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machine learning-Based Technique for Phishing URLs Detection from TLS 1.2 and TLS 1.3 Traffic Without Decryption 4th

Machine Learning-Based Technique for Phishing URLs Detectio...

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4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Kumar, Munish Pais, Alwyn Roshan Rao, Routhu Srinivasa National Institute of Technology Karnataka Surathkal India GMR Institute of Technology Andhra Pradesh Rajam532127 India

ISBN: (纸本)9789819900848

Phishing is one of the major leading cyberattack leading to huge financial loss and sensitive information loss such as account information, card details, password, login credentials. Existing techniques for phishing URL detection are unable to efficiently classify them. the use of TLS 1.2 and TLS 1.3 for client/server applications to communicate over the Internet securely has also contributed to the increase in these attacks. TLS 1.2 and TLS 1.3 traffic is encrypted, so detecting phishing URLs from encrypted traffic without decryption is a challenging task. In this paper, a machine learning (ML)-based technique is proposed for the detection of phishing URLs from encrypted traffic. the features are extracted from TLS 1.2 and TLS 1.3 traffic and based on the extracted features URLs are classified using ML algorithms. the dataset has been prepared for legitimate and phishing sites based on the features extracted from TLS 1.2 and TLS 1.3 traffic. Based on the experimental results, it is observed that the proposed model achieved promising results in the detection of phishing URLs from the encrypted traffic with an accuracy of 89.6%. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Ransomware Attack Detection by Applying machine learning Techniques 4th

Ransomware Attack Detection by Applying Machine Learning Tec...

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4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Routray, Siddharth Prusti, Debachudamani Rath, Santanu Kumar Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela India

ISBN: (纸本)9789819900848

the world faces various perilous threats due to computer security breaches in the present era. It is proliferating at such a fast rate that it hampers the integrity and confidentiality of people as well as organizations, resulting in a substantial monetary loss. Among different threats, it has been observed that ransomware is one of its types that results in data loss and makes victims by paying huge ransoms. In this study, a research attempt has been made to detect the attack by applying various machine learning techniques with the dataset. First, the data were trained directly using different machine learning techniques such as k-nearest neighbor, support vector machine (SVM) with different kernel functions (SVM-linear, SVM-polynomial, SVM-radial basis function, SVM-sigmoid), random forest, decision tree, and multilayer perceptron without incorporating any feature selection techniques to detect whether the attack is ransomware or benign. Further to optimize the results, feature selection methods based on the filter (Chi-square test, correlation coefficient), wrapper (forward feature selection, backward feature elimination), and embedded methods (Lasso regularization (L1)) are applied to select the prominent features, and redundant features are discarded. Finally, all the results obtained from different experiments are analyzed with critical assessment. By investigating the performance measures of various classifiers, it has been observed that significant improvement in the result is being achieved by the machine learning techniques when the feature selection techniques are considered. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Two-Space Spatial-Temporal Regularized Correlation Filters for UAV-Based Aerial Tracking 5

Two-Space Spatial-Temporal Regularized Correlation Filters f...

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5th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Guo, Jie Zhuang, Long Xie, Cong Nanjing Research Institute of Electronics Technology Signal and Information Processing Department Nanjing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Discriminative Correlation Filters (DCF) are proved to be effective and efficient tracking methods for unmanned aerial vehicle (UAV) because of their high speed and accuracy. However, DCF suffers from unwanted boundary effects. Spatially regularized DCF (SRDCF) tackles this problem by adding spatial penalty on DCF coefficients. Afterwards, spatial-temporal regularized DCF (STRCF) introduces temporal regularization in the DCF coefficient space to SRDCF for more robust appearance learning and faster computation. In this work, we present two-space spatial-temporal regularized DCF (TSSTCF) by introducing spatial-temporal regularization in the regression space to STRCF. the spatial-temporal regularization in the regression space is complement with it in the DCF coefficient space, leading to more robust target appearance learning. More importantly, in the regression space, we can easily control the learning of every single circular shifted sample. this property helps the model suppress the responses of hard negative samples while still keep temporal regularization of other samples. Furthermore, an adaptive factor is introduced to dynamically control the penalty quantity in the regression space. the TSSTCF model can be efficiently solved via the Alternating Direction Method of Multipliers (ADMM). Extensive experiments have been done to prove the superiority of our method. © 2022 IEEE.

关键词： Antennas

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An Innovative Internet of things (IoT) Computing-Based Health Monitoring System with the Aid of machine learning Approach 5

An Innovative Internet of Things (IoT) Computing-Based Healt...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kumar, Bhupendra Rajput, Namita Yagnam, Nagesh Debre Tabor University Debre Tabor College of Business & Economics Department of Accounting & Finance Ethiopia Sri Aurobindo College Department of Commerce University of Delhi Delhi India Debre Tabor University Department of Information & Technology Africa Ethiopia

