Abstract
This paper studies the feasibility of an early warning system that prevents users from the dangerous situations they may fall into during web surfing. Our approach adopts behavioral Hidden Markov Models to explore collective intelligence embedded in users' browsing behaviors for context-aware category prediction, and applies the results to web security threat prevention. Largescale experiments show that our proposed method performs accuracy 0.463 for predicting the fine-grained categories of users' next accesses. In real-life filtering simulations, our method can achieve macro-averaging blocking rate 0.4293 to find web security threats that cannot be detected by the existing security protection solutions at the early stage, while accomplishes a low macro-averaging over-blocking rate 0.0005 with the passage of time. In addition, behavioral HMM is able to alert users for avoiding security threats by 8.4 hours earlier than the current URL filtering engine does. Our simulations show that the shortening of this lag time is critical to avoid severe diffusions of security threats.
| Original language | English |
|---|---|
| Title of host publication | CCS'12 - Proceedings of the 2012 ACM Conference on Computer and Communications Security |
| Pages | 992-994 |
| Number of pages | 3 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 ACM Conference on Computer and Communications Security, CCS 2012 - Raleigh, NC, United States Duration: 2012 Oct 16 → 2012 Oct 18 |
Other
| Other | 2012 ACM Conference on Computer and Communications Security, CCS 2012 |
|---|---|
| Country | United States |
| City | Raleigh, NC |
| Period | 12/10/16 → 12/10/18 |
Fingerprint
Keywords
- Collaborative filtering
- Collective intelligence
- Security assurance
ASJC Scopus subject areas
- Software
- Computer Networks and Communications
Cite this
POSTER : Context-aware web security threat prevention. / Lee, Lung Hao; Juan, Yen Cheng; Lee, Kuei Ching; Tseng, Wei Lin; Chen, Hsin Hsi; Tseng, Yuen Hsien.
CCS'12 - Proceedings of the 2012 ACM Conference on Computer and Communications Security. 2012. p. 992-994.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - POSTER
T2 - Context-aware web security threat prevention
AU - Lee, Lung Hao
AU - Juan, Yen Cheng
AU - Lee, Kuei Ching
AU - Tseng, Wei Lin
AU - Chen, Hsin Hsi
AU - Tseng, Yuen Hsien
PY - 2012
Y1 - 2012
N2 - This paper studies the feasibility of an early warning system that prevents users from the dangerous situations they may fall into during web surfing. Our approach adopts behavioral Hidden Markov Models to explore collective intelligence embedded in users' browsing behaviors for context-aware category prediction, and applies the results to web security threat prevention. Largescale experiments show that our proposed method performs accuracy 0.463 for predicting the fine-grained categories of users' next accesses. In real-life filtering simulations, our method can achieve macro-averaging blocking rate 0.4293 to find web security threats that cannot be detected by the existing security protection solutions at the early stage, while accomplishes a low macro-averaging over-blocking rate 0.0005 with the passage of time. In addition, behavioral HMM is able to alert users for avoiding security threats by 8.4 hours earlier than the current URL filtering engine does. Our simulations show that the shortening of this lag time is critical to avoid severe diffusions of security threats.
AB - This paper studies the feasibility of an early warning system that prevents users from the dangerous situations they may fall into during web surfing. Our approach adopts behavioral Hidden Markov Models to explore collective intelligence embedded in users' browsing behaviors for context-aware category prediction, and applies the results to web security threat prevention. Largescale experiments show that our proposed method performs accuracy 0.463 for predicting the fine-grained categories of users' next accesses. In real-life filtering simulations, our method can achieve macro-averaging blocking rate 0.4293 to find web security threats that cannot be detected by the existing security protection solutions at the early stage, while accomplishes a low macro-averaging over-blocking rate 0.0005 with the passage of time. In addition, behavioral HMM is able to alert users for avoiding security threats by 8.4 hours earlier than the current URL filtering engine does. Our simulations show that the shortening of this lag time is critical to avoid severe diffusions of security threats.
KW - Collaborative filtering
KW - Collective intelligence
KW - Security assurance
UR - http://www.scopus.com/inward/record.url?scp=84869446575&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869446575&partnerID=8YFLogxK
U2 - 10.1145/2382196.2382302
DO - 10.1145/2382196.2382302
M3 - Conference contribution
AN - SCOPUS:84869446575
SN - 9781450316507
SP - 992
EP - 994
BT - CCS'12 - Proceedings of the 2012 ACM Conference on Computer and Communications Security
ER -