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Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

Intelligent Automation & Soft Computing 2023年第6期36卷 3043-3056页

作者： S.Ashwini S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia

Increase in the use of internet of things owned devices is one of the *** publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing *** Phishing is the slow and resilient attack stacking techniques probe the *** proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm(NWAT).Though Predictive mapping,Predictive Validation and Predictive analysis mechanism is *** the test purpose Canadian Institute of cyber security(CIC)dataset is used for creating a robust prediction *** attack generates a resilience corruption works that slowly gathers the credential information from the *** proposed Predictive analysis model(PAM)enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT *** prediction system considers the peer-to-peer communication window open for the established communication,the suspicious process and its pattern is identified by the new *** proposed model is validated by finding thepredictionaccuracy,Precision,recallsF1score,errorrate,Mathew’sCorre-lationCoefficient(MCC)andBalancedDetectionRate(BDR).Thepresented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.

关键词： Cyber security internet of things phishing attacks fault-tolerant devices smart devices cyber security attacks

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Real-Time Bitcoin Cost Identification to Improve Efficiency Using Lasso Regression in Comparison with Decision Tree

Real-Time Bitcoin Cost Identification to Improve Efficiency ...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Pavankumar, Gajula Velmurugan, J. Padmakala, S. Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Saveetha University Chennai India

ISBN: (纸本)9798350358926

Enhancing the Bitcoin cost prediction to improve accuracy using a decision tree in comparison with Lasso regression. For trend confirmations, we often utilize the financial phrases support and resistance to the prediction. Materials and Methods: The price of bitcoin is forecasted using a straightforward prediction technique called Lasso regression. The effectiveness of each model's performance on the task was evaluated, and the findings were examined. The goal of testing so many different models was to examine how the fundamental hypotheses of each of them varied. We used Lasso regression together with the best parameters to weigh the input variables because it is a reliable technique. Lasso regression is a Model based on a sample T-test with a value of p is 0.000 (Independent sample t-test p © 2024 IEEE.

关键词： Forecasting

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Efficiency Unleashed by Examining Response Time Enhancement for Student Club Activities in iOS App and Web Application 5

Efficiency Unleashed by Examining Response Time Enhancement ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Venkata Indrasena Reddy, S. Surendran, R. Logapriya, E. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The aim of this research is to analyze and enhance the response time of digital platforms, including both mobile apps and web applications, dedicated to student club activities. Materials and Methods: A sample size of N=12 is used for testing both the web and iOS applications. To guarantee 80% statistical power, sample sizes are calculated using G Power. Results: In contrast to the IOS application, which responds in 27.33 ms, the WEB application has a faster accuracy rate at 21.00 ms. Considering that the two have a p-value of 0.003 (p © 2024 IEEE.

关键词： Accuracy Statistical analysis Market research Mobile applications Time factors Intelligent systems Testing

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A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

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Intelligent Automation & Soft Computing 2023年第6期36卷 3101-3119页

作者： P.Shanmuga Prabha S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia

Today,securing devices connected to the internet is challenging as security threats are generated through various *** protection of cyber-physical systems from external attacks is a primary *** presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted *** proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis(MEDA)through Principle Component Analysis(PCA)and Singular Value Decompo-sition(SVD)for the extraction of unique *** feature mappings are analyzed with Recurrent 2 Convolutional Neural Network(R2CNN)and Gradient Boost Regression(GBR)to identify the maximum *** Late Fusion Aggregation enabled with Cyber-Net(LFAEC)is the robust derived *** quantitative analysis uses predicted threat points with actual threat variables from which mean and difference vectors *** performance of the presented system is assessed against the parameters such as Accuracy,Precision,Recall,and F1 *** proposed method outperformed by 98% to 100% in all quality measures compared to existing methods.

关键词： External attacks cyber-physical systems principle component analysis singular value decomposition recurrent 2 convolutional neural network gradient boost regression

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5G-Enabled Remote Healthcare Monitoring for Improved Patient Care

5G-Enabled Remote Healthcare Monitoring for Improved Patient...

