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Detecting Ransomware Threats Using Machine Learning Approach

Detecting Ransomware Threats Using Machine Learning Approach

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Sunitha Guruprasad Arnav G Rao Sanmitha Chitharanjan Shetty Rose Bastian Sarah Abdul Khader Dept. of Computer Science & Engineering St Joseph Engineering College Mangaluru India Visvesvaraya Technological University Belagavi Karnataka India Dept. of Computer Science & Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness.

关键词： Analytical models Accuracy Computational modeling Stacking Telecommunication traffic Very large scale integration Feature extraction Ransomware Integrated circuit modeling Random forests

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Novel Plant Leaf Disease Detection Approach using Hybrid Deep Learning Strategy 5

Novel Plant Leaf Disease Detection Approach using Hybrid Dee...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Vamshi, Jalla Tanwar, Shashi Thatipudi, Jayendra Gopal Ponsangeetha, A. Mukherjee, Saptarshi Mandhare, Jagdish B. School of Agriculture Sr University Telangana Warangal India G D Goenka University Department- Computer Science and Engineering Haryana Gurugram India University of North Texas Dept. of Computer Science United States Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India Goldsmith Institute of Advanced Research Department-Mtech Bioinformatics Giar West Bengal Kolkata India Bharati Vidyapeeth College of Engineering Department of Instrumentation Navi Mumbai India

ISBN: (纸本)9798350385649

The early detection and identification of plant diseases are pivotal for precision agriculture, aiming to mitigate crop losses and optimize yields. This study presents a novel approach to plant disease identification, leveraging Deep Learning (DL) techniques with a focus on transfer learning. Traditional methods often rely on complex image enhancement, disease region segmentation, and feature extraction, whereas our approach employs Convolutional Neural Networks (CNNs) for efficient and accurate disease detection. To enhance accessibility and diagnostic capabilities, we implement Hybrid DL Strategy, which consists of EfficientNetB0 and MobileNetV2 model, tailored for lightweight applications suitable for smartphone implementation. Efficient NetB0 model, which considers dept., width, and resolution during convolution, enhancing the model's capacity to capture intricate features critical for accurate disease diagnosis. The overall accuracy of proposed method is 98.44%, surpassing CNN, ResNet, and AlexNet. This high accuracy underscores the effectiveness of the proposed model in correctly classifying both diseased and healthy plant leaves. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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Emerging Technologies for Sustainability and Intelligent systems (ICETSIS), ASU International Conference in

作者： Nowshin Tasnim Kazi Rifah Noor Mursalina Islam Mohammad Nurul Huda Iqbal H Sarker Dept. of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science & Engineering United International University Dhaka Bangladesh Edith Cowan University Perth WA Australia

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.

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Early Prediction of Cardiovascular Diseases Using Feature Selection and Machine Learning Techniques 6

Early Prediction of Cardiovascular Diseases Using Feature Se...

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6th IEEE International Conference on Communication and Electronics systems, ICCES 2021

作者： Rashme, Tamanna Yesmin Islam, Linta Jahan, Sohely Prova, Ayesha Aziz Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Barishal Dept. of Computer Science and Engineering Dhaka Bangladesh Central Women's University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665435871

cardiovascular disease is one of the most important diseases that affects the heart and blood vessels. The loss of lives is mostly linked to a lack of early disease detection, and a preemptive prediction of cardiovascular disease risk will greatly alleviate the situation. Due to the increasing amount of data growth in the health care industry, therefor Machine Learning techniques predict the disease depends on the severity of the patient's side effect. This research work proposes a model to perform early prediction of cardiovascular disease by using different machine learning algorithms, which are used for different prediction purposes. For feature selection purposes, Random Forest algorithm is used to select suitable attributes for the prediction process. The proposed model is assessed based on evaluation metrics;accuracy, precision, recall (sensitivity), f1- score, and specificity. In this exploration of predicting cardiovascular disease, the XGBoost machine learning classifier accomplished a higher rate of accuracy 75.10%. Also, this model provides a higher rate for other evaluation metrics for all the evaluation metrics 76.64%, 69.88%, 79.32%, 78.16%, and 72.84% for precision, sensitivity, specificity, and f1-score, respectively in the case of early cardiovascular diseases prediction. © 2021 IEEE.

关键词： Forecasting

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EMRChain: Electronic Medical Records Management system using Blockchain

EMRChain: Electronic Medical Records Management System using...

