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DDoS Detection in SDN Environments: Leveraging Machine Learning Algorithms with Local Interpretable Model-Agnostic Explanations (LIME) for Enhanced Security

DDoS Detection in SDN Environments: Leveraging Machine Lear...

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International Conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Karna, Siwani Jahanve, Poluru Reddy Yadukul, S. Bhaskaran, Sreebha Vekkot, Susmitha Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9789819777167

SDN, short for Software Defined Networking, represents a network architecture controlled by software applications. Distributed Denial-of-Service (DDoS) attacks create a challenge to these SDN environments and lead to many issues like loss of data and network instability, and takes a lot of time to address requests made by human users. It is very crucial to detect such attacks, for users to seamlessly use the network and take advantage of what is available online. This study demonstrates the significance of Machine Learning algorithms in identifying malicious traffic. Employing algorithms Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) proves effective in detecting complex and dynamic attacks. The study utilizes Local Interpretable Model-agnostic Explanation (LIME) to analyze the strength of each feature that contributes towards the prediction of classification. The results highlight the superior performance of the Gradient Boosting Classifier, achieving an accuracy of 99.75% and an F1-score of 99.68% in detecting network anomalies and threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application programs

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Forecasting Bitcoin Price Trends: Integrated Machine Learning with Market Trends 3

Forecasting Bitcoin Price Trends: Integrated Machine Learnin...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Samir Ahamed, S. Radha, D. Kirthika Devi, V.S. Department of Computer Science And Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Bitcoin price prediction remains a complicated task in financial markets since price fluctuations are characterized by high variability, non-linear and dependent on different trends. The common models of forecasting can be disruptive because they do not readily accommodate the breadth of the data and the dynamics of the marketplace. The paper presents a new approach to predicting Bitcoin price shifts based on state-of-the-art data structures and both machine learning and technical analysis. The formative historical price datasets and large scale features are organized and manipulated in an efficient manner with the aid of data structures as well as the temporal and non-linear trends in prices are captured by ARIMA,Exponential smoothing and Random. Also, trends of moving averages are used to boost the model’s competency to analyze the sentiments of the market. The proposed framework was put through a test on real world Bitcoin price data and it was shown to provide better results as compared to the traditional methods with better accuracy and visualization. Besides that, our method enhances the accuracy of the forecast, while the deployment of the method allows developing a realtime Bitcoin price prediction model that can be useful for traders and investors. The integration of machine learning and technical analysis gives a powerful and reliable tool to forecast the Bitcoins price trends. © 2025 IEEE.

关键词： Prediction models

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Two Birds with One Stone: A Dense Attentive GAN-based Model for Detecting Alzheimer's and Parkinson's Disorders 14

Two Birds with One Stone: A Dense Attentive GAN-based Model ...

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14th International Conference on Cloud computing, Data science and engineering, Confluence 2024

作者： Devika, K. Murthy Oruganti, V. Ramana Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798350344837

This paper investigates two neurodegenerative disorders in adults - Alzheimer's (AD) and Parkinson's (PD). Most works in the research explored these diseases independently - AD vs. healthy subjects and PD vs. healthy subjects. We consider a one-class framework for capturing the characteristics of healthy subjects. This one-class model views any subject with AD or PD as an outlier. We employ Dense Attentive GAN for the one-class model. T1-weighted longitudinal structural magnetic resonance images (sMRI) are used as input. Furthermore, we use longitudinal samples to train the framework (two scans per subject over a 12-month gap). Most traditional methods relied on cross-sectional data (one scan per subject). The proposed method is analogous to doctor's diagnoses of the subject in different sessions to recognise the disorder. Extensive experimental studies have shown that our proposed method outperforms all other AD and PD-based works in the literature. © 2024 IEEE.

关键词： Generative adversarial networks

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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

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IEEE Transactions on Information Theory 2025年第6期71卷 4785-4796页

作者： Tian, Liying Song, Xiaoshi Liu, Zilong Li, Yubo Northeastern University School of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the "modulating" zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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Pioneering Pediatric Health - Machine Learning Ensembles for Appendicitis Detection 15

Pioneering Pediatric Health - Machine Learning Ensembles for...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vimal, Tejasvin Ganesh, Amruth J.M. Kumar, S. Yougesh Abhishek, S. Anjali, T. Department of Electronics and Communication Engineering Amrita School of Engineering Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350370249

Children’s appendicitis is often diagnosed using outdated techniques that may not be as quick or accurate as they need to be. The present investigation employs sophisticated machine learning (ML) methodologies to mitigate these constraints and enhance the diagnostic results. We investigated the effectiveness of many ensemble machine learning (ML) models, such as Gradient Boosting Machine (GBM), CatBoost, LightGBM, AdaBoost, and Decision Trees, using a proprietary dataset that contains a diverse range of pediatric appendicitis cases. After extensive training and validation, these models showed amazing accuracy and precision rates—the best models reaching up to 99% accuracy. In addition to outlining each model’s effectiveness, our comparison study clarifies which models are applicable in clinical contexts. This research work addresses how these models might improve clinical processes, lower diagnostic mistake rates, and speed up and improve the accuracy of clinical decision-making. The findings of this study demonstrate the revolutionary potential of machine learning to improve pediatric healthcare by offering reliable instruments for intricate medical diagnosis. ©2024 IEEE.

