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Enhancing Cybersecurity using Machine Learning Strategies for Phishing Defense

Enhancing Cybersecurity using Machine Learning Strategies fo...

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International Conference on Communication and Electronics Systems (ICCES)

作者： C. Jamunadevi S. Pandikumar Naveen N S. Prasath Dhivyashree S Department of Computer Technology PG Kongu Engineering College Perundurai Erode Department of MCA Acharya Institute of technology Bangalore Department of IT Excel Engineering College Komarapalayam Department of Computer Science VET Institute of Arts and Science (Co-education) college Thindal MSc (Software Systems) Kongu Engineering College Perundurai Erode

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Cybersecurity experts continue to be concerned about phishing attacks because it causes a real risk to people, businesses, and economies all around the world. Phishing attacks cause a critical threat to global cybersecurity, necessitating effective classification methods for phishing URL operations. So, this work aims to categorize phishing URL operations into distinct types: deceptive, spear, whaling, and pharming. Leveraging machine learning techniques including logistic regression, support vector machine, Naive Bayes, decision tree, K-NN, and Perceptron, evaluate their efficacy in accurately identifying and classifying phishing attacks. The findings underscore the importance of these models in providing cybersecurity professionals with actionable insights into the diverse tactics employed by malicious actors. Among the evaluated models, logistic regression emerges as the preferred choice, demonstrating superior performance in accurately categorizing phishing URLs. This work contributes to enhancing cybersecurity defenses by offering a comprehensive framework for understanding and mitigating the impact of phishing attacks on digital ecosystems.

关键词： Uniform resource locators Support vector machines Logistic regression Phishing Biological system modeling Ecosystems Machine learning Decision trees Computer crime Business

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Vehiguard Transfer Learning-Driven Quantum PSO-LSTM for Automotive Cyber Security

Vehiguard Transfer Learning-Driven Quantum PSO-LSTM for Auto...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Aarthi D Gowri. J D. Gokila P. Loganathan Rakhi Mutha S. V. Saveeithaa Department of Computer Science and Engineering Karpagam College of Engineering Coimbatore Department of Computer Science Kongunadu Arts and Science College Coimbatore Department of Computer Science Kristu Jayanti College Bengaluru Department of Electronics and Communication Engineering Excel Engineering College Namakkal Department of Information Technology Amity University Jaipur Rajasthan Department of Electronics and Communication Engineering Erode Sengunthar Engineering College Erode

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Quantum-Inspired Particle Swarm Optimization (QPSO) has emerged as a powerful optimization technique that enhances hyper parameter tuning and convergence efficiency in machine learning models. In the context of vehicular network security, integrating QPSO with Long Short-Term Memory (LSTM) networks and Transfer Learning offers a robust framework for detecting cyber threats in dynamic vehicular environments. Traditional anomaly detection mechanisms often struggle with real-time adaptability, leading to reduced detection accuracy and delayed response to evolving attack vectors. The proposed VehiGuard framework leverages QPSO to optimize hyper parameters efficiently, improving LSTM’s ability to detect anomalies in vehicular data streams. Transfer learning further enhances model adaptability by utilizing pre-trained models from related domains, significantly reducing the need for extensive labelled datasets while maintaining high detection accuracy. This hybrid approach ensures a scalable and computationally efficient solution for vehicular cyber security. Empirical evaluations conducted on widely used vehicular datasets demonstrate that VehiGuard outperforms conventional models in terms of detection accuracy, computational efficiency, and robustness against diverse attack vectors. The results highlight the potential of integrating quantum-inspired optimization with deep learning and knowledge transfer to secure autonomous vehicles and IoT-enabled transportation systems. By addressing critical challenges in vehicular network security, VehiGuard contributes to the development of more resilient and adaptive intelligent transportation systems.

关键词： Adaptation models Accuracy Computational modeling Transfer learning Network security Vectors Particle swarm optimization Computer crime Long short term memory Optimization

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Application of Natural Gradient Boosting and Grid Search in Fault Detection and Diagnosis of Electrical Power Grids

Application of Natural Gradient Boosting and Grid Search in ...

