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Cyber-attack Detection Using Gradient Clipping Long Short-Term Memory Networks in Internet of Things 1

Cyber-attack Detection Using Gradient Clipping Long Short-Te...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Ayyalasomayajula, Madan Mohan Tito Mandala, Vishwanadham Prasad, Hanumat Gangopadhyay, Amit Shrivastava, Neeraj Sundaram, Ajith Aspen University School of Business & Technology Department of Computer Science Arizona United States Cummins Inc. IN United States Department of CSE- AI&ML Guntur India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Electronics and Communication Engineering Andhra Pradesh Tirupati India Medicaps University Indore India Amrita Vishwa Vidyapeetham Amrita Nagar Edappally Amrita School of Business Kochi Kerala Ernakulam India

ISBN: (纸本)9798350367003

The Internet of Things (IoT) is a network that connects a vast number of objects, enabling them to communicate and interact with each other with human intervention. The IoT is seeing rapid growth in the field of computing. However, it is important to acknowledge that IoT is very susceptible to many forms of assaults due to the hostile nature of the internet. In order to address this problem, it is necessary to implement practical steps to ensure the security of IoT networks, such as the implementation of network anomaly detection. While it is impossible to completely prevent assaults indefinitely, timely discovery of an attack is essential for effective defense. Because IoT devices have limited storage and processing power, standard high-end security solutions cannot protect them. In addition, IoT devices are now autonomously linked for extended durations. Consequently, it is necessary to create advanced network-based security solutions such as deep neural network solutions. While several researches have focused on the use of neural network methods for attack detection, there has been less emphasis on detecting assaults, especially in IoT networks. The objective of this research is to develop a gradient clipping long shortterm memory network (GC-LSTM) that can efficiently and promptly identify IoT network assaults. The Bot-IoT dataset is employed for evaluating various detection methodologies. The incorporation of additional features resulted in improved results. The GC-LSTM model, as proposed, achieves a remarkable accuracy of 99.98%, 97.67% of detection rate, 87.34% of TNR and 34.67% of FAR. © 2024 IEEE.

关键词： Network intrusion

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Automated Word Prediction in Telugu Language Using Statistical Approach

Automated Word Prediction in Telugu Language Using Statistic...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Lakshmi, L. Devi, K.Dhana Sree Kalyani, A.Naga Ravisankar, M. Rani, K. Pushpa Deaprtment of DS & AI Faculty of Science and Technology Hyderabad501203 India Department of CSE Hyderabad India BVRIT HYDERABAD College of Engineering for Women Department of CSE Hyderabad India MLR Institute of Technology Dundigal Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798350348217

Automated word prediction commonly called as language modeling is the task of predicting the next Word. Word completion and word prediction phenomenally benefit the disabled users who use physical keyboard or virtual keyboards in desktops and handheld systems. The objective of this paper is to predict the next word prediction and sequence of words prediction in Telugu language sentences with stochastic approach. The N-gram linguistic models such as uni-gram, bigram, tri-gram, maximum likely hood estimation, Laplace and add1 smoothing have used for automatic completion of a sentence with the prediction of appropriate word to save time, keystrokes and misspelling. We have used large data corpus of Telugu language from Telugu Wiki pages, which has words from different domains to predict correct word. © 2023 IEEE.

关键词： Forecasting

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Advanced Hybrid Intelligence Framework for Scalable and Robust Big Data Analytics 7

Advanced Hybrid Intelligence Framework for Scalable and Robu...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sreeja, S.P. Sreenivasulu, D. Jain, Alok Reddy, R. Akhilesh Jabbar, Fatimah Dhari Keerthi, V. New Horizon College of Engineering Master of Computer Application Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798350350067

The phenomena of data explosion in different areas of life require enhanced methods to performing big data analysis. This paper proposes the Hybrid Intelligent Machine Learning System (HImlS) which work through the combination of multiple machine learning approaches to improve the efficacy of big data analysis. HImlS applies the benefits of both supervised and unsupervised learning, integrates deep learning models and ensemble methods to solve the difficulties which are from high-dimension learning and heterogeneous data. As a result of combining feature selection with the process of dimensionality reduction HImlS moderates the curse of dimensionality which enhances the models' predictive capability while lowering the computational requirements. It is proved that the system works on actual big data implementation, exhibits a better solution in terms of the accuracy of the model, size, and stability aspects in contrast to the conventional machine learning approaches. This research also analyses the effects that different techniques of hyperparameters optimization select and the involvement of distributed computing systems for speeding up analytics. Thus the study infers HImlS as a suitable solution to the shortcomings characterized by current big data analytical techniques, giving an efficient method for harnessing big data solutions. © 2024 IEEE.

