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Enhancing the Efficacy of Machine Learning-Based Intrusion Detection Systems on an Imbalanced and Contemporary Dataset

Enhancing the Efficacy of Machine Learning-Based Intrusion D...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： A. Kiruthikkumar N. Uma Maheswari D. David Neels Ponkumar A. Niyas Ahamed S. Nazrin Salma R. Saravanakumar Department of Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics Engineering (VLSI Technology) R.M.K. Engineering College Thiruvallur Tamil Nadu India General Engineering R.M.K. College of Engineering and Technology Thiruvallur Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

As the Internet has increased in popularity, more computers are networked and becoming part of our daily lives. Common and less evident server vulnerabilities may be used by hackers to access systems and conduct complicated assaults. An Intrusion Detection System (IDs), a popular computer security tool, uses machine learning on a previously acquired dataset. The datasets were generated in distinct networks across brief periods and may not contain the latest information. They're bloated and can't hold enough data to survive an attack. Modern intrusion detection systems are less effective with inconsistent and outdated information for rare attack paths. This paper presents five machine-learning intrusion detection solutions. K Nearest Neighbor (KNN), Decision Tree (DT), Gradient Boosting (GB), Adaboost (AB), and Random Forest (RF) are used. The latest security dataset CSE-CIC-IDS2018 replaces earlier, more often-used datasets to improve Intrusion Detection Systems (IDS). An unbalanced dataset was selected. The imbalance ratio is reduced using the Synthetic Minority Oversampling Technique (SMOTE), a synthetic data model. The approach increases attack type-specific system performance while lowering false alarms and missing incursions. Minor classes increase their data amounts to reach the average data size. Experimental results demonstrate that the recommended strategy greatly enhances uncommon intrusion detection.

关键词： Radio frequency Adaptation models Runtime Intrusion detection Nearest neighbor methods Feature extraction Boosting Decision trees Random forests Testing

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Facial Emotion Recognition Using a Local Binary Pattern based Deep Learning

Facial Emotion Recognition Using a Local Binary Pattern base...

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Computer, Communication and Control (IC4), International Conference on

作者： Iftikhar Aslam Tayubi Dilip N. Pawar Ajmeera Kiran Pundru Chandra Shaker Reddy Nipun Sharma D Chitra Faculty of Computing and Information Technology Rabigh King Abdulaziz University Jeddah Saudi Arabia Instrumentation Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashrra India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Electronics and Communication Engineering Presidency University Bangalore Karnataka India Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-Pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders.

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Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases

Implementing Machine Learning for Early Detection and Progno...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： J L Mazher Iqbal Omprakash Gurrapu Saritha P S Rajesh Kanna R R. Jayanthi Tejaskumar MB Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Volvo Trucks North Carolina USA Department of Artificial intelligence and data science Dhanalakshmi Srinivasan College of Engineering Coimbatore India Department of Computer Science Christ University Bangalore India Department of Master of Computer Applications Dayananda Sagar College of Engineering Bengaluru India Don Bosco Institute of Technology Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, Random Forest, XGBoost, and CNN to forecast the occurrence of the. Due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care.

关键词： Neural networks Predictive models Data models Chronic kidney disease Diabetes Convolutional neural networks Cardiovascular diseases Prognostics and health management Random forests Cancer

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Development of Lightweight and Cheaper 5G Mobile Communication System to Analyze the Performance of Espresso Ciphers and Grain Family 2nd

Development of Lightweight and Cheaper 5G Mobile Communicati...

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2nd International Conference on Cyber Warfare, Security and Space Computing, SpacSec 2024

作者： Kamalanathan, C. Balamurugan, J. Sharma, Neelam Reddy, A. Basi Selvan, R. Senthamil Department of Electrical Electronics and Communication Engineering GITAM Deemed to Be University Bengaluru Campus Karnataka Bengaluru India Department of Master of Business Administration St.Joseph’s College of Engineering OMR Tamilnadu Chennai India Department of Physical Education Lovely Professional University Punjab India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Annamacharaya Institute of Technology and Sciences Andhra Pradesh Tirupati India

ISBN: (纸本)9783031734939

A significant difficulty in a resource-constrained setting is keeping an optimal sense of balance among safety and other presentation parameters such as memorial need, energy requirement and throughput. In its most recent study on lightweight cryptography, the National Institute of Standards and Technology (NIST) recommended symmetric cyphers for devices with little resources. This work presents statistical security studies of six modern stream ciphers: Espresso, Grain v1, Sprout, Fruit, Plantlet, and Lizard. The analyses were conducted using the following tests: avalanche, randomness, structure, and autocorrelation. After porting the improved code to a microcontroller in low-cost, that is the ATmega 328P, they have also conducted a thorough performance study of these ciphers. A device’s suitability for use in an IoT-based network is a deciding factor in its selection. According to statistical security, performance measurements, and comparative analysis, the chosen ciphers are good choices for situations with limited resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 5G mobile communication systems

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Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases

Implementing Machine Learning for Early Detection and Progno...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Iqbal, J. L. Mazher Gurrapu, Omprakash Saritha, P.S. Rajesh Kanna, R. Jayanthi, R. Tejaskumar, M.B. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India Volvo Trucks NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Christ University Department of Computer Science Bangalore India Dayananda Sagar College of Engineering Department of Master of Computer Applications Bengaluru India Don Bosco Institute of Technology Bangalore India

