A Comparative Study on Data Mining Approach Using Machine Learning Techniques: Prediction Perspective

Phishing is a common attack on credulous people by making them disclose their unique information. It is a type of cyber-crime where false sites allure exploited people to give delicate data. This paper deals with methods for detecting phishing websites by analyzing various features of URLs by Machine learning techniques. This experimentation discusses the methods used for detection of phishing websites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. To protect end users from visiting these sites, we can try to identify the phishing URLs by analyzing their lexical and host-based features.A particular challenge in this domain is that criminals are constantly making new strategies to counter our defense measures. To succeed in this contest, we need Machine Learning algorithms that continually adapt to new examples and features of phishing URLs.

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Data Mining and Machine Learning Techniques for Malware Detection

Rising Threats in Expert Applications and Solutions - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-15-6014-9_66 ◽

2020 ◽

pp. 557-567

Author(s):

Niharika Sharma ◽

Bhavna Arora

Keyword(s):

Machine Learning ◽

Data Mining ◽

Malware Detection ◽

Machine Learning Techniques ◽

Learning Techniques

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Nutritional Status Prediction in Neonate Using Machine Learning Techniques: A Comparative Study

Communications in Computer and Information Science - Machine Learning, Image Processing, Network Security and Data Sciences ◽

10.1007/978-981-15-6318-8_7 ◽

2020 ◽

pp. 69-83

Author(s):

Zakir Hussain ◽

Malaya Dutta Borah

Keyword(s):

Machine Learning ◽

Nutritional Status ◽

Comparative Study ◽

Machine Learning Techniques ◽

Learning Techniques

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A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch056 ◽

2020 ◽

pp. 1096-1117

Author(s):

Rodrigo Ibañez ◽

Alvaro Soria ◽

Alfredo Raul Teyseyre ◽

Luis Berdun ◽

Marcelo Ricardo Campo

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Gesture Recognition ◽

Microsoft Kinect ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Natural Interaction ◽

Computer Communication ◽

Human Computer Interfaces ◽

Learning Techniques

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.

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