关键词： Adversarial machine learning

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Fingerprint Liveness Detection Using Handcrafted Feature Descriptors and Neural Network 11

Fingerprint Liveness Detection Using Handcrafted Feature Des...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Chen, Zhi-Sheng Pulungan, Reza Li, Yung-Hui Raswa, Farchan Hakim Harjoko, Agus Wang, Jia-Ching Kinarta, Indra Yusuf Lee, Chung-Ting Providence University Department of Computer Science and Information Engineering Taiwan Universitas Gadjah Mada Department of Computer Science and Electronics Indonesia AI Research Center Hon Hai Research Institute Taiwan National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665492324

Fingerprint recognition is commonly used to verify a user's identity. However, the fingerprint recognition systems in use today can be vulnerable to attacks. For example, some artificial fingerprints can spoof fingerprint recognition systems and use identity information to obtain personal information. For security reasons, fingerprint liveness detectors should be robust to attacks using various materials. As a result, we propose a fingerprint recognition method that uses multi-radius local binary pattern (Multi-radius LBP) and local phase quantization (LPQ) as local texture descriptors and wavelet transforms to remove noise. Classification results are obtained using a multi-layer perceptron (mlP) classifier. This study confirms that the proposed method is useful to improve the average fingerprint liveness detection tasks. On LivDet 2015 dataset, the proposed method achieves 6.73% Average Error Rate(AER). © 2022 IEEE.

关键词： Local binary pattern

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Artificial Intelligence-Driven Power Management System for Enhanced Efficiency in Smart Grids

Artificial Intelligence-Driven Power Management System for E...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Deepak, B.L. Sabin, T.T. Darshana, A. Vindya, L. Suma Manjunath, N. Mallikarjunaswamy, S. Shilpa, M. Rekha, V. Sjc Institute of Technology Dept of Information Science and Engineering Chickballapur India Sjc Institute of Technology Dept of Ai & Ds Chickballapur India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Christ University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350388602

Smart grids are crucial in modern energy systems, integrating sensors, communication networks, and control mechanisms to improve energy distribution. However, conventional methods like Time-Based Scheduling (TBS), Demand Response Mechanism (DRM), and Static Load Distribution (SLD) struggle with inefficiencies such as poor real-time load balancing, limited adaptability, and inaccurate power usage predictions. These drawbacks hinder the overall effectiveness of smart grid *** address these challenges, the proposed ai-Driven Power Management System (ai-PMS) leverages machine learning to optimize energy distribution and predict demand more accurately. By using historical and real-time data, ai-PMS dynamically adjusts power flow, ensuring better load distribution and efficiency. This method surpasses the limitations of TBS, DRM, and SLD. The implementation of ai-PMS has resulted in a 0.25% improvement in energy efficiency and a 0.15% boost in grid reliability, significantly reducing power losses. The system also enhances the integration of renewable energy sources, making it a more sustainable and adaptive solution for modern smart grids. These advancements promise better power management and lower operational costs for grid operators. © 2024 IEEE.

关键词： Smart power grids

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A Hybrid Deep Learning Framework for Efficient Network Intrusion Detection Systems using Data Augmentation

A Hybrid Deep Learning Framework for Efficient Network Intru...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Priya Shirley Muller K Madhavi Naga Durga Saile K B Prasanthi B Jayaram Goski Sathish Department of Mathematics Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Mallareddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering (AIML& IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Departmentt of Computer Science (AI&ML) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering RMK Engineering College Gummidipoondi India Department of Information Technology St.Martins Engineering College (Autonomous) Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Because of their critical position as the initial barrier against harmful actions, intrusion detection systems (IDSs) rely substantially on the efficacy of cybersecurity in protecting corporate communication. There are several limits to machine learning approaches that be solved by utilizing other deep learning architectures, even though they are widely used for intrusion detection. In addition, unbalanced datasets frequently make it difficult to evaluate the suggested models, which prevents a thorough evaluation of the models' effectiveness. This study seeks to tackle these difficulties by improving the performance of numerous deep-learning(DL) structures for IDS using data augmentation approaches. The experimental results shown that even a basic CNN-based architecture may produce very efficient network-attack-detection, but more complicated designs only managed to show slight performance increases. The results show that cybersecurity frameworks can easily incorporate the suggested deep learning-based intrusion detection approaches, which improves the capacity to identify and counteract complex network threats. The outcomes of this research exhibit that the accuracy of intrusion detection models is highly dependent on the dataset's quality and quantity, with augmented CIC-IDS-2017 showing an accuracy of up to 92%.

关键词： Deep learning Accuracy Communication systems Network intrusion detection Complex networks Data augmentation Integrated circuit modeling computer crime

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Authentication using Biometric Data from Mobile Cloud Computing in Smart Cities 3

Authentication using Biometric Data from Mobile Cloud Comput...

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

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Near East University Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near Artificial Intelligence Engineering Dept Near East University Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them. © 2023 IEEE.