ISBN: (纸本)9798331538538

关键词： Diseases

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Intelligent and Smart Health Care Monitoring System based on Deep Convolutional Neural Network Model

Intelligent and Smart Health Care Monitoring System based on...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： G. Anitha R. Sankar A. Vimala Juliet V. Dhivya B. Thiyaneswaran Ata Kishore Kumar Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Department of Master of Computer Applications S.A. Engineering College Chennai Department of Electronics & Instrumentation Engineering SRM Institute of Science & Technology Chennai Department of ECE Vel Tech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of ECE Sona College of Technology Salem Department of ECE School of Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh

Remote health monitoring apps have revolutionized the healthcare industry as a whole with the advent of Internet of Things (IoT) technology. Strategies used to get insights through the analysis of lifestyle patterns as well as behaviors are also crucial to tailored healthcare and preventative services. Opportunities for both the investigation of illnesses and the forecasting of abnormal health situations are made possible by the use of intelligent data retrieval as well as categorization methods. The broad use of IoT has improved telemedicine capabilities and introduced more precise remote patient monitoring, both of which have had a favorable effect on the world at large. The impact of the Internet of Things (IoT) becomes deeper as it enhances healthcare infrastructure for patients, doctors, and hospitals. Through the usage of secure patient portals, healthcare providers have easier access to up-to-date patient information, allowing for more precise diagnosis and treatment. The prototype's potential for efficient healthcare monitoring is reinforced by the success of similar systems. In this research, we automated the AlexNet Model building process by experimenting with several approaches. In addition, a whole new method of diagnosis has been created. The experimental data shows that the suggested algorithm is superior to the state-of-the-art methods. The total accuracy of the suggested model is 97.13%, which is significantly higher than the accuracy of any of the other models compared. This demonstrates the model's efficiency and places it as a potentially game-changing breakthrough in healthcare.

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Nico Surantha Vito Vincentdo BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Department of Electrical Faculty of Scienc and EngineeringElectronics and Communication Engineering Tokyo City University Japan

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined.

关键词： Fuzzy logic Training Actuators Adaptive systems Hydroponics Media Control systems

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An Adaptive Deep Learning Framework for Prediction of Agricultural Yield

An Adaptive Deep Learning Framework for Prediction of Agricu...

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

作者： K Rama Rao Manthena Swapna Kumari Ravindra Eklarker Pundru Chandra Shaker Reddy Kirti Muley Vijay Kumar Burugari Electronics and Communication Engineering Geethanjali College of Engineering and Technology Hyderabad Telangana India Department of CSE(AIML &IoT) Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of AI &DS Vardhaman College of Engineering Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Master of Computer Applications Bharati Vidyapeeth's Institute of Management and Information Technology Navi Mumbai India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

Agriculture and its large yields are vital to human survival. Recently, machine learning and deep learning have been applied to determine agricultural output. This study estimated apple production utilizing a unique deep-learning(DL)-based technique. A study-designed convolutional neural network (CNN) classified golden and royal gala apple photos. Classifying CNN-extracted pictures with traditional machine-learning(ML) methods was a hybrid application. Datasets A is used to evaluate the performance of the proposed model. Based on Dataset A accuracy findings, the CNN-SVM hybrid model performed best. CNN-SVM and CNN-Gradient Boosting hybrid models performed best and accurately for Dataset A. The more balanced distribution of train, test, and validation sizes, the suggested hybrid CNN model, and the model's assessment made dataset C the best choice. The hybrid model was 99.70% accurate for Dataset A. Precision, recall, f-measure, and Cohen kappa were 99%. The study found that hybrid models could accurately predict apple fruit yield using golden and royal gala photos.

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Step-by-Step Image Encryption Using UACI and PixAdapt 2nd

Step-by-Step Image Encryption Using UACI and PixAdapt

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2nd International Conference on Cyber Warfare, Security and Space Computing, SpacSec 2024

作者： Balamurugan, J. Yadav, Mali Madhavi, Jetti Reddy, A. Basi Selvan, R. Senthamil Department of Master of Business Administration St. Joseph’s College of Engineering OMR Tamilnadu Chennai India Department of Physical Education Lovely Professional University Punjab India Department of Mathematics Malla Reddy Engineering College Medchal Hyderabad India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Annamacharaya Institute of Technology and Sciences Andhra Pradesh Tirupati India

ISBN: (纸本)9783031734939

A new cutting-edge method recently garnered interest is image encryption utilizing a genetic approach. Currently, most genetically based picture encryption algorithms use fixed parameters to encrypt images, ignoring any image-representative properties. This research led to PixAdapt, a novel adaptive picture encryption technique. It is being re-engineered such that the fitness of the encrypted picture can be determined using UACI, and the relevant parameters may be adjusted using hereditary hill climb or pretend annealing. Pseudorandom numbers have been generated using a variety of chaos-based maps, including the Logistic, Rossler, Henon, and Tent maps, as well as the linear feedback shift register. The PixAdapt algorithm uses the theory of confusion and diffusion to further ensure that the plaintext and ciphertext images are completely dissimilar. This is the first instance of optimizing picture encryption settings using metaheuristic search algorithms. Finding the best possible UACI value, the hereditary hill climb algorithm encrypts all of the photos. Fitness enhancement, limit evolution, arithmetical analysis, and encryption quality testing have all been conducted on the method. When it comes to effectively encrypting images, PixAdapt is one of a kind and has shown that the UACI encryption parameter is a good match. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Shift registers

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