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SMS-Shield: A Lightweight Approach for Smishing Detection using Machine learning 1

SMS-Shield: A Lightweight Approach for Smishing Detection us...

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1st international conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Samad, Saleem Raja Abdul Ganesan, Pradeepa Thangam, S. Balasubramaniyan, Sundaravadivazhagan Rajiakodi, Saranya Al Kaabi, Amna Salim Rashid University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences Information Technology Department Shinas Oman Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India University of Technology and Applied Sciences-Al-Musannah College of Computing and Information Sciences Information Technology Department Al-Musannah Oman Central University of Tamil Nadu Department of Computer Science Tamil Nadu Thiruvarur India

ISBN: (纸本)9798350348637

SMS continues to be an indispensable component of contemporary digital communication. Because it is so widely available, cost-effective, reliable, and delivers information instantly. Its versatility enables its utilization for real-time chats, alerts, and verification purposes. Nevertheless, the ubiquitous nature of SMS renders it an easy prey for smishing, a deceitful form of cyberattack. Cybercriminals exploit the trust associated with SMS messages to deceive recipients. Smishing, an increasingly prevalent deceptive SMS fraud, poses a threat to both businesses and individuals. These messages include websites or phone numbers and purport to be from banks, governmental bodies, or well-known businesses. These misleading text messages lure their users into disclosing private information or clicking on links that could be harmful in many ways. Early detection of such malicious messages prevents users from falling victim to them. Various techniques were presented by researchers to accurately detect smishing attacks. Machine learning models exhibit great potential as a strategy to counter the increasingly dangerous SMS phishing attacks. Machine learning models can discern legitimate from fraudulent SMS messages by analyzing datasets. This paper introduces a novel method (SMS-SHIELD), which combines Machine learning and Natural Language Processing techniques to enhance smishing detection. This approach yields outstanding results, with random forest and extreme gradient boosting algorithms achieving remarkable accuracy rates of 99.05% and 98.03%, coupled with impressive F1-scores of 98.95% and 97.86%, respectively. © 2024 IEEE.

关键词： Phishing

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A novel particle picking approach for cryo-electron microscopy images

A novel particle picking approach for cryo-electron microsco...

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2024 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2024

作者： Zhang, Pengcheng Zhou, Fei College of Electronic and Information Engineering Shenzhen University Guangdong Province Shenzhen518060 China

ISBN: (纸本)9781510683044

Cryo-electron microscopy (cryo-EM) has become a crucial tool for determining the structures of proteins and macromolecular complexes. However, the extremely low signal-to-noise ratio (SNR) and large image scale(approximately 4K) of electron microscopy images pose significant challenges in selecting hundreds of thousands of particles from these images for reconstructing high-resolution protein three-dimensional structures. Existing particle picking methods still struggle to meet the requirements of such research. We present a deep neural network model that amalgamates convolutional neural network (CNN) and graph convolutional networks (GCN). The model initially employs CNN for preliminary particle picking, modeling protein particles as the center points of protein particle bounding boxes and leveraging keypoint detection to detect as many particles as possible. Subsequently, the detected particle boxes are cropped from the original electron microscopy images and used as inputs for the GCN for further classification, aiming to distinguish between the background and protein particles to achieve higher precision. Experimental evaluations were conducted on various datasets, and the results indicate that it can acquire accurate results. © 2024 SPIE.

关键词： Convolutional neural networks

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Music Genre Classification- A Holistic Approach Employing Multiple Features 15

Music Genre Classification- A Holistic Approach Employing Mu...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Anuraj, K. Poorna, S.S. Renjith, S. Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amritapuri India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

There is a significant increase in content related to digitized music, along with the rapid growth of digital multimedia. Hence the music genre categorization plays a crucial role in this digital era. It is useful for searching and retrieving user-specific information, as well as recommendation systems. This work is a comparative study on the performance of the combination of various Artificial Intelligence models as well as various features in music genre classification using GTZAN dataset. In order to carryout the pilot study, three types of features, viz. Mel spectrograms, Mel frequency cepstral coefficients and features based on Wave2Vec are employed. AI techniques encompassing deep learning and machine learning models are utilized to effectively classify the music types from the aforementioned features. Results of this study show that the combination of features based on Wave2Vec with 1D Convolutional Neural Network yielded the highest accuracy of 82%. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning for Medical Image Segmentation: Applications in Disease Detection and Diagnosis 3