关键词： Adversarial machine learning

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Twitching Control for Pneumatic Artificial Muscle Actuators

Twitching Control for Pneumatic Artificial Muscle Actuators

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Bogdan, Piotr A. Smith, Oliver Klein, Frederico B. Gianni, Mario University of Plymouth School of Engineering Computing and Mathematics Faculty of Science and Engineering United Kingdom University of Liverpool School of Electrical Engineering Electronics and Computer Science United Kingdom

ISBN: (纸本)9798350325706

In this work we present the design and development of a twitching control system for pneumatic artificial muscle (PAM) actuators. In this system, a shape memory alloy (SMA) poppet valve is used to control the flow of air pressurizing the inner bladder of a McKibben-based PAM. The valve is driven by pulse width modulation with changing frequency, allowing the PAM to resemble the mammalian muscles twitching. An adjustable passive exhaust orifice is used in place of an exhaust air valve to release the air pressure from the bladder mimicking natural muscle relaxation behaviour. Experiments under isometric and isotonic contractions show that the proposed control system reduces hysteresis of about 72% compared to continuous activation against 15% loss in contraction force with respect to conventional exhaust air valves. © 2023 IEEE.

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Optimizing Energy Usage and Data Storage in Industrial IoT Settings Through Heuristic-Based Data-Rate Processors 2

Optimizing Energy Usage and Data Storage in Industrial IoT S...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharathi Kannan, B. Sriramulu, Srinivasan Gopal, Vidhya Karthick, M. Reddy, Yanna Nithin Natarajan, B. Galgotias University School of Computing Science and Engineering India Sona College of Technology Computer Science and Engineering Salem India K Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350383287

The Internet of Things (IoT) is a fast-expanding field, with applications in areas as diverse as transportation, aviation, automation, electricity, and healthcare. Sensors, protocols, actuators, cloud services, and layers are just few of the components that make up an IoT architecture. The architecture of the Internet of Things is crucial in delivering the goods. The research community now provides dozens of IoT architectures. interoperability, security, privacy, dependability, energy limits, scalability, and a lack of shared standards are only some of the problems that have been recognized by the research community. The proposed IoT architecture should also consider issues of interoperability, scalability, dependability, and trust. By fixing these issues, this research work builds an IoT ecosystem that can accommodate a wide range of devices and services, guarantee smooth communication and scalability, and lay the groundwork for secure, dependable IoT offerings. In effective offloading and resulting interruptions in task execution might result from high latency or network outages. However, because of the importance of a select few delay-sensitive applications, it is important to guarantee their safety. Therefore, this research work aims to unload data at edge and cloud servers, Heuristic based Energy Efficient Data Rate Controller for Offloading has been developed for sensor networks. The bio-inspired NSGA-2 method is used to assess the performance of the suggested algorithm within the context of the problem's multi-objective function formulation. When compared to the current energy aware scheduling approach, the results show that the offloading method can decrease the amount of time and energy needed to run edge servers, maintain load balancing, and maximize resource utilization while still allowing applications that require delays to meet their deadlines. Finally, simulation results demonstrate the proposed approach outperforms 20% high efficiency compared to the

关键词： Internet of things

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EduNFT: a secure NFT platform for Higher Educational Institutions 6

EduNFT: a secure NFT platform for Higher Educational Institu...

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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Sunil, Sidharth Bhat, Anjum Zameer Abbas, Ali Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Computing and Electronics Engineering Middle East College Muscat Oman

ISBN: (纸本)9798350351538

Web 3.0 technologies such as blockchain, cryptocurrency, and Non-Fungible Tokens (NFTs) are progressively making waves. Despite their adoption in various sectors, their application in the education sector, particularly in Higher Educational Institutions (HEIs), is largely unexplored. Our research proposes the development of a secure NFT platform using blockchain technology, tailored for HEIs. The primary aim of this research is to authenticate rewards, and certificates, and digitalize badges using NFTs, minimizing potential fraud and enhancing data security. Reward NFTs can enhance students' and teachers' career recognition and credit. EduNFT is a ‘first of its kind’ in the Sultanate of Oman in creating a Web 3.0 technology-enabled campus where students are rewarded with digital assets (NFTs) for their achievements by the teachers or the institution over an NFT platform in a secured environment. It runs on the Polygon blockchain which is a "layer two" or "sidechain" scaling solution that runs alongside the Ethereum blockchain - allowing speedy transactions with low fees. The research methodology incorporates an authentication process that requires users to log in via their crypto wallets which enhances security and maintains user privacy. Instead of traditional physical rewards, this digital approach allows achievements to be stored perpetually in the blockchain, reducing risks of loss or damage. The proposed research empowers an impressive expansion of NFT utilization in higher education, contributing to the ongoing growth of the NFT space. The outcome, a pioneering NFT platform powered by Web 3.0 technology, stands to revolutionize the higher education sector, not only in Oman but potentially worldwide. In addition, the research serves as a crucial step toward integrating contemporary technologies into educational systems and furthering their evolution. © 2024 IEEE.

关键词： Smart contract

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Application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and Applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Smart Traffic Control: Adaptive Signal Management Based on Real-time Lane Detection using YOLOv8

Smart Traffic Control: Adaptive Signal Management Based on R...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350382723

As cities grow, handling traffic in big urban are-as becomes a huge proble-m. More cars on the road and not enough roads le-ad to heavy traffic jams. This increases trave-l time and harms our environment. Our study tackle-s these problems with a ne-w approach. We use real-time- lane detection with YOLOv8 and adaptable- traffic lights. Using sharp computer vision, our system pinpoints vehicle-s. It adjusts traffic light timings on-the-go to improve traffic flow. Our model works e-xtremely accurately. During the- learning phase, it achieve-d a mAP50 score of 99.3% and a mAP50-95 score of 87.4%. In the te-sting phase, it got a mAP score of 99.2% and a mAP50-95 score of 86.2%. The re-sults highlight how the system can improve city trave-l. It's valuable for city planners and traffic officials. It helps the-m understand smart transportation systems bette-r. © 2024 IEEE.

关键词： Computer vision

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