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URL based Phishing Detection using machine learning

URL based Phishing Detection using Machine Learning

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international conference on Information Systems and Computer Networks (ISCON)

作者： Mohd Shoaib Mohammad Sarosh Umar Department of Computer Engineering Zakir Hussain College of Engineering & Technology Aligarh Muslim University Aligarh India

Phishing is a malicious act involving combination of both social engineering and technical deception to steal an individual’s personal identity data and financial account credentials. Sophisticated phishing attacks are increasing day by day resulting in financial and other data loss to industries and user community. Phishers use malicious URLs as one of the way to cause harm and steal information. A plethora of work has been reported to detect the phishing attacks using different machine learning techniques which indicative to achieving higher accuracy in detecting malicious URLs. this paper investigates the different techniques, which researchers had applied for accurate detection of phishing and brings forth the current challenges. the paper also proposes a phishing detection model that implements machine-learning technique to detect phishing attack.

关键词： Uniform resource locators Industries Systematics machine learning algorithms Phishing Computational modeling machine learning

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Sentiment Analysis of Audio Files Using machine learning and Textual Classification of Audio data

Sentiment Analysis of Audio Files Using Machine Learning and...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Shipra Saraswat Shubham Bhardwaj Saksham Vashistha Rishabh Kumar Dept. of Computer Science & Enggineering ABES Institute of Technology Ghaziabad Affiliated to AKTU Lucknow UP India

Sentiment Analysis has an increasing implication in solving Human-machine collaboration issue. It’s a tough task to be able to know how an individual feels but it seems even worse to recognize these sentiments using a machine. As we all know in today’s world with day-to-day advancement in technologies people are searching for more and more easy and convenient ways to operate, with subsequent growth in the applications of Artificial Intelligence (AI), it now has generated a need to spontaneously identify the sentiments of the person involved in the Human Computer Interaction (HCI). the demand for sentiment analysis is increasing and it now is applied in various parts of industry. this research paper discusses the methods to identify various sentiments from human conversation using textual classification of audio data.

关键词： Human computer interaction Text mining Sentiment analysis machine learning algorithms Supervised learning machine learning Oral communication

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Colon Cancer Tissue Classification Using ML

Colon Cancer Tissue Classification Using ML

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international conference on Information Systems and Computer Networks (ISCON)

作者： Ashish Tripathi Kuldeep Kumar Anuradha Misra Brijesh Kumar Chaurasia Department of Computer Science and Engineering Amity School of Engineering & Technology Amity University Lucknow Lucknow Uttar Pradesh India Center for Data Analytics Bond Business School Bond University Gold Coast Queensland Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world’s most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. the dataset is evaluated by these machine learning (ML) procedures: K-Nearest Neighbor, Support Vector machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.

关键词： Deep learning Support vector machines machine learning algorithms Transfer learning Feature extraction Boosting Colon

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Big data and Predictive data analytics in the Smes Industry Using machine learning Approach

Big Data and Predictive Data Analytics in the Smes Industry ...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Reemiah Muneer Alotaibi Shakir Khan College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSU) Riyadh Saudi Arabia

machine learning techniques' effectiveness and practicality in manufacturing, particularly servicing, have greatly evolved in recent years. Big data has important possibilities & prospects for SMEs. Big data can foster collaboration among SMEs by developing quick fixes for problems across all sectors. By availing use of the availability for judgement, this may be accomplished. For two major reasons, SMEs are carefully chosen inside context: SMEs have the benefit & adaptability for speedier adapting to shifts toward productivity because of the overall assessment with in sector, which causes a little alteration in them to impact bigger macro scale effects. Nevertheless, there are also several problematic concerns in the Big data setting, including storing, the capacity of the businesses to analyse and produce useful info from all of this, and privacy and confidentiality. their use allows predictive maintenance, which yields substantial efficiency gains. However, putting such solutions into practise successfully still needs a lot of work in data preparation to get the proper information and interdisciplinarity in teams. Here, small and medium-sized businesses (SME) frequently lack capacity, skill, and experience. the installation of machine learning solutions for predictive maintenance in SME is presented in this study as a methodical and practice-oriented strategy that has previously been proven.

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Privacy-Preserving machine learning for Image data: From Grayscale Single Feature to Color Multi-feature

Privacy-Preserving Machine Learning for Image Data: From Gra...

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international conference on Big data and Artificial Intelligence (BDAI)

作者： Zahir Alsulaimawi Oregon State University EECS

the increasing demand for privacy protection in machine learning applications has led to the development of various privacy-preserving techniques. Our paper focuses on privacy-preserving machine-learning techniques applied to image classification tasks. Our approach addresses scenarios involving grayscale images with a single sensitive feature and color images with multiple sensitive and public features. We propose a novel approach combining adversarial training and modified loss functions to balance privacy and utility preservation. Specifically, we tackle the data representation problem as (x, s, u), where x represents the raw high-dimensional data, s denotes the sensitive features, and u corresponds to the public or utility features. through extensive experiments conducted on diverse datasets, we demonstrate the effectiveness of our methods. Our approach safeguards sensitive information while achieving high classification accuracy for public features. the findings from our study provide valuable insights and practical guidance for developing privacy-preserving machine learning models in real-world applications involving image data.

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Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using machine learning 6th

Bus Journey Time Prediction: A Comparison of Whole Route an...

