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A Threat modeling approach to analyze and mitigate WhatsApp attacks: A Review

A Threat modeling approach to analyze and mitigate WhatsApp ...

引用

2023 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2023

作者： Patidar, Pawan Kumar Tomar, Deepak Singh Pateriya, R.K. Sharma, Yogesh Kumar Maulana Azad National Institude of Technology Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350398748

One of the most popularly used features on smart-phones is WhatsApp. It is a free messaging app available for An-droid, IOS, and all other smartphones. A WhatsApp vulnerability is a hole or a weakness in the app, which can be a design flaw or an implementation bug. Through bug, an attacker can enter into the app and cause harm to the database of an application. A database contains private information like backup files, chatting information, contacts, etc. The most dangerous vulnerability in WhatsApp are Authentication, Account Hijacking, and Message Manipulation. In these vulnerabilities, an attacker can manip-ulate the message but not hijack the entire account. There are some basic requirements for a secure and privacy-preserving chat service, database backup, encrypted database, etc. The awareness about the vulnerability of WhatsApp and its security settings in new versions of WhatsApp. Use WhatsApp security features and secure the services of the application. This paper aims to be aware of the risks and vulnerabilities of WhatsApp and use the Threat modeling method to mitigate its vulnerability. It used threat Modelling steps to help organizations to quantify risks and vulnerabilities, ensuring those that need the most attention and resources do so to minimize their attack surface in a purposeful way. © 2023 IEEE.

关键词： Smartphones

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Botanical Bio Vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

引用

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

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. This study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. This approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. This research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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Fraud Detection in Financial Transactions using Machine Learning - A Comparative Study 3

Fraud Detection in Financial Transactions using Machine Lear...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jayanthkumar, Avati Sathwik, Mayakuntla Sai Reddy, Shagam Srinivasa Kumar, Donadula Mounish Samir, Chougule Saish Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Vadodara India

ISBN: (纸本)9798331518592

Data analysis techniques using machine learning approaches are the most prominent when we are considering the online fraud, it may be a credit card fraud or any other online fraud in the banking system. Using Machine Learning techniques, we can easily manage the information of fake transactions by extensive data analysis of time, location and amount in the real time. This paper gives the comprehensive study of the changing scene of the fraud discovery in monetary exchanges, investigating the most recent patterns, advances, and techniques utilized by financial institutions and other administrative bodies. An extensive review of various machine learning techniques has been done to find out the effective solution to protect the online fraud. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey on Eliminating Botnet and Intrusion Attacks through Machine Learning

A Survey on Eliminating Botnet and Intrusion Attacks through...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Faseela, M.H. Manjith, B.C. Dept. Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9798350325188

With the rising linkage and dependence on computer networks, the threat of intrusions and botnets has become a critical concern for organizations and those involved. Malicious actors are in control of unauthorized access or actions that harm the security and integrity of computer systems and insecure devices. The identification of attacks and botnets is of utmost importance in ensuring the security of networks and safeguarding confidential information. In recent years, there has been a growing interest among researchers and organizations in developing unique evaluation methods that can adapt to emerging threats and apply intelligent algorithms such as sparse modelling, transfer learning, distributed computing, or meta-learning capable of detecting, classifying, and predicting previously unknown attack patterns, which can result in machine learning and deep learning models that are more accurate and efficient. This review paper compares the performance of machine learning and deep learning approaches such as Naïve Bayes, Support Vector Machines (SVM), and Logical Regression for intrusion and botnet attacks in various network platforms. This paper provides a comprehensive comparison of machine-learning approaches used in the cyber realm for botnet and intrusion detection and encompasses an analysis of the datasets utilized, the specific cyber issues addressed, and the resultant outcomes. Furthermore, it offers valuable insights into the challenges and advancements within the realm of cybersecurity, thereby facilitating the development of novel approaches and technologies aimed at enhancing digital safety. © 2023 IEEE.

关键词： Botnet & Intrusion detection Cybersecurity Machine learning & Deep learning Techniques

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Optimized Approach for Video Summarization using Transfer Learning and LSTM

Optimized Approach for Video Summarization using Transfer Le...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Dhiman, Abhishek Deshmukh, Maroti National Institute of Technology Uttarakhand Dept. of Computer Science and Engineering Srinagar Garhwal India

ISBN: (纸本)9798350323917

Video content has been increasing at a very high pace, so the need for summarizing videos is of urgent need. Video summarization emphasizes on quick go-through of video content. In the last few decades, the field of video summarization has inspired a lot of research. Many of the techniques have been proposed by different researchers, but most of them are not able to create summaries of general videos, i.e., they are generally suitable for a set of categories of videos. To overcome this problem, we have introduced a general framework for video summarization, where we have used a pre-trained VGG16 model for feature extraction, and based on the features, we found the frame level importance using a Long Short Term memory (LSTM) network. After that, we selected the frames with high frame-level scores and finally combined them to form the video summary. Experimenting with the different types of videos taken from the different datasets shows that the proposed methodology outperforms the existing state-of-the-art methods. © 2023 IEEE.

