Domain Watcher: Detecting Malicious Domains Based on Local and Global Textual Features

Visual Saliency Based on Local and Global Features in the Spatial Domain

The human visual system can quickly and efficiently capture the salient objects in a scene. Based on the biological mechanism, a new multi-scale saliency analysis method is proposed in this paper, in which the differences of region colors and spaces are calculated in different scale and their saliency map are fused together. First, we calculate the image saliency by using the color and space in...

Hyperspectral Images Classification by Combination of Spatial Features Based on Local Surface Fitting and Spectral Features

Hyperspectral sensors are important tools in monitoring the phenomena of the Earth due to the acquisition of a large number of spectral bands. Hyperspectral image classification is one of the most important fields of hyperspectral data processing, and so far there have been many attempts to increase its accuracy. Spatial features are important due to their ability to increase classification acc...

Breaking Bad: Detecting malicious domains using word segmentation

In recent years, vulnerable hosts and maliciously registered domains have been frequently involved in mobile attacks. In this paper, we explore the feasibility of detecting malicious domains visited on a cellular network based solely on lexical characteristics of the domain names. In addition to using traditional quantitative features of domain names, we also use a word segmentation algorithm t...

Detecting Malicious Content on Facebook

Online Social Networks (OSNs) witness a rise in user activity whenever an event takes place. Malicious entities exploit this spur in user-engagement levels to spread malicious content that compromises system reputation and degrades user experience. It also generates revenue from advertisements, clicks, etc. for the malicious entities. Facebook, the world’s biggest social network, is no exceptio...

Study on Network-Based Technology for Detecting Potentially Malicious Websites