引用

6th EAI international conference on Intelligent Transport Systems, INTSYS 2022

作者： Dunne, Laura McArdle, Gavin School of Computer Science University College Dublin Belfield Dublin 4 Ireland

ISBN: (纸本)9783031308543

Accurately predicted bus journey times are essential for bus network reliability and making bus transport attractive. the most common approach when predicting bus journey times with machine learning (ML) is to predict journey times for each stop pair segment. Segment data can be very noisy, leading to inaccuracies. To investigate this, this paper compares the classic stop pair segment approach to three other methods. Firstly, a naive method of calculated historical averages is introduced as a baseline. We then explore two methods based on predicting the whole bus route journey time from origin to terminus. To estimate a passenger’s journey, where the whole route is not travelled, we estimate the proportion of the whole journey time the passenger’s journey will take. the first of these methods calculates this proportion from similar historical journeys, and the second proposed method trains an ML model to predict this proportion for each segment of the passenger’s journey. the results show that this novel proposed approach results in less error across most metrics, when compared to the segment prediction method. An interesting insight from the analysis shows the proposed approach has enhanced benefits during peak travel time and during the working week. Gains in prediction accuracy at these times would benefit the most commuters. this research can be applied to make robust scheduling decisions that will increase bus network reliability, improve bus network satisfaction and uptake, and lead to more sustainable cities. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Sentiment Analysis applied to Arabic Tweets Using machine learning and Deep learning*

Sentiment Analysis Applied to Arabic Tweets Using Machine Le...

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international conference on Cloud Technologies and Applications (CloudTech)

作者： Mohammed Benali Abdeljalil Elouardighi LM2CE laboratory FEG Hassan 1 university Settat Morocco

Natural language processing is a field of study that treats multiple tasks, such as text generation, language modeling, and classification. the extraction of valuable information from text documents allows for better decision making in political decisions and analyzing public opinion; those techniques are also deployed in support systems. this work focuses on Arabic sentiment analysis for Twitter comments in Modern Standard Arabic and Moroccan dialectal Arabic. the process starts by extracting data from Twitter using APIs. this dataset is used to train various machine learning and deep learning models. the data must be properly trained, which requires the right pre-processing. We used emojis to annotate the data, and word sequences are used to create word vectors, and these vectors are then passed through dense layers to generate word embeddings, which is specifically tailored to our dataset. Sentiment analysis is undertaken to extract opinions from Arabic text using both Deep learning (DL) and machine learning (ML) models. Enabling a comparative analysis of performance between Deep learning and machine learning algorithms applied to Arabic textual data. the results will be presented above and evaluated on the basis of many factors and indicators. this work has shown very satisfactory and promising results, which is very interesting and encouraging to keep working on this subject.

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Deep learning analytics and Operations Research: Models, Applications and Managerial Implications

Deep Learning Analytics and Operations Research: Models, App...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： K. Vijayalakshmi Kushagra Kulshreshtha Mahesh Bhong Durgeshwar Pratap Singh C Praveen Kumar Vijay Bhutani Dean of Research Loyola Academy Alwal Secunderabad Telangana Institute of Business Management GLA University Mathura Uttar Pradesh India Dept. of Mechanical Engineering Indira College of Engineering and Management Pune Maharashtra India Dept of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering Amity University Greater Noida Uttar Pradesh India

Business analytics refers to the processes, strategies, and procedures that are utilised to derive value for individuals, organisations, and organisations from data. In today's fast-paced and more globalised digital economy, it is essential for decision-making to be driven more by data and to be supported by artificial intelligence (AI) and machine learning (ML). Deep learning (DL) does offer a number of benefits, but it also has a number of limitations that have, up to this point, prohibited businesses from making widespread use of it. Deep neural networks offer the potential of surpassing models from standard machine learning in terms of prediction accuracy, which is one of the reasons why the introduction of deep learning has led this area to undergo a major transformation. this is one of the reasons why the introduction of deep learning has caused this field to undergo a considerable shift. However, according to the findings of our review of the body of recently published research, the number of papers relevant to our industry that make use of operations research is quite low. Consequently, the objectives of this study provide an overview. In a few different case studies, we look at the value that operations research brings to the table by using real data from real-world business activities. Every one of these instances demonstrates improvements in operations research performance above what is possible with traditional machine learning, which ultimately results in gains in direct value. In this paper, we present academics, managers, and practitioners in the field of operations research who are interested in developing their abilities in deep learning and business analytics with suggestions and implications. Our computational investigations highlight the need of bespoke architectures by recommending a one-of-a-kind deep-embedded network as a solution to the problem that standard, out-of-the-box solutions are often not satisfactory.

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A Reputation-Based learning Nodes Selection Algorithm for Decentralized Federated learning

A Reputation-Based Learning Nodes Selection Algorithm for De...

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IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Jing Qu Yunxia Feng Hongda Xu Kang Xie School of Information Science and Technology Qingdao University of Science and Technology Qingdao China

Due to the increasing risk of data security, distributed learning model based on real-world data analytics has attracted more attention, and it has been applied in a variety of areas ranging from medical screening to agriculture, industry, finance, and defense science. Generally, participants provide their own private datasets to efficiently train the distributed models on real-world data, which inevitably leads to privacy and security concerns. Without uploading raw training data, Federated learning enables large-amount nodes to train a distributed model and preserves security and privacy of user sensitive information. However, Federated learning is limited by expensive computational costs during collective parameter server aggregation. Moreover, malicious nodes among computing nodes interfere model training to some extent and further cause the leakage of data privacy. To address the above-mentioned problems, we propose a novel Decentralized Federated learning by integrating blockchain and federation learning for efficient node selection and communication. Based on the proposed model, a reputation-based learning nodes selection algorithm is presented to measure the probability of honest participation of distributed nodes. the simulation results demonstrate that our RBLNS is capable of improving the training result significantly and decreasing convergence time.

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Using data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th international conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. these motion vectors are then used to analyze facial expressions using some data mining algorithms. this analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. the facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep learning (DL), Support Vector machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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