关键词： Mobile cloud computing

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IoT-Based Livestock Monitoring and Management System Using Machine Learning Algorithms

IoT-Based Livestock Monitoring and Management System Using M...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Nagendar Yamsani K. Muthukumaran B Santhosh Kumar Asha V Navdeep Singh JoshuvaArockia Dhanraj School of Computer Science and Artificial Intelligence SR UNIVERSITY Warangal Department of Biomedical Engineering DSCET-Anna University Chennai Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

This article provides a description of an Internet of Things (IoT) system that employs machine learning algorithms to provide real-time and precise insights into the health and behaviour of animals. A network of Internet of Things (IoT) sensors strategically placed throughout the cattle area of the proposed system is capable of monitoring a variety of attributes, including temperature, humidity, grazing patterns, and movements. The information gathered by these sensors is wirelessly transmitted to a central centre, where it is analysed by algorithms that acquire expertise through practice. To extract meaningful results from acquired data, supervised and unsupervised machine learning algorithms are implemented. The system maintains real-time surveillance of farmers or administrators and promptly alerts them to any anomalous events that may occur in regards to the animals' welfare or behaviour. This empowers them to expeditiously implement preventative measures against infections or optimise feeding regimens. Investigating historical data can yield significant insights that facilitate well-informed decision-making concerning enduring concerns like resource allocation or breeding initiatives. The incorporation of machine learning technology and the internet of things (IoT) into livestock management practices increases overall agricultural efficiency, productivity, and animal welfare.

关键词： Temperature sensors Temperature measurement Machine learning algorithms Animals Surveillance Sensor phenomena and characterization Real-time systems

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Dynamic Malware Classification and Signature Generation Using Multi-View Convolutional Neural Networks

Dynamic Malware Classification and Signature Generation Usin...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Sweety Prasanna Kiruba G Shubha G. Sanu K. Saranya Tatiraju.V. Rajani Kanth Mahesh S Department of Computer Technology Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering (AI & ML) KLS Gogte Institute of Technology Afliated to Visvesvaraya Technological University Belagavi India Department of Electronics and Communication Engineering Sona College of Technology Salem India TVR Consulting Services Private Limited Hyderabad India Department of Computer Science and Engineering-AI Mar Baselios Christian College of Engineering and Technology Peermade Idukki

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The increasing sophistication of malware has posed significant challenges to cybersecurity, necessitating advanced approaches for malware detection and signature generation. This study presents a novel method for dynamic malware classification and signature generation using Multi-View Convolutional Neural Networks (MVCNNs). The proposed framework captures multiple perspectives of malware behavior, including system calls, memory access patterns, and network activities, to create a comprehensive representation of malicious activities. The MVCNN architecture leverages these diverse views to enhance classification accuracy, addressing the limitations of single-view analysis. The dynamic execution of malware in a sandboxed environment generates behavior logs, which are preprocessed and transformed into multi-dimensional feature maps for input into the MVCNN. The model then extracts high-level features across multiple views, enabling robust classification and the automated generation of unique malware signatures. Experimental results demonstrate the proposed method's effectiveness, achieving a classification accuracy of over 98% on benchmark datasets. Additionally, the generated signatures exhibit high precision and recall in identifying malware variants. The proposed approach not only improves malware detection rates but also provides scalability and adaptability for real-time cybersecurity applications. This research highlights the potential of multi-view deep learning frameworks to advance dynamic malware analysis and enhance cybersecurity resilience.

关键词： Deep learning Accuracy Scalability Prevention and mitigation Benchmark testing Feature extraction Malware Real-time systems Convolutional neural networks computer security

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Generative Adversarial Networks for Semi-Supervised Learning in Clinical Decision Support Systems 3rd

Generative Adversarial Networks for Semi-Supervised Learning...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sengan, Sudhakar Katbar, Nek Muhammad Kothalanka, Amarendra Shelke, Mayura Chinnathambi, Kalimuthan Sharma, Dilip Kumar Ammasai, Viswanathan Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of AI & DS AISSMS IOIT Pune411001 India Deprtment of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819783540

Clinical Decision Support Systems (CDSS) have emerged as crucial tools in healthcare, leveraging vast amounts of data to aid clinicians in their decision-making processes. However, the challenge of efficiently utilizing limited labelled medical data remains. This paper introduces a novel approach using Semi-supervised DC-GAN to address this challenge. Building upon the foundational principles of Generative Adversarial Networks (GANs), this proposed model augments the capabilities of traditional semi-supervised methods, optimizing their performance in the CDSS landscape. Through extensive evaluations, they compared this approach with three widely accepted semi-supervised algorithms: MixMatch, Mean Teacher, and Π-Model. The results indicate that Semi-supervised DC-GAN enhances the data utilization process and significantly improves model training, especially when working with limited labelled datasets. This research underscores the potential of GANs in revolutionizing semi-supervised learning within CDSS, paving the way for more efficient and informed clinical decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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