Abstract
The Internet has become the most vulnerable part of critical civil infrastructures. Proactive measures such as early warnings are required to reduce the risk of disasters that can be created using it. With the continuous growth in scale, complexity and variety of networked systems the quality of data is continuously decreasing. This paper investigates the ability to employ Bayesian inference for network scenario analysis with low quality data to produce early warnings. Theoretical account of the approach and experimental results using a real world attack scenario and a real network traffic capture is presented.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing (CSCloud) |
| Publisher | IEEE |
| Pages | 399-404 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-4673-9300-3 |
| DOIs | |
| Publication status | Published - 07 Jan 2016 |
| Externally published | Yes |
| Event | 2nd IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2015 - New York, United States Duration: 03 Nov 2015 → 05 Nov 2015 |
Conference
| Conference | 2nd IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2015 |
|---|---|
| Country | United States |
| City | New York |
| Period | 03/11/2015 → 05/11/2015 |
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Keywords
- Bayesian inference
- Early warning systems
- Low quality data analysis
- Network security
Cite this
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Towards an Early Warning System for Network Attacks Using Bayesian Inference. / Kalutarage, Harsha Kumara; Lee, Chonho; Shaikh, Siraj Ahmed; Sung, Francis Lee Bu.
2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing (CSCloud). IEEE , 2016. p. 399-404 7371513.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Towards an Early Warning System for Network Attacks Using Bayesian Inference
AU - Kalutarage, Harsha Kumara
AU - Lee, Chonho
AU - Shaikh, Siraj Ahmed
AU - Sung, Francis Lee Bu
PY - 2016/1/7
Y1 - 2016/1/7
N2 - The Internet has become the most vulnerable part of critical civil infrastructures. Proactive measures such as early warnings are required to reduce the risk of disasters that can be created using it. With the continuous growth in scale, complexity and variety of networked systems the quality of data is continuously decreasing. This paper investigates the ability to employ Bayesian inference for network scenario analysis with low quality data to produce early warnings. Theoretical account of the approach and experimental results using a real world attack scenario and a real network traffic capture is presented.
AB - The Internet has become the most vulnerable part of critical civil infrastructures. Proactive measures such as early warnings are required to reduce the risk of disasters that can be created using it. With the continuous growth in scale, complexity and variety of networked systems the quality of data is continuously decreasing. This paper investigates the ability to employ Bayesian inference for network scenario analysis with low quality data to produce early warnings. Theoretical account of the approach and experimental results using a real world attack scenario and a real network traffic capture is presented.
KW - Bayesian inference
KW - Early warning systems
KW - Low quality data analysis
KW - Network security
UR - http://www.scopus.com/inward/record.url?scp=84962920885&partnerID=8YFLogxK
U2 - 10.1109/CSCloud.2015.35
DO - 10.1109/CSCloud.2015.35
M3 - Conference contribution
AN - SCOPUS:84962920885
SP - 399
EP - 404
BT - 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing (CSCloud)
PB - IEEE
ER -