Improving malicious URLs detection via feature engineering: Linear and nonlinear space transformation methods
نویسندگان
چکیده
منابع مشابه
Case Retrieval Using Nonlinear Feature-Space Transformation
Good similarity functions are at the heart of effective case-based reasoning. However, the similarity functions that have been designed so far have been mostly linear, weighted-sum in nature. In this paper, we explore how to handle case retrieval when the case base is nonlinear in similarity measurement, in which situation the linear similarity functions will result in the wrong solutions. Our ...
متن کاملLearning based Malicious Web Sites Detection using Suspicious URLs
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...
متن کاملEngineering space for light via transformation optics.
Conceptual studies and numerical simulations are performed for imaging devices that transform a near-field pattern into magnified far-zone images and are based on high-order spatial transformation in cylindrical domains. A lens translating a near-field pattern from an almost circular input boundary onto a magnified far-field image at a flat output boundary is considered. The lens is made of a m...
متن کاملFeature-based Malicious URL and Attack Type Detection Using Multi-class Classification
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...
متن کامل- Improving Class Separability - a Comparative Study of Transformation Methods for the Hyperspectral Feature Space
Principle and practical aspects of three transformation methods for the hyperspectral feature space (MNF, DAFE, DBFE) are described. In two application cases (urban case: selected man-made materials, water and shadow; rural case: tree species) these methods are used for the discrimination of spectrally similar classes. Supervised classifications are conducted in the original feature space as we...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Systems
سال: 2020
ISSN: 0306-4379
DOI: 10.1016/j.is.2020.101494