منابع مشابه
LEARNING TO DETECT PHISHING URLs
Phishing attacks have been on the rise and performing certain actions such as mouse hovering, clicking, etc. on malicious URLs may cause unsuspecting Internet users to fall victims of identity theft or other scams. In this paper, we study the anatomy of phishing URLs that are created with the specific intent of impersonating a trusted third party to trick users into divulging personal data. Unl...
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Malicious Web sites largely promote the growth of Internet criminal activities and constrain the development of Web services. As a result, there has been strong motivation to develop systemic solution to stopping the user from visiting such Web sites. In this paper, we propose a learning based approach to classifying Web sites into 3 classes: benign, phishing, and malware. Our mechanism only an...
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We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the most relevant n-grams for prediction, we ev...
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Nowadays, malicious URLs are the common threat to the businesses, social networks, net-banking etc. Existing approaches have focused on binary detection i.e. either the URL is malicious or benign. Very few literature is found which focused on the detection of malicious URLs and their attack types. Hence, it becomes necessary to know the attack type and adopt an effective countermeasure. This pa...
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Web pages are identified by their URLs. For authoritative web pages, pages that are focused on a specific topic, webmasters tend to use URLs which summarize the page. URL information is good for clustering because, they are small and ubiquitous, making techniques based on just URL information magnitudes faster than those which make use of the text content as well. We present a system that makes...
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ژورنال
عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology
سال: 2011
ISSN: 2157-6904,2157-6912
DOI: 10.1145/1961189.1961202