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Threat Eye: Behavior Analytics for Cloud Security using ml

Threat Eye: Behavior Analytics for Cloud Security using ML

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： J. R. V. Jeny S. Shivaspandana K. Pavan N. Muthukumaran K. Akash Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Cloud computing has developed for its storage and accessibility of data, providing flexibility, scalability, and affordability. But its extensive use has also resulted in cyberattacks and unauthorized access. The traditional intrusion detection mechanisms become ineffective for different attack patterns, insider attacks and zero-day exploits. This paper provides an efficient way to address this issue by providing a mechanism to identify the threat based on the behaviour of the user. Unlike traditional approaches this paper provides a system that monitors the user behaviour patterns considering the features such as login history, authentication attempts, session duration, file access activities, password change frequency and IP location modifications. By considering these features the attack can be detected, and the data access is restricted to prevent from unauthorized access. This paper provides handling of attack with a combination of machine learning algorithms including SVM, Decision tree and XBoost, these help in distinguishing between the attacker and the authorized user. This helps in identifying complex attack behaviours and detect insider threats. Additionally, RSA encryption is integrated to secure data transmissions, preventing unauthorized access. By combining behavioural analysis, attacks prediction, and encryption, this provides a robust cybersecurity framework.

关键词： Support vector machines Logistic regression Machine learning algorithms Cloud computing security Scalability Intrusion detection Passwords Threat assessment Encryption Monitoring

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Optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed Systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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Securing Wireless and Optical Networks: Advanced Strategies for Network and Information Security in Modern Communication Systems

Securing Wireless and Optical Networks: Advanced Strategies ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Dhasmana, Gulshan Sharma, Vishal Boob, Nandini Shirish Aravind, K. Reddy, R. Akhilesh Yadav, Kanchan Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

In the era of rapidly changing communication systems, securing wireless and optical networks has become of essence to protect sensitive data and ensure operation. This study addresses network and information security in current communication systems by studying cutting edge techniques to enhance network and information security in wireless and optical networks. The wireless networks are convenient and may be moved around, but they are also prone to hacking, illegal access or some other cyberattacks. As optical networks possess both a large capacity and relatively low latency, it is not without special security issues such as fibre tapping and signal jamming. This study presents detailed analyses of state of the art authentication techniques, intrusion detection systems (IDS), and encryption algorithms specifically designed for these networks. Then there are, such as Quantum Key Distribution (QKD) which almost assuarly unbreakable techniques are examined alongside conventional cryptographic methods. Additionally, anomaly detection based on machine learning is explored for real time threat identification in optical and wireless channels. It stresses the need of the cross domain solutions and multi layered security frameworks covering the network layer and the physical security measures. Through case studies and recent developments in cyber threats and network resilience, this article provides a thorough understanding of how these tactics could be used to make networks such as wireless network and optical network more resilient against increasing cyber risks. © 2025 IEEE.

关键词： Network intrusion

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A Real-time Sign Language Recognition System using MediaPipe and Random Forest Classifier combining with Text-to-Speech Technique

A Real-time Sign Language Recognition System using MediaPipe...

引用

Artificial Intelligence and Data engineering (aiDE), International Conference on

作者： Sanapareddi Gari Yuva Nithesh Reddy Subhash Mondal P M G Bashir Asdaque K Vamsi Krishna Computer Science & Engineering (AI & ML) Dayananda Sagar University Bengaluru India Mechanical Engineering Dayananda Sagar University Bengaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This research study introduces a real-time sign language detection system utilizing hand gestures, specifically developed to bridge the communication gap for deaf and hard-of-hearing individuals. The system employs advanced computer vision techniques, utilizing MediaPipe for accurate hand tracking and landmark detection, extracting key features such as fingertip and palm positions. These features are normalized to address variations in hand size and orientation, ensuring consistent input for classification. A Random Forest Classifier (RFC), trained on a labeled dataset of gestures, processes the data to achieve reliable recognition. The system offers dual feedback mechanisms: real-time visual text overlays on live video streams and Text-to-Speech (TTS) output, making it intuitive and user-friendly. Iterative training and out-of-bag error monitoring enhance model performance, ensuring seamless functionality across devices ranging from mobile phones to larger systems. Motivated by the need for inclusivity, this solution enables effortless communication in both social and professional contexts. By leveraging ai-driven approaches, the system provides an accessible and efficient tool for promoting understanding and interaction, advancing assistive technologies for deaf and hard-of-hearing communities. This work represents a significant step toward creating more inclusive communication solutions.