Deep Learning for Medical Image Segmentation: Applications i...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Kannan, R. Jestus, J. Madesh, P. Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

The accurate diagnosis of disorders is contingent upon the accurate segmentation of medical images. It enables physicians to isolate specific regions of the body for further investigation. This study introduces a novel deep learning-based segmentation technique that employs a dynamic region-based model in conjunction with convolutional neural networks (CNNs) to enhance the precision of borders and the accuracy of segmentation. To overcome the obstacles associated with segmenting intricate medical images and guarantee precise edge identification, a novel adaptive boundary loss function is suggested. The attention mechanism of the model enables it to fine-tune both small and complicated regions of interest, and it is compatible with a variety of imaging modalities, such as MRI, CT, and ultrasound. The proposed method outperforms existing models on Dice coefficient and Intersection over Union (IoU) metrics, as evidenced by comprehensive evaluations on benchmark datasets, including skin lesion and brain tumor segmentation. The findings indicate that the model has the potential to enhance clinical decision-making by offering more precise disease detection segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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Feature engineering for Predicting Consumer Purchase Behavior: A Comprehensive Analysis 3rd

Feature Engineering for Predicting Consumer Purchase Behavio...

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3rd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Karmakar, Mousumi Al Kafi, Md Abdullah Afridi, Arafat Sahin Sabbir, Wahid Raza, Dewan Mamun Alliance Univ Dept Comp Sci & Engn Bangalore Karnataka India Daffodil Int Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9783031640667;9783031640674

Feature engineering is essential for consumer behaviour prediction machine learning models. Analyzing customer behaviour reveals the complexity of feature development. A thorough literature review found that feature engineering has improved consumer purchase behaviour model prediction accuracy in several studies. We use six machine learning algorithms: Random Forest, Decision Trees, K-Nearest Neighbors, Naive Bayes, and Logistic Regression. This study examined Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors, Random Forest, Logistic Regression, and Gaussian Naive Bayes. The models were trained and evaluated using consumer purchase activity data on demographics, product preferences, online behaviour, and temporal factors. Every model achieved 80% accuracy, with Gradient Boosting Classifier, Random Forest, Decision Tree, and Logistic Regression performing best. Due to careful feature selection and preprocessing, the six machine learning models have similar accuracy and F1 scores. Proper feature engineering techniques affect consumer purchase behaviour, which this study investigates. This paper proposes feature engineering, which is novel. A correlation matrix is more efficient and effective than traditional feature selection methods for selecting relevant features. The accuracy depends on the machine learning method and characteristics used. However, the four models that performed well in our study-Decision Tree accuracy 86%, Gradient Boosting Classifier accuracy 86%, Random Forest accuracy 86%, and Logistic Regression accuracy 86%-are reliable and trustworthy for predicting customer purchasing habits.

关键词： Feature engineering Consumer purchase behavior Machine learning Classification Prediction

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Multiple Disease Prediction with Effective Feature engineering Technique 15

Multiple Disease Prediction with Effective Feature Engineeri...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Premalatha, J. Narendranath, K. Saran, M.S. Vigneswaran, G. Kongu Engineering College Department of Information Technology Perundurai638060 India

ISBN: (纸本)9798350370249

Diabetes, lung disease, and heart failure are common health problems that require effective treatment techniques and early detection everywhere. We have developed a novel method in our study that incorporates machine learning approaches to improve the prediction accuracy for these scenarios. Recognizing the importance of complementary therapies such as exercise in addition to medication, our approach uses patient health metrics analysis to enhance the identification of diabetes, lung disease, and heart failure. We used a novel Principal Component Health (PCH) feature engineering technique to identify the most impacting features after conducting extensive research with nine machine learning algorithms. Better predictive performance was made possible by the feature set that was enhanced as a result of this optimization. With a score of $80 \%$, our suggested decision tree method significantly outperformed other models in terms of accuracy. Cross-validation methods of validation strengthened the validity of our results. With the ultimate goal of improving patient outcomes and saving lives, this research offers cutting-edge techniques for the early identification and management of diabetes, lung disease, and heart failure. It constitutes a significant scientific contribution. © 2024 IEEE.