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

作者： Laith H. Alzubaidi Prashanth V T. Ilakkia S. Senthil Kumar Devipriya A G Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electrical and Electronics Engineering Excel Engineering College Namakkal India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In recent years, the demand of electrical power grids had increased tremendously since; girds are primary sources for meeting the electricity demand. However, due to occurrence of faults continuously the power grids are failing in providing constant supply. Several methods are employed for detecting the faults but they failed in effectively classifying types of faults. Therefore, this research proposes Natural Gradient Boosting and Grid Search (NGBoost-Grid Search) for detection and diagnosis of electric faults in electrical power grid. Initially, the data is taken from publicly available electric fault line detection dataset. Then, these current signals are pre-processed by using Interquartile Range (IQR), Hilbert Huang Transformation (HHT) and min-max normalization. After that, Elastic Net technique is employed for selecting the optimal features from pre-processed signals for eliminating irrelevant signals. Finally, selected optimal features are classified by using NGBoost for distinguishing the fault current signals and healthy current signals, Grid search is integrated to hyper tune for improving the performance of NGBoost and gave effective classification of fault and normal current signals. The proposed NGBoost-Grid Search achieved attained better results such as accuracy (96.47%), precision (97.63%), Recall (89.94%) and Fl-score (93.29%) respectively when compared with existing Support Vector Machine (SVM).

关键词： Support vector machines Measurement Accuracy Filtering Frequency-domain analysis Fault detection Noise Boosting Power grids Time-domain analysis

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Enhancing Data Transmission Reliability in Cluttered Environments using Adaptive Wireless Sensors 2

Enhancing Data Transmission Reliability in Cluttered Environ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Dinesh, R. Vanathi, P. Susitha, N. Priya, P. Ambikavathi, C. Ali, Syed Sumera Excel Engineering College Department of Ece Namakkal India Adhiyamaan College of Engineering Department of Information Technology Hosur India School of Arts and Science Vinayaka Mission's Chennai Campus Vinayaka Mission's Resarch Foundation Du Dept. of Computer Science India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur India School of Arts and Sciences Vinayaka Mission Research Foundation Department of Computer Science Tamilnadu Paiyanoor India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India

ISBN: (纸本)9798350369960

Wireless data transmission in complicated terrains like cities or deep woodlands sometimes raises dependability concerns. These issues are caused by barriers, multipath fading, and signal interference. Our study uses adaptive wireless sensors to improve data transmission reliability to solve this problem. These sensors are meant to dynamically modify gearbox parameters in real time, unlike standard sensors. This distinguishes them. The study uses key research methods. Dynamic Path Selection chooses the best transmission paths for data packets. Adaptive Modulation and Coding (AMC) alters modulation and coding rates based on real-time environmental information. The study also uses Spatial Diversity using Beamforming. This method concentrates energy using multi-antenna systems, increasing signal intensity and decreasing interference. Practical experiments in clutter-filled regions showed improved gearbox reliability. Compared to standard systems, adaptive sensors reduced packet loss by 45% and increased throughput by 30%. Adaptive wireless sensors increase connection reliability in difficult conditions. These sensors may be utilised for many applications, including urban IoT frameworks and autonomous navigation. © 2023 IEEE.

关键词： Cluttered Environments Data Transmission Dynamic Path Selection Wireless Sensors

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Generative Adversarial Network Based Real-Time Malware Detection in Cloud Environments

Generative Adversarial Network Based Real-Time Malware Detec...

