Malicious-URL Detection using Logistic Regression Technique

Over the last few years, the Web has seen a massive growth in the number and kinds of web services. Web facilities such as online banking, gaming, and social networking have promptly evolved as has the faith upon them by people to perform daily tasks. As a result, a large amount of information is uploaded on a daily to the Web. As these web services drive new opportunities for people to interact, they also create new opportunities for criminals. URLs are launch pads for any web attacks such that any malicious intention user can steal the identity of the legal person by sending the malicious URL. Malicious URLs are a keystone of Internet illegitimate activities. The dangers of these sites have created a mandates for defences that protect end-users from visiting them. The proposed approach is that classifies URLs automatically by using Machine-Learning algorithm called logistic regression that is used to binary classification. The classifiers achieves 97% accuracy by learning phishing URLs

A survey on current malicious javascript behavior of infected web content in detection of malicious web pages

In recent years, the advance growth of cybercrime has become an urgent issue to the security authorities. With the improvement of web technologies enable attackers to launch the web-based attacks and other malicious code easily without having prior expert knowledge. Recently, JavaScript has become the most common attack construction language as it is the primary browser scripting language which allow developer to develop sophisticated client-side interfaces for web application. This lead to the growth of malicious websites and as main platform for distributing malware or malicious script to the user's computer when the user access to these webpages. Initial act and detection on such threats early in a timely manner is vital in order to reduce the damages which have caused billions of dollars lost every year. A number of approaches have been proposed to detect malicious web pages. However, the efficient detection of malicious web pages previously has generated many false alarm by the use of sophisticated obfuscation techniques in benign JavaScript code in web pages. Therefore, in this paper, a thoroughly survey and detailed understanding of malicious JavaScript code features will be provided, which have been collected from the web content. We conduct a thorough analysis and studies on the usage of different JavaScript features and JavaScript detection technique systematically and present the most important features of malicious threats in web pages. Then the analysis will be presented along with different dimensions (features representation, detection techniques analysis, and sample of malicious script)