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

作者： Vimal, V.R. Jayalakshmi, D. Narayanan, Lakshmi Kanthan Hemavathi, R. Loganayagi, S. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Thandalam Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

The implementation of 5G technology boosts the possibility of the remote monitoring in healthcare to the above and beyond while providing new capabilities like data transmission speed, connectivity, and reliability. The contribution of this study aims to cover the application of 5 G network in healthcare monitoring systems in order to improve patient care, whereby, it considers integration of devices, real time data processing, AI and machine learning, security, scalability and telemedicine. Our approach covers setting up an advanced focus network propose, adopting IoT based health protocols devices, employing edge process, combining with AI purpose of predictive model, and consuming advanced security protocols. Stepping stone trial proved that patient response time, alert accuracy, user satisfaction, as well as data processing performance is greatly enhanced. Implementation AI/ML features increased predictive accuracy and improving capability to detect anomalies thus improving the overall robotic control systems. These results display imposing enhancement in effectiveness, steadiness, and scalability of a system. At the same time, we present this system with the highest level of data security and privacy. Integrative orientation pinpoints 5G-emboldened remote health-care monitoring as one of the major ways to deliver effective and streamlined patient care. © 2024 IEEE.

关键词： 5G mobile communication systems

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IoT-Driven Energy Consumption Optimization in Smart Homes 2

IoT-Driven Energy Consumption Optimization in Smart Homes

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Loganayagi, S. Hemavathi, R. Jayalakshmi, D. Vimal, V.R. Kanthan Narayanan, Lakshmi Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha University Saveetha School of Engineering Department of Cse Thandalam Chennai India Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Smart homes that optimize energy use have emerged from the Internet of Things (IoT). IoT-driven energy optimization in smart homes has several benefits, as this study shows. Our study found that IoT-driven energy consumption optimization can save smart homes 15% of their energy use. This reduction saves homes $150 a year on energy expenditures. These savings also reduce CO2 emissions by 500 kg, meeting sustainability targets. A staggering 90% of users are satisfied with IoT-driven energy optimization. The usercentric approach allows homeowners to customize their energy choices, as 70% of users do so. The study also shows that these systems meet 80% of customers' energy goals. IoT-driven systems anticipate energy usage with 95% accuracy, demonstrating their reliability. The excellent 98% device compatibility ensures easy device connection. The study also shows that privacy and security safeguards work, with no data breaches. With a 500 kg CO2 reduction, IoT-driven energy usage optimization has a significant environmental impact. A 20% energy waste reduction over five years is expected for long-term sustainability. Up to 10% of energy is renewable, contributing to a greener future. © 2024 IEEE.

关键词： Smart homes

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Enhancing Accuracy in Image Recognization for Deepfake Images Using Convolutional Networks Comparing with K-Nearest Neighbors (KNN) Algorithm

Enhancing Accuracy in Image Recognization for Deepfake Image...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Chanakya, A.S. Surendran, R. Madhusundar, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

The primary goal of this research is to improve the accuracy of deepfake picture recognition using Convolutional Neural Networks (CNN) and compare the results to a K-Nearest Neighbors (KNN) approach. This entails researching approaches like transfer learning, feature extraction, and model evaluation to increase the robustness and efficiency of deepfake detection systems. For the training and evaluation phases, a Kaggle dataset with both authentic and deepfaked photos was used. The dataset included samples from several deepfake generating techniques and scenarios. This work involved the implementation of two independent groups, each applying different algorithmic approaches for picture identification. Group 1 used Convolutional Neural Networks (CNNs), while Group 2 used a neural network design with the K-Nearest Neighbors (KNN) algorithm. Each group had 20 samples, for a total sample size of 40. This sample size was chosen to ensure enough statistical power for subsequent studies. *** was used for a priori power analysis, and the statistical power (G-power) was set at 80%. The chosen significance level (alpha, α) was 0.05, and the type II error rate (beta, β) was 0.2, A 95% confidence interval was selected to assess the precision of the estimated effectiveness of the two algorithmic approaches. Result: The accuracy of the K-Nearest Neighbors (KNN) algorithm was 72.8%, whereas the accuracy of the Convolutional Neural Networks (CNNs) was 97.15%. A two-tailed significance test confirmed the superiority of the CNN model over the KNN model, with a statistically significant p-value of 0.001 (p 0.05) for accuracy. © 2025 IEEE.