ISBN: (纸本)9798350398267

the community health area comprises a massive amount of information, and particular approaches are used to handle that information. One of the most common approaches is handling as well as processing. this technique forecasts the likely consequences of cardiovascular disease. the outcome of this method is to predict the preceding heart illness. the job controls IOT using a sensor (a pulse sensor to monitor pulses) and Arduino, and the results may be seen on a sequential screen. IFTTT is used to analyze sensor readings in Google Sheets, which are subsequently converted into CSV go-like data. the datasets used are classified according to treatment parameters, in addition to being used for data preparation and testing. this technique evaluates those parameters using the information preparation order method. With AI computations and categorization work. the dataset is first dissected, examined, and screened, after which the gathered data is processed in Python programming using machine learning Algorithms, namely Decision Tree Algorithm with Random Backwoods Classification Algorithm. SVM (Support vector machine) produces the best results in terms of detecting heart disease. As a result, the suggested paradigm is shown to be a reliable one for predicting past heart disease. the suggested hardware and software technology assists patients in predicting cardiac disease in its initial stages. © 2022 IEEE.

关键词： Internet of things

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MLTPDFS: Design of machine learning Model for Temporal Analysis of Progressive Diseases via Facial Scans 5th

MLTPDFS: Design of Machine Learning Model for Temporal Analy...

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5th international conference on Information Systems and Management Science, ISMS 2022

作者： Dandekar, Pranali Rahul Pethe, Yoginee Surendra Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9783031311529

Edge analysis, colour variation analysis, texture change analysis, etc. can identify multiple health conditions in facial scans. Identifying these diseases requires individual image processing models. Temporal facial analysis can identify stress, anxiety, happiness, high blood pressure, diabetes, and other health conditions. Most of these models use independent machine learning (ML) components for disease identification, limiting real-time scalability. Some of these models combine ML components, but their clinical prediction and analysis is limited. this text proposes a novel machine learning Model for Temporal analysis of progressive diseases via Facial Scans. the proposed model trains CNNs to identify Stress, Anxiety, Heart Conditions, Cancer, and Brain abnormalities. Each disease type requires its own CNN Model. VGGNet-19 predicts stress and anxiety, YoLOV3 identifies brain abnormalities, ResNet-101 predicts heart conditions, and ResNet-152 identifies cancer levels. these models were chosen for their accuracy, precision, and AUC for different diseases. the proposed model combines CNNs using bio-inspired machine learning. the combined model’s hyper-parameters are tuned based on real-time performance metrics. An incremental learning layer improves these metrics by incorporating continuous learning. Due to these operations, the proposed model improves classification accuracy by 5.4%, precision by 6.5%, recall by 8.3%, and AUC by 4.5%. the model performed faster than existing methods due to machine learning, which reduces training and validation delays, making it useful for real-time clinical use cases. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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A Computational Approach to Heat Detection in Radio Frequency Transmitters Using Convolutional Neural Networks 5

A Computational Approach to Heat Detection in Radio Frequenc...

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5th international conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (ICABCD)

作者： Zulu, Bongiwe Sumbwanyambe, Mbuyu Univ South Africa Dept Elect & Min Engn Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

the purpose of this work was to implement a technique that will monitor overheating temperatures on the digital television transmission system\. these overheating temperatures cause transmitters to switch off, resulting in the loss of signal transmission this was achieved by integrating convolutional neural network technique with thermal imaging technology. the application of this technique was implemented using the MATLAB software. this involved the investigation and the tuning of different parameters of convolutional neural networks (CNN) to identify the best option for the model. the thermal images were utilized to train and then test the convolutional neural network. the prediction precision was found to be influenced by the parameter options (Layers, Validation frequency, Filters). this means that any degree within these setting possibilities is feasible if the accuracy is good. the performance accuracy results from training to testing of this model varied from 96.9% to 100%. this indicates that an image's temporal relationships can be successfully captured by applying associated parameters to a convolutional neural network. the results have shown that CNN performs better where there is a lot of trained data. the architecture provides a much precise fitting to an image dataset due to the decreased number of relevant parameters and the reusability of weights. the results of this work will be used on remote radio frequency transmitter sites for heat detection application.

关键词： heat detection machine learning thermal imager Transmitter Dataset

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Multi-Criteria Decision Making for machine learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis 5

Multi-Criteria Decision Making for Machine Learning Algorith...

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5th international conference on Engineering Technology and its Applications, IICETA 2022

作者： Jassim, Mustafa Mohammed Ali, Manar Hassan Elamer, Amer S. Jaber, Mustafa Musa Mohammed, Mohammed Q. Alkhayyat, Ahmed Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Informatics Baghdad10011 Iraq Dijlah University Collage Department of Computer Science Baghdad10021 Iraq Al-Esraa University College Department of Information Technology Baghdad10021 Iraq The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9781665472159

autism spectrum disorder is known as a neurodevelopmental disorder with sensory problems. Many infected adults die frequently due to the psychological conditions they live in. machine learning helps doctors detect cases of autism spectrum disorders in adults. It has proven very successful in this field. In this study, after pre-processing and solving the data class imbalance problem the data we proposed to use MCDM methods to know prioritization and the best method to diagnose autism between four algorithms (SVM, Random Forest, Neural Network, and KNN), after obtaining the classification results we utilized AHP method to assign weights to each classification performance metrics based on expert assessment then he used both the obtained weights and the classification results to enter them into the VIKOR method to make a decision based on the weighted table where we would get the ranking of the algorithms. the random Forest method scores the best in this classification for diagnosing autism, with an accuracy rate of 0.9957 and sensitivity 0.9841. © 2022 IEEE.