关键词： Lung cancer

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Re-shaping Post-COVID-19 teaching and learning: A Blueprint of Virtual-Physical Blended Classrooms i n the Metaverse Era 42

Re-shaping Post-COVID-19 Teaching and Learning: A Blueprint ...

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42nd IEEE international conference on Distributed computing Systems (ICDCS)

作者： Wang, Yuyang Lee, Lik-Hang Braud, Tristan Hui, Pan Hong Kong Univ Sci & Technol Guangzhou Peoples R China Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

During the COVID-19 pandemic, most countries have experienced some form of remote education through video conferencing software platforms. However, these software platforms fail to reduce immersion and replicate the classroom experience. The currently emerging Metaverse addresses many of such limitations by offering blended physical-digital environments. This paper aims to assess how the Metaverse can support and improve e-learning. We first survey the latest applications of blended environments in education and highlight the primary challenges and opportunities. Accordingly, we derive our proposal for a virtual-physical blended classroom configuration that brings students and teachers into a shared educational Metaverse. We lOcus on the system architecture of the Metaverse classroom to achieve real-time synchronization of a large number of participants and activities across physical (mixed reality classrooms) and virtual (remote VR platform) learning spaces. Our proposal attempts to transform the traditional physical classroom into virtual-physical cyberspace as a new social network of learners and educators connected at an unprecedented scale.

关键词： Human-computer interaction VR/AR Distance learning Metaverse E-learning

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Reinforcement learning-Based Strategies For Achieving Long-Term Sustainability In IoT

Reinforcement Learning-Based Strategies For Achieving Long-T...

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2024 international conference on Integrated Circuits, Communication, and computing Systems, ICIC3S 2024

作者： Shristy Kumar, Krishan National Institute of Technology Dept. of E.C.E Hamirpur India National Institute of Technology Department of Electronics and Communication Engineering Hamirpur177005 India

ISBN: (数字)9798350364088

ISBN: (纸本)9798350364088

In today's landscape, the demand for Internet of Things (IoT) devices have increased, leading to a vast network of heterogeneous devices communicating with each other. However, this connectivity poses significant challenges such as privacy concerns, accuracy issues, and energy inefficiency. Addressing these challenges requires innovative solutions, and one emerging technology poised to tackle them is the federated reinforcement learning framework. This framework offers several advantages for IoT devices, including increased learning speed and enhanced security through centralized model updates instead of raw data transmission. By incorporating federated learning with dueling DQN, our proposed technique offers improved accuracy by mit-igating the overestimation problem prevalent in other methods. In comparison to existing approaches, our Federated Dueling deep Q network technique demonstrates superior optimization in energy efficiency and delay while delivering enhanced accuracy. This combination of benefits positions it as a leading solution for the evolving needs of IoT device management and optimization. © 2024 IEEE.

关键词： Reinforcement learning

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Detection of Fake Profiles and News on Social Media Using Machine learning Algorithms 1

Detection of Fake Profiles and News on Social Media Using Ma...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Saxena, Sachin Singh, Archana Tiwari, Shailesh Amity School of Engineering and Technology Amity University Noida India KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9798350353648

Technology is advancing rapidly every day, becoming an integral part of our lives and helping people connect with each other through social media and online news outlets. But as everything has its own advantages and disadvantages, it has too, and this paves a way for individuals to make fake news and accounts that are posing a major threat to society. This paper tries to incorporate a strategy utilizing Machine learning algorithms. Random Forest, Support Vector Machines and Neural Network are some of the algorithms which are used to distinguish fake profiles on social media and for detection of fake news online Naïve Bayes' and LSTM are utilized. Hence, detection of fake profiles and fake news online is possible using the above-mentioned frameworks. The outcome illustrates the ability to distinguish between fake and real profiles using extracted features by applying trained machine learning models. Naive Bayes, k-nearest neighbor, and Poisson calculation independently give precision of 87%, 88.3.5%, and 91.2% individually. The proposed approach shows that the algorithm neural networks system has an accuracy of 94.3%. The proposed work uses the machine learning approach for detection of tampered profiles on social media platforms. © 2024 IEEE.

关键词： Adversarial machine learning

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