Social network web sites become very popular nowadays. Users can establish connections with other users forming a social network, and quickly share information, photographs, and videos with friends. Malwares called social network worms can send text messages with malicious URLs by employing social engineering techniques. They are trying let users click malicious URL and infect users. Also, it can quickly attack others by infected user accounts in social network. By curiosity, most users click it without validation. This thesis proposes a malicious URL detection method used in Facebook wall, which used heuristic features with high classification property and machine learning algorithm, to predict the safety of URL messages. Experiments show that, the proposed approach can achieve about 96.3% of True Positive Rate, 95.4% of True Negative Rate, and 95.7% of Accuracy.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0815111-155110 |
| Date | 15 August 2011 |
| Creators | Su, Qun-kai |
| Contributors | Chia-Mei Chen, D. J. Guan, Chun-I Fan |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0815111-155110 |
| Rights | user_define, Copyright information available at source archive |
Page generated in 0.0018 seconds