关键词： Long short-term memory

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

引用

2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Madanian, Samaneh Dai, Sherry Natarajan, Priyadarshini Rasoulipanah, Hamidreza Auckland University of Technology Dept. of Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350341072

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large. © 2023 IEEE.

关键词： Risk assessment

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Healthcare Insurance Fraud Prediction with Correlation based Forward Feature Selection 2

Healthcare Insurance Fraud Prediction with Correlation based...

引用

2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Rath, Sushma Panigrahi, Suvasini Veer Surendra Sai University of Technology Dept. of Computer Science and Engineering Burla India

ISBN: (纸本)9798350344882

Health insurance serves as a vital safety net for individuals of various economic backgrounds in contemporary society. Enormous financial losses and loss of life have occurred throughout history due to healthcare fraud. The primary motive for fraudsters engaging in healthcare fraud is financial gain. Fraudsters can be doctors, patients, insurance companies, or combinations of these entities, including collaborations between doctors and patients, patients and insurance companies, and more. In this work, various phases of machine learning pipeline are executed, including collecting data, preparing it, building models, and evaluating their performance. The paper presents a novel combination of correlation-based and forward feature selection based on feature importance techniques to enhance overall performance. In this study, a comprehensive comparison of various supervised learning algorithms is conducted, namely Logistic Regression (LR), Decision Tree Classifier (DT), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The Random Forest approach yielded the highest accuracy, achieving a remarkable mean accuracy rate of 93.83%. © 2023 IEEE.

关键词： Health insurance

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A Novel Data Hiding Scheme based on Block Features Enhanced AMBTC 4

A Novel Data Hiding Scheme based on Block Features Enhanced ...

引用

4th Asia Service Sciences and Software engineering Conference, ASSE 2023

作者： Lin, Chia-Chen Oka, Hizrawan Dwi Zhu, Enting Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan

ISBN: (纸本)9798400708534

In this paper, we first improve Chen et al.'s ternary representation-based Absolute Moment Block Truncation Coding (AMBTC), then we combine block features and AMBTC to design our novel block feature enhanced-AMBTC-based data hiding scheme called BFI-AMBTC-based DH scheme. In our scheme, AMBTC blocks are classified into three types: complex, smooth, and flat according to the distribution of pixels in a block. For each block type, we propose different data hiding strategies according to their unique features. Experiments confirm that our hybrid data hiding strategies work well. The image quality of stego images with our hybrid data hiding strategies not only has been significantly improved but also is much closer to that offered by the conventional AMBTC compared with other existing AMBTC-based data hiding schemes. In addition, the hiding capacity achieved by our BFI-AMBTC-based DH scheme is confirmed almost two times Ou and Sun's scheme. © 2023 ACM.

关键词： Steganography

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Advancements in EEG-Based Emotion and Consciousness Recognition: Techniques, Challenges, and Innovations 1

Advancements in EEG-Based Emotion and Consciousness Recognit...

引用

1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Jadekar, Ranjana B. Poornima, B. Gowramma, B.H. Puneeth, S.P. Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Information Science and Engineering Karnataka Belagavi India

ISBN: (纸本)9798350376517

This study delves into the application of Electroencephalogram (EEG)-based techniques for emotion and consciousness recognition, focusing on disorders of consciousness (DOC). By evaluating existing approaches in terms of techniques, methodologies, and accuracies, the research highlights the efficacy of both machine learning and deep learning models. The key challenges were identified such as noise removal due to artifacts and variability in patient responses. The findings underscore the potential of advanced computational methods in enhancing the reliability and precision of EEG-based diagnostic tools, paving the way for better clinical interventions and deeper understanding of human emotions. Also, the findings show that using deep learning, the current works have achieved better results for identification of emotion. © 2024 IEEE.

关键词： Electroencephalography

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N-gram Features and Machine Learning Models for Recognizing Human Enzyme Protein Sequences 4

N-gram Features and Machine Learning Models for Recognizing ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mehta, Disha M. Prajapati, Rashmin B. Upadhyay, Keyur N. Valani, Nisha S. Soni, Neha R. Patel, Shrina Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering Gujarat Vasad India

ISBN: (纸本)9798350300093

The identification of enzyme proteins is crucial for understanding the underlying mechanisms of various biological processes. In recent years, the development of machine learning techniques has made it possible to automate this task with high accuracy. In this study, we propose a method that uses 3-gram features and machine learning models for recognizing human enzyme protein sequences. First, the 3-gram features are extracted from the amino acid sequences of enzymes and use these features as input to several machine learning models, including naive bayes, decision tree, random forest, and Extra tree (ET). We compare the performance of these models using metrics such as accuracy, precision, recall, and F1 score. The results obtained show that the ET model performs the best, achieving an accuracy of 96%, precision of 93%, recall of 93%, and F1 score of 93%. Overall, the proposed approach demonstrates the potential of using machine learning models and 3-gram features for accurate identification of human enzyme protein sequences. © 2023 IEEE.

关键词： Decision trees

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