关键词： Hands Training Sign language Visualization Technological innovation Accuracy Feature extraction Real-time systems Text to speech Random forests

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Advances in Big Data and Data Mining: Techniques and Applications in Data Fusion for Enhanced Insights and Decision-Making

Advances in Big Data and Data Mining: Techniques and Applica...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Goyal, Himanshu Rai Shrivastava, Anurag Nagpal, Amandeep Reddy, R. Akhilesh Yadav, Kanchan Revathi, R. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India New Horizon College of Engineering Department of Applied Sciences Bangalore India

ISBN: (纸本)9798331512965

The advancement of big data has accelerated over the years to change how companies utilize data to drive decisions. And so, this paper aims to look at big data and data mining in different perspectives while highlighting the importance of data fusion in decision making in the current society. Many times, data fusion combines different data sources to provide better and broader results. Clustering of raw data, anomaly detection and machine learning are discussed as well as their use in healthcare, financial management and smart cities. This paper also presents such issues as data heterogeneity, data quality assurance, as well as scalability issues. Lastly, tomorrow's possibilities, such as artificial intelligence and quantum computing, are presented as possible enhancements of the data fusion effectiveness are mentioned due to the expected marked advancement in real-time and adaptive decisionmaking systems. © 2025 IEEE.

关键词： Data centers

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Neural Networks Meet Neural Activity: Utilizing EEG for Mental Workload Estimation 27th

Neural Networks Meet Neural Activity: Utilizing EEG for Ment...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Roy, Partha Pratim Kim, Byung-Gyu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Division of AI Engineering Sookmyung Women’s University Seoul04310 Korea Republic of

ISBN: (纸本)9783031781940

Electroencephalography (EEG) offers non-invasive, real-time mental workload assessment, which is crucial in high-stakes domains like aviation and medicine and for advancing brain-computer interface (BCI) technologies. This study introduces a customized ConvNeXt architecture, a powerful convolutional neural network, specifically adapted for EEG analysis. ConvNeXt addresses traditional EEG challenges like high dimensionality, noise, and variability, enhancing the precision of mental workload classification. Using the STEW dataset, the proposed ConvNeXt model is evaluated alongside SVM, EEGNet, and TSception on binary (No vs SIMKAP task) and ternary (SIMKAP multitask) class mental workload tasks. Results demonstrated that ConvNeXt significantly outperformed the other models, achieving accuracies of 95.76% for binary and 95.11% for multi-class classification. This demonstrates ConvNeXt’s resilience and efficiency for EEG data analysis, establishing new standards for mental workload evaluation. These findings represent a considerable advancement in EEG-based mental workload estimation, laying the foundation for future improvements in cognitive state measurements. This has broad implications for safety, efficiency, and user experience across various scenarios. Integrating powerful neural networks such as ConvNeXt is a critical step forward in non-invasive cognitive monitoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electroencephalography

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Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding

Fast and High-Quality Auto-Regressive Speech Synthesis via S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Bohan Wang, Hankun Zhang, Situo Guo, Yiwei Yu, Kai MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The auto-regressive (AR) architecture, exemplified by models such as GPT, is extensively utilized in modern Text-to-Speech (TTS) systems. However, it often leads to considerable inference delays, primarily due to the challenges associated with next-token prediction in long speech sequences. In this work, we introduce VADUSA, one of the first approaches to accelerate AR-based TTS through speculative decoding. Our findings demonstrate that VADUSA not only delivers a significant reduction in inference time but also enhances TTS quality by employing draft heads to predict future speech tokens in an auto-regressive manner. Additionally, the incorporation of a tolerance mechanism during the sampling process further boosts performance, yielding approximately a 3× speedup in AR TTS. Moreover, our approach exhibits strong generalization across diverse datasets and various speech token types. © 2025 IEEE.

