheless, datasets and network training techniques are not perfect. The work also uses a benchmarking approach to compare new network configurations. © 2024 IEEE.

关键词： Image segmentation

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A Whispering Widgets and Wireless Wonders Pioneering Change in the World of IoT 7th

A Whispering Widgets and Wireless Wonders Pioneering Change ...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Tripathi, Amit Kumar Islam, Baharul Murugan, R. Adhvaryu, Rachit Department of Electronical and Electronics Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Engineering Assam Down Town University Assam Guwahati India Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9789819753369

The IoT has made it possible to connect and control more devices than ever before, opening up new avenues for efficiency and creativity. However, new problems have emerged as a result of this explosion of linked devices, notably concerning the security and privacy of personal information. To meet these difficulties, we provide a new approach within the context of "Whispering Widgets and Wireless Wonders: Ushering in a New Era for the Internet of Things." Our suggested approach safeguards data during transit and storage by using state-of-the-art cryptographic methods including Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES). Furthermore, we use blockchain technology to guarantee the immutability and transparency of data by creating a public and unchangeable ledger of all data exchanges. Our research shows that our suggested strategy for securing IoT data is superior to more conventional methods. We test how well our system protects sensitive information, keeps data intact, keeps users anonymous, and how efficient it is overall. When compared to conventional approaches, the "Proposed Method" regularly performs better, demonstrating its strength and applicability for protecting IoT data. High security is guaranteed by the combination of ECC and AES, and data integrity is improved by blockchain technology. Furthermore, our approach places a premium on user privacy, giving people more say over their data inside the IoT ecosystem. Finally, we believe that our suggested solution is a major advancement in achieving more data security and privacy in the IoT context. To the problems caused by the proliferation of linked gadgets, it provides a thorough and effective answer. Our approach prioritizes security, data integrity, and user privacy to let people and businesses get the benefits of the Internet of Things (IoT) without compromising on data privacy or security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025

关键词： Data integrity

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms 4

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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A Novel and Effective Ensemble Machine Learning Model for Identifying Healthy and Rotten Fruits

A Novel and Effective Ensemble Machine Learning Model for Id...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Kumar, S. Arun Mary, L. Hubert Varsa, G.V. Shaamili Ulaganathan, M.S. Kannagi, L. Bharathi, P. Shyamala Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798350334357

The world's food supply is dwindling at an alarming rate because of inefficient measures used to cope with food waste at all levels of the food system, from farms to households to restaurants. Ripe fruit may now be picked. Predicting when fruit will be ripe and of better condition is crucial not just for farmhand and the food business, but also for grocery shops and corner markets. The term 'fruit maturity classification' refers to the method through which fruits are categorized based on their stage of development. Machine learning (ML) is being used in a wide variety of smart agricultural use cases, including soil and water management, seeding crops, early diagnosis, weed elimination, crop dispersal, crop collection, and productivity prediction. The purpose of this research is to identify the most effective deep learning algorithms for estimating fruit ripeness and quality to extend their storage life. Banana fruit datasets are utilized in this investigation. To increase the total no of images in the collection, image augmentation is utilized. For both datasets, we propose an Ensemble Machine Learning Technique (EMLT) that combines SVM with ResNets. Our analysis led us to the conclusion that the suggested EMLT model is the most efficient technique for determining the quality of bananas and classifying their ripeness. © 2023 IEEE.

关键词： Fruits

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Detection and Evaluation of Cybersecurity Threats in MANET Based on AI 4

Detection and Evaluation of Cybersecurity Threats in MANET B...

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

作者： Mohandas, R. Vaigandla, Karthik Kumar Sivapriya, N. Kirubasankar, K. Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India Balaji Institute of Technology and Science Department of Electronics & Communication Engineering Telangana Warangal India Cauvery College for Women Department of Computer Applications Tamil Nadu Trichy India

ISBN: (纸本)9798331529635

This research focuses on the use of AI to enhance the security of Mobile Ad-hoc Networks (MANETs), which aredecentralized and mobile wireless networks characterized by their dynamic nature. The work aims to assess the risks associated with cyber activities and identify various sorts of cyber threats, such as Distributed Denial of Service (DDoS) attacks, malware invasions, data leaks and unauthorized access attempts. This will be achieved by using Artificial Intelligence (AI)-powered algorithms and models. The objective is to enhance the accuracy in identifying and categorizing these threats, hence increasing the security and dependability of MANET networks. Anomaly detection is a supplementary protection mechanism that specifically targets the hardware and network traffic of MANETs. Implementing a monitoring strategy is necessary in order to detect any anomalous activity that might potentially indicate security vulnerabilities or attacks in the MANET setups. The primary objective is to identify and address the unique characteristics of timely detection, which may enhance the security capabilities of MANET against cyber attacks. Machine learning (ML) algorithms often demonstrate impressive performance in efficiently identifying and accurately classifying various cyber threats, including DDoS attacks, malware infiltrations, and attempted unauthorized access. This anomaly detection technology effectively identifies robot abnormalities and malicious actions in network data, while also proactively avoiding system vulnerabilities or threats. In addition, this research discovered very effective AI-based cybersecurity solutions for dynamic decentralized MANET systems. These systems were specifically designed for tasks such as street-view switching, route finding, self healing, and self configuration. © 2024 IEEE.

关键词： Cyber attacks

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