Loading...
Please wait, while we are loading the content...
Similar Documents
WARNINGBIRD : A Review of Suspicious URLS Recognition System for Real Time Twitter Stream
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shewale, P. S. Patel, Payal Kergal, Vignesh Chandankar, Shriya Shree, Sowmya |
| Copyright Year | 2015 |
| Abstract | Famous social networking sites contain many suspicious URLs. We are using Twitter as social networking site. Twitter can suffer from malicious tweets containing suspicious URLs for spam, phishing, and malware distribution. In this project, we will propose WARNINGBIRD as real-time, a suspicious URLs detection system for Twitter. Because attackers have limited resources and thus have to reuse them again, a portion of their redirect chains will be shared. We focus on those shared resources to detect suspicious URLs. Our classifier has high accuracy and low false-positive and false-negative rates. We will also present WARNINGBIRD as a real-time system for classifying suspicious URLs that can be seen in the Twitter stream.We will be collecting a large number of tweets from the dummy Twitter account in public timelineand trained a statistical classifier with features derived from correlated URLs and tweet context information. We also present WARNINGBIRD as a real time system for classifying suspicious URLs in the Twitter stream.Previous Twitter spam detection schemes have used account features such as the ratio of tweets containing URLs and the account sign up date, or relation features in the Twitter graph. Malicious users can easily fabricate account features. Extracting relation features from the Twitter graph is time and resource consuming. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijsrd.com/articles/IJSRDV3I1474.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