关键词： Decision trees

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Image Classification Based on Quantum machine learning

Image Classification Based on Quantum Machine Learning

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Haocheng Xiong Xinyuan Duan Yue Yu Junhan Zhang Hao Yin School of Software Sichuan University Chengdu Sichuan China

Nowadays, using machine learning for image classification is very common. However, due to the increasing demand for data processing and fast computing, the idea of enhancing machine learning with quantum computing has been proposed, known as quantum machine learning (QML). Quantum machine learning has the advantages of higher efficiency and accuracy. Quantum computing uses quantum bits (qubits) for data storage and computing, where a qubit can represent quantum states |0〉and |1〉simultaneously, enabling the processing of information for two states simultaneously, which is unparalleled in classical computing. Moreover, quantum machine learning can handle more complex data and process data faster. In classical machine learning, the processing of large-scale data and complex problems often faces problems of high computational complexity and low algorithm efficiency. Quantum computing can handle multiple computing tasks simultaneously, achieving faster computing. therefore, in some scenarios that require efficient computing, quantum machine learning may be the best choice. In this study, we simulated quantum circuits using Qiskit and built a hybrid quantum-classical neural network model using VQNet to classify MNIST handwritten digits and CIFAR-10 datasets. the experiments showed that quantum machine learning has the advantages of efficiency, accuracy, and security over classical machine learning, which may be an improvement over classical machine learning. this research proposes a machine learning algorithm based on quantum computing, which promotes the development of quantum computing and quantum technology. At the same time, it provides a new solution and idea for image classification, enabling people to pursue faster and more accurate quantum machine learning instead of being limited to classical machine learning. [1]

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HDP-IoT: An IoT Framework for Cardiac Status Prediction System using machine learning 5

HDP-IoT: An IoT Framework for Cardiac Status Prediction Syst...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Reddy, Vijaya Vardan S. P. Joseph, Armstrong J. Priscilla, M. Shathik, Anvar J. Prabu, R. thandaiah RMK Engn Coll Dept ECE Kavaraipettai Thiruvallur India Sri Venkateswara Coll Engn & Technol Autonomous Dept CSE Chittoor India SA Engn Coll Dept ECE Chennai Tamil Nadu India Srinivas Univ Coll Engn & Technol Dept Cloud Technol & Data Sci Mangalore India SIMATS Saveetha Sch Engn Dept ECE Chennai Tamil Nadu India Coll Sci & Arts Dept Comp Sci University U Arab Emirates

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

the mortality rate is high as a result of the significant increase in the number of various forms of non-communicable heart disorders that occur each year. these illnesses are long-term and gradually progressing, and they might advance to the point where the patient's health becomes too critical to manage. As a consequence, the vast majority of individuals have a massive heart attack or discover they have a cardiac condition when it is too severe. therefore, this work presents a cardiovascular status forecasting approach, developed from HDP-IoT and machine learning that can gather essential data from the human body using IoT sensors and transport that information to the server, where the information is given with user identification. the data collected from the body must then be normalised before being fed into ml algorithms, which may then be used to quantify and forecast the overall health of a patient's cardiovascular system. the pulse rate, ECG signal, and cholesterol levels have been gathered from the human body using Internet of things devices up to this point. the findings of the pulse rate and cholesterol tests received have been pretty excellent.

关键词： Internet of things machine learning CardiovascularDisease ECG Sterol Heart Rate

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MRI image denoising using U-Net and Image processing Techniques 5

MRI image denoising using U-Net and Image Processing Techniq...

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5th IEEE international conference on Advances in Science and Technology, ICAST 2022

作者： Mehta, Darshil Padalia, Dishant Vora, Kush Mehendale, Ninad K.J Somaiya College of Engineering Department of Computer Engineering Mumbai India K.J Somaiya College of Engineering Department of Electronics and Telecommunication Mumbai India K.J. Somaiya College of Engineering Department of Electronics Mumbai India

ISBN: (纸本)9781665492638

Brain tumors are one of the leading causes of death, and hence it is critical to diagnose them early. MRI is the most effective diagnostic tool for detecting a tumor. However, thermal noise, temperature fluctuations, and other artifacts can generate noisy MRI scans, leading to inaccurate diagnoses. Deep learning algorithms combined with image processing techniques have aided in a variety of medical imaging tasks, including enhancing MRI images. Our work proposes a U-Net architecture with two encoder-decoder pairs for denoising MRI scans which were finely tuned on a dataset generated by injecting synthetic Gaussian noise. the model improved the Peak signal to Noise Ratio (PSNR) from 11.90 to 30.96. the presented work also provides empirical evidence that the proposed denoising strategy enhances the prediction accuracy of brain tumors by nearly 23%. the developed denoising technique using U-Net would benefit radiologists and computer-aided diagnostic systems (CAD) in precisely diagnosing the disease by generating cleaner and clearer MRI scans. © 2022 IEEE.

关键词： Image enhancement

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