关键词： inference speedup speculative decoding text-to-speech

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ai-Driven Cybersecurity Strategies for ISPs: Balancing Threat Mitigation and Monetization

AI-Driven Cybersecurity Strategies for ISPs: Balancing Threa...

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20th International Conference on Cyber Warfare and Security, ICCWS 2025

作者： Singh, Nakul Kumar, Shreyas AI and Cybersecurity Practitioner San Francisco United States Department of Computer Science and Engineering Texas A&M University United States

ISBN: (纸本)9781917204316

As traditional revenue streams from bundled internet, voice, and TV services decline due to the rise of OTT platforms like Netflix and voice-over-IP services such as WhatsApp, Internet Service Providers (ISPs) are seeking innovative strategies to remain competitive. This white paper highlights the pivotal role of ai-driven cybersecurity in securing household video and data traffic across broadband, video streaming, and smart home devices, offering ISPs a powerful tool to enhance customer experiences and boost Average Revenue Per User (ARPU). By leveraging ai technologies, ISPs can safeguard and optimize the flow of data across connected homes, ensuring a seamless, secure, and high-quality experience for users. ai-driven cybersecurity solutions enable ISPs to monitor and protect video streaming content, gaming applications, and smart home devices such as IP cameras. These solutions not only enhance security but also improve network performance by identifying and mitigating potential vulnerabilities in real time. By integrating ai-powered cybersecurity tools with real-time Quality of Experience (QoE) analytics, ISPs gain deep insights into user behavior and device interactions. This allows them to proactively address issues such as data breaches, service interruptions, and latency, ensuring uninterrupted and high-quality service delivery. Armed with these insights, ISPs can create personalized service bundles that combine premium cybersecurity features with high-demand offerings such as 4K streaming, low-latency gaming, and smart home automation. For example, ISPs can offer tailored packages that include advanced firewall protection, parental controls, and IoT device management alongside premium content and high-speed internet options. These ai-driven bundling strategies not only enhance the security and performance of connected devices but also unlock new revenue streams by upselling premium packages to customers. Additionally, value-added features such as real-time

关键词： Quality of service

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Automated Image Captioning Systems

Automated Image Captioning Systems

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International Conference on Inventive Computation Technologies (ICICT)

作者： Aman Swaraj Ravi Raj Aniket Shaw Richa Dubey Prianka Dey Sagarika Chowdhury Computer Science and Business Systems Narula Institute of Technology Kolkata India Computer Science & Engineering (AI& ML) Narula Institute of Technology Kolkata India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The objective of the project is to design a system that leverages live camera feeds to detect objects in real time and generate concise, meaningful descriptions of the scene. The system processes the visual input, identifies key objects or actions, and produces a one-line caption summarizing the observed scenario. This caption is then saved in a text file, stored in the same directory as the program. For instance, if the live feed shows a person seated in front of the camera holding flowers, the system would generate the caption: “A person holding a bouquet of flowers,” which gets automatically written to the file. This solution integrates advanced technologies like real-time object detection and natural language generation to achieve efficient scene analysis and automated captioning. Such a system has diverse applications, including aiding visually impaired individuals, enhancing video surveillance, and improving user interaction in smart environments. By combining visual recognition with descriptive language processing, it provides an innovative way to summarize live camera feeds in a user-friendly format.

关键词： Visualization Braille Technological innovation Visual impairment Flowering plants Cameras Video surveillance Real-time systems Feeds Reliability

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