scholarly journals Review of the machine learning methods in the classification of phishing attack

2019 ◽  
Vol 8 (4) ◽  
pp. 1545-1555
Author(s):  
John Arthur Jupin ◽  
Tole Sutikno ◽  
Mohd Arfian Ismail ◽  
Mohd Saberi Mohamad ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Computer Network ◽  
Computer Users ◽  
Machine Learning Method ◽  
Learning Method ◽  
Phishing Attacks ◽  
Internet Users ◽  
Internet Networks ◽  
Use Of Internet

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.

scholarly journals Blockchain Leveraged Cyberbullying Preventing framework

2021 ◽  
Author(s):  
Md Anawar Hossen Wadud ◽  
Md Ashraf Uddin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Natural Language Processing Technique ◽  
Being Bullied ◽  
Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

scholarly journals Identifying psychological indexes based on social media data: A machine learning method

2021 ◽  
Vol 29 (4) ◽  
pp. 571
Author(s):  
Yue SU ◽  
Mingming LIU ◽  
Nan ZHAO ◽  
Xiaoqian LIU ◽  
Tingshao ZHU
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Method ◽  
Learning Method ◽  
Social Media Data ◽  
Media Data

scholarly journals Estimate Sentiment of Crowds from Social Media during City Events

2019 ◽  
Vol 2673 (11) ◽  
pp. 836-850 ◽  
Author(s):  
Vincent X. Gong ◽  
Winnie Daamen ◽  
Alessandro Bozzon ◽  
Serge P. Hoogendoorn
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Urban Areas ◽  
Estimation Error ◽  
Training Methods ◽  
Machine Learning Method ◽  
Learning Method ◽  
Crowd Management ◽  
Event Based

City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.

Leveraging User-Specified Metadata to Personalize Image Search

2010 ◽  
pp. 296-311
Author(s):  
Kristina Lerman ◽  
Anon Plangprasopchok
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Interest Groups ◽  
Special Interest Groups ◽  
Machine Learning Method ◽  
Image Search ◽  
Learning Method ◽  
Query Term ◽  
Photo Sharing ◽  
The Social

The social media sites, such as Flickr and del.icio.us, allow users to upload content and annotate it with descriptive labels known as tags, join special-interest groups, and so forth. We believe user-generated metadata expresses user’s tastes and interests and can be used to personalize information to an individual user. Specifically, we describe a machine learning method that analyzes a corpus of tagged content to find hidden topics. We then these learned topics to select content that matches user’s interests. We empirically validated this approach on the social photo-sharing site Flickr, which allows users to annotate images with freely chosen tags and to search for images labeled with a certain tag. We use metadata associated with images tagged with an ambiguous query term to identify topics corresponding to different senses of the term, and then personalize results of image search by displaying to the user only those images that are of interest to her.

scholarly journals Blockchain Leveraged Cyberbullying Preventing framework

2020 ◽  
Author(s):  
Md Anawar Hossen Wadud ◽  
Md Ashraf Uddin ◽  
Shamima Parvez ◽  
Mohammad Motiur Rahman ◽  
Ammar Alazab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Natural Language Processing Technique ◽  
Being Bullied ◽  
Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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