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

作者： Srinivasa Rao Bittla Abothar Mahmood D. Kokila Ayesha Siddiqua Ajitha P R Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Artificial Intelligence & Data Science Excel Engineering College Namakkal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Recently, the rapid growth of cloud computing has created a vast attack surface for malware threats. The existing Support Vector Machine (SVM) accurately detected malwares although the dynamic nature of cloud environments led to high-dimensional data and class imbalance issues. Hence, this research proposes a Generative Adversarial Network (GAN) to address the class imbalance by creating synthetic malware samples. Initially, the input data is collected and fed into the analysis phase with Cuckoo Sandbox to generate malware behavior report in JavaScript Objective Notation (JSON) format. After that, Trend Micro Locality Sensitive Hashing (TLSH) is employed to measure the similarity among files and then the clustering phase groups the similar files as a cluster according to hash values. Further, the features are extracted from JSON format which contains information on Application Programming Interface (API) calls and their return codes. Then, the optimal features are selected using Chi-square to calculate the statistical independence on each feature and finally, GAN model is used to detect malware in cloud environments. From the results, the proposed GAN achieved better results in terms of accuracy (99.61%) when compared to existing Artificial Neural Network (ANN).

关键词： Support vector machines Cloud computing Codes Computer viruses Artificial neural networks Generative adversarial networks Feature extraction Malware Real-time systems Application programming interfaces

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Machine Learning-Driven Hybrid Adaptive Beamforming for Efficient Interference Mitigation in Dense Wireless Sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

引用

Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： P. Loganathan A.T. Priyeshkumar K. Kodeeswari K Jose Reena I. Poonguzhali M. Balaji Department of Electronics and Communication Engineering Excel Engineering College(Autonomous) Namakkal Department of Electronics and Communication Engineering Mahendra College of Engineering Salem Department of Electronics and Communication Engineering Kongu Engineering College(Autonomous) Erode Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Vivekananda College of Technology for Women Tiruchenhode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The Adaptive Beamforming Algorithm (ABA) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WSNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency.

关键词： Wireless sensor networks Adaptive systems Array signal processing Scalability Prevention and mitigation Interference Throughput Real-time systems Energy efficiency Signal to noise ratio

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Mental Healthcare Chatbots: Technological Advancements, Challenges, and Opportunities

Mental Healthcare Chatbots: Technological Advancements, Chal...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Amirthavarshini H Suresh Subramanian Chokiyan Karthikeyini Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Mental healthcare chatbots are artificially intelligent systems that have been built with the intent of simulating human conversation and provide emotional support to one’s user. They are also widely used as tools to help individuals cope with the stress, anxiety, and other mental health challenges they have been facing, including easily accessible and affordable solutions. These chatbots use NLP and emotion recognition technology to assist users in understanding their feelings and responding accordingly. This review unpacks the latest advancements of recently developed chatbots, such as GPT-4-based models, BERT and MBERT based models, sentiment analysis, and the study of multimodal systems combining text, voice, and facial expression recognition. Also addressed are important challenges, including privacy concerns, cultural inclusivity, and clinical validation. Finally, the review identifies opportunities to improve chatbots, including supporting multi-language and primary telehealth platforms and customizing user interaction with chatbots to create more inclusive and effective mental health support worldwide.

关键词： Emotion recognition Analytical models Sentiment analysis Reviews Text recognition Telemedicine Medical services Mental health Speech recognition Chatbots

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A Comprehensive Review of Real-Time Sign Language Interpretation Using Various Machine Learning Models

A Comprehensive Review of Real-Time Sign Language Interpreta...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Maneesh J J Chokiyan Karthikeyini Suresh Subramanian Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Real-time Sign Language interpretation is among the fastest-growing fields that can potentially minimize the communication gap between deaf people and the hearing population and prepare the ground for inclusivity and accessibility throughout social, educational, and professional contexts. The review discusses the development of technologies for sign languages interpretation in real time, having the main emphasis that have to be met such as high interpretation accuracy, low latency, and computational efficiency. This review methodology, which compared machine learning and deep learning algorithms and discussed different model architectures, analyzed the trends and emerging methodologies that have shaped the current state of the field and identified significant gaps in the literature. In particular, the tendency is such that the multimodality involving hand, face, and body movement participation is becoming increasingly more effective; the pre-trained models get adapted to perform sign language tasks. Most importantly, diversity in datasets promotes model robustness across languages and dialects. A bit further, practical problems are discussed with real-time interpretation deployment on mobile devices and other resource-limited platforms. The following review is designed to point out the likely best directions for future research and development that would generalize, through the advancement of real time sign language interpretation, to become an accessible and reliable technology for everyday use.