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Blockchain and Distributed systems Security (ICBDS), IEEE International Conference on

作者： Arjun Rajasekharan Rani Koshy Dept. of Computer Science and Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala India Department of Computer Science and Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system leverages blockchain technology to address these issues, ensuring data remains immutable, transparent, and highly secure. Utilizing the Ethereum network and IPFS technology, our system provides a decentralized framework for storing and sharing medical records. Healthcare professionals, patients, and authorized parties can seamlessly exchange and access records through Ethereum’s decentralized structure and smart contracts, enabling automated and auditable interactions. To enhance confidentiality, all data uploaded to IPFS undergoes AES 256-bit encryption, with the encrypted Content Identifier (CID) stored on the blockchain. This ensures that intercepted CIDs cannot be accessed without the decryption key. IPFS technology facilitates decentralized storage and retrieval, eliminating the need for a centralized server and improving scalability and resilience. Patients retain full control over their health information, granting or revoking access as necessary. This system ensures secure patient identification, precise access management, and data confidentiality while allowing authorized parties to track and trace record access and modifications. Our blockchain-based EMR management system addresses the inherent risks of traditional centralized systems by providing a secure, transparent, and decentralized solution. By leveraging Ethereum and IPFS, it enhances data security, patient control, and interoperability, thus paving the way for more robust and patient-centric healthcare information management. This approach ensures that healthcare data remains secure, patients maintain control over their information, and authorized parties can effectively manage and access the records.

关键词： Data privacy Scalability Smart contracts Process control Blockchains InterPlanetary File system Servers Electronic medical records Interoperability Resilience

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Image Representation of Numerical Data-points for Classification Using Convolutional Neural Network 4

Image Representation of Numerical Data-points for Classifica...

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4th International Conference on Computing and Communication systems, I3CS 2023

作者： Deka, Rup Kumar Kalita, Kausthav Pratim Chettri, Sarat Kumar Assam Don Bosco University Dept. of Computer Science and Engineering Assam Guwahati-17 India Assam Don Bosco University DDept. of Computer Applications Assam Guwahati-17 India

ISBN: (纸本)9798350323771

Higher dimensional data is a problem for classification. Researchers put forward various approaches like dimension reduction, ranking features for an optimal set, different feature extraction techniques, etc. for classification. In the current context of categorization, CNN (Convolution Neural Network) is a powerful tool, which classifies the data based on images as input. In this work, we have demonstrated a method to represent individual samples in an image, irrespective of dimension, and used CNN for classification. Further, to preserve data privacy, the images are flipped randomly (horizontal and vertical flips) for classification using CNN. The outcomes of both approaches are compared with related techniques and have shown promising results. © 2023 IEEE.

关键词： Classification (of information)

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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems

关键词： Wireless communication Time-frequency analysis system performance Symbols New Radio Resource management Reliability Uplink Optimization Demodulation

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RFID Thermal Monitoring Sheet (R-TMS) for Skin Temperature Measurements during Superficial Microwave Hyperthermia Treatment 7

RFID Thermal Monitoring Sheet (R-TMS) for Skin Temperature M...

引用

7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Lestini, Francesco Panunzio, Nicoletta Marrocco, Gaetano Occhiuzzi, Cecilia University of Rome Tor Vergata Pervasive Electromagnetics Lab Civil Engineering and Computer Science Engineering Dept. Rome Italy

ISBN: (纸本)9789532901160

Hyperthermia is a widely used anti-cancer treatment that exploits the interactions between high-power electromagnetic fields and the human tissues to locally release a great amount of power in the underlying tissue regions. The electromagnetic source is generally outside the body, in close proximity to the skin. Thus, dangerous hotspots may occur within the skin, and hence continuous monitoring of the superficial temperature distribution is required throughout the treatment. Current Thermal Monitoring Sheets (TMSs), that are grids of many temperature sensors, have some limitations in terms of poor spatial resolution and high thermal conduction errors. Passive Ultra High Frequency (UHF) Radio Frequency IDentification (RFID) devices could offer an attractive alternative thanks to their wireless nature, low-invasiveness, and demonstrated good on-skin performance, especially for temperature monitoring. The preliminary design of an RFID-based Thermal Monitoring Sheet (R-TMS) is presented in this paper as a planar array of circular loop antennas with temperature-sensing-oriented RFID ICs. Both sensing and electromagnetic requirements of the R-TMS are addressed by means of numerical simulations. It proved capable of transmitting the collected temperature data with a remarkable spatial resolution, yet without interfering with the effectiveness of the hyperthermia treatment nor inducing unwanted hotspots over the skin. © 2022 University of Split, FESB.

关键词： Frequency selective surfaces

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Quijano, Andrew Halkidis, Spyros T. Gallagher, Kevin Akkaya, Kemal Samaras, Nikolaos New York University Dept. of Computer Science and Engineering New YorkNY United States University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Florida International University Advanced Wireless and Security Lab MiamiFL United States

ISBN: (纸本)9798350367942

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site."Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks. © 2024 IEEE.

关键词： Differential privacy

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