关键词： k-nearest neighbors

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Real-Time Violence Recognition in CCTV Video Surveillance Enhanced by AI Deep Technique 6

Real-Time Violence Recognition in CCTV Video Surveillance En...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Appavu, Narenthirakumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798331522667

Despite its many uses, deep learning (DL) is a key tool in the machine learning (ML) community’s toolbox. Numerous techniques have been developed to identify violence and criminal activity on video since many crimes take place in public areas that are not adequately monitored. The significance of automatic violence detection in surveillance video studies has grown. They do, however, have several drawbacks and frequently rely on particular circumstances. This article presents a method for detecting aggression in surveillance films that is based on convolutional neural networks (CNNs). Accuracy is increased by applying both deep learning and machine learning methods as advised. Performance assessments showed how effectively the suggested approach identifies violent content in video. Test results show that the future solution works better than current approaches for classifying violent then criminal material in films. The models that were previously trained InceptionV3, ResNetV2, Inception_ResNetV2, Violence_Net, and the suggested vioNet remained trained on four datasets. The following were the classification outcomes of the endorsement data for each model: VioNet model 99.72%, InceptionResNetV2 90%, ViolenceNet pseudo-97, and InceptionV3 92%. Due to its easier integration of feature maps, the Dense_Net prototypical was selected for our application arrangement instead of the more intricate Inception or InceptionResNet models. Compared to earlier iterations, its sturdy construction provides greater accuracy and performance with fewer filters and settings. © 2025 IEEE.

关键词： Convolutional neural networks

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Algorithmic Innovations in Machine Learning for Drug Discovery and Pharmaceutical Innovations 2

Algorithmic Innovations in Machine Learning for Drug Discove...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sangeetha, M. Harin Fernandez, F. Mary Ganesh Ramachandran, A. Saravanan, S.K. Bhanumathi, M. University of Technology and Applied Sciences Department of Computer Engineering Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats BigData and Network Security Department of Computers Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Algorithmic advancements in machine learning have transformed drug discovery and pharmaceutical innovation. This study examines how machine learning might speed medication development and pharmaceutical innovation. Data from genomes, chemistry, and clinical trials is collected to start the research. Careful data cleansing, normalization, and feature engineering prepare the data for model building. Random Forest, SVM, NN, Decision Tree, and Logistic Regression are tested for prediction. ROC AUC results show that Random Forest is the most accurate model at 86%. Ensemble approaches have drug discovery potential. Beyond accuracy, models are assessed for precision, false positive, and false negative rates. Drug discovery relies on precision to measure positive predictions. The Random Forest model makes the most accurate positive predictions at 0.87. However, false positives and negatives also matter in drug discovery. The Decision Tree model contains the falsest positives (35), underlining the need to reduce them to save resources. The Neural Network approach reduces false negatives to 12, ensuring good medication candidates are not overlooked. The results show that different machine learning models have trade-offs, underscoring the necessity for a personalized strategy depending on objectives and restrictions. Algorithmic breakthroughs in pharmaceutical innovation reduce medication development time by 40% and cost by 25%, according to the study. © 2024 IEEE.

关键词： Drug discovery

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Machine Learning Prognosis for Smartphone Dependency 5

Machine Learning Prognosis for Smartphone Dependency

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Prathima, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Concern over smartphone addiction has grown recently as more and more people show signs of addiction, including compulsive phone use, decreased productivity, and even physical and mental health issues. Consequently, a growing necessity exists for the creation of efficient tools to forecast smartphone addiction and pinpoint individuals susceptible to this behavior. The proposed model is crafted to predict smartphone addiction using data collected from a survey of individuals who use smartphones. The survey encompassed inquiries related to demographics, phone use patterns, and various psychological aspects like anxiety, depression, and stress. a popular and effective machine learning method, to build our model. After pre-processing the data, the model is trained and its performance assessed by its accuracy, to see whether the model could be learned efficiently. Our findings demonstrated how effective the recommended treatment was at preventing smartphone addiction. Key features crucial for predicting addiction included patterns of phone use, such as the frequency of checking notifications, daily hours spent on the phone, and the predominant types of frequently used apps. Other important factors included age, gender, and stress levels. Our designed model has multiple possible uses. The solution finds its usage by healthcare professionals to identify individuals prone to potential risks of developing smartphone addiction and provide appropriate interventions. It could also be used by app developers to design apps that are less addictive and promote healthier phone use habits. © 2024 IEEE.

关键词： addiction healthcare machine learning prediction smart phone

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