关键词： Deep learning Sign language Adaptation models Accuracy Recurrent neural networks Translation Reviews Real-time systems Convolutional neural networks Optimization

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Machine Learning Based Skin Disease Analyzer with Image Processing

Machine Learning Based Skin Disease Analyzer with Image Proc...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Nivedita, V. Subramaniam, Kamalraj Ramya, M. Parameshachari, B.D. SRM Institute of Science and Technology Department of Computer Science and Engineering Trichy621105 India Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore641021 India Excel Engineering College Department of Electronics and Communication Engineering Namakkal637303 India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665453424

People may acquire skin illnesses as a consequence of hereditary factors, the natural ageing process, hormones, allergic responses, exposure to the sun or hazardous chemicals, and other environmental variables. Skin disorders are among the most prevalent and pervasive diseases in the world. The diagnosis is exceedingly challenging despite the fact that this condition is rather common since there are slight changes in skin tone, colour, and the presence of hair. When it is in its earlier phases, individuals sometimes fail to recognise the impacts of skin diseases. At the moment, dermatologists diagnose skin conditions by performing a sample, which is subsequently analysed, and then manually give drugs to patients. In this research, we offer a technique for the diagnosis of skin diseases that is based on image processing, Python, and the Yolov3 tool. Our goal is to eliminate the need for physical examination and to deliver reliable findings in a fast way. An picture of the portion of the patient's skin that is infected may be sent as an input to the system by the patient. On this picture, several image processing methods are used, and feature values are retrieved and located, in addition to the illness that is under investigation. The suggested approach is particularly helpful in geographical locations with a limited number of dermatologists. Acne, melanoma, blisters, and cold sores are the four forms of skin diseases that are discussed in this article, along with a technique for diagnosing each of them. The fundamental objective of this approach is to attain the greatest degree of accuracy in the prediction of skin diseases as is possibly conceivable. © 2022 IEEE.

关键词： Python

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Diagnosing Alzheimer Disease Using MRI Scan: A Deep Learning Approach

Diagnosing Alzheimer Disease Using MRI Scan: A Deep Learning...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S. Saravanan K. Muthumanickam N. Subha P.C.Senthil Mahesh Department of CSE-Data Science Dayananda Sagar Academy of Technology and Management Bengaluru India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Thottiyam Tamilnadu India Department of Computer Science and Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The neurodegenerative disease known as Alzheimer's disease (AD), which affects people worldwide, is complicated to treat and expensive. Conventional approaches, including manual Support Vector Machine (SVM) based methods with separate feature extraction and preprocessing, lack automation and pose limitations. This project recognizes the pressing need for efficient and automated solutions. Building on existing work that employed SVM and manual processing for Alzheimer's diagnosis, this work proposes a novel approach. Utilizing brain MRI scans and the VGG16 architecture, our automated system achieves high accuracy in predicting both labeled and unlabeled data. The integration of VGG16 ensures a streamlined process, eliminating manual intervention in feature extraction and preprocessing. This automation enhances accessibility, reduces costs, and offers a user-friendly interface for users. In contrast to traditional methods, our proposed system allows users to input MRI scans, facilitating prompt and accurate predictions. Upon analysis, the proposed system provides doctor recommendations and suggests prescriptions. The VGG16-based approach not only ensures superior diagnostic accuracy but also streamlines the entire process, emphasizing the importance of early detection for improved outcomes in Alzheimer's disease management.

关键词： Support vector machines Accuracy Automation Magnetic resonance imaging Manuals Pressing Streaming media

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