Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies |
| Publisher | ACM |
| Pages | 187-193 |
| Number of pages | 7 |
| ISBN (Print) | 9781450346177 |
| DOIs | |
| Publication status | Published - Dec 2016 |
Fingerprint
Keywords
- event correlation
- process auditing
- IDS/IPS
- SIEM
- advanced persistent threats
- security analytics
Cite this
}
A big data analytics based approach to anomaly detection. / Razaq, Abdul; Tianfield, Huaglory; Barrie, Peter.
Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. ACM, 2016. p. 187-193.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A big data analytics based approach to anomaly detection
AU - Razaq, Abdul
AU - Tianfield, Huaglory
AU - Barrie, Peter
N1 - Requested first pub date and AAM 3-3-17 Acceptance email in SAN AAM provided 16-3-17; no embargo required.
PY - 2016/12
Y1 - 2016/12
N2 - We present a novel Cyber Security analytics framework. We demonstrate a comprehensive cyber security monitoring system to construct cyber security correlated events with feature selection to anticipate behaviour based on various sensors.
AB - We present a novel Cyber Security analytics framework. We demonstrate a comprehensive cyber security monitoring system to construct cyber security correlated events with feature selection to anticipate behaviour based on various sensors.
KW - event correlation
KW - process auditing
KW - IDS/IPS
KW - SIEM
KW - advanced persistent threats
KW - security analytics
U2 - 10.1145/3006299.3006317
DO - 10.1145/3006299.3006317
M3 - Conference contribution
SN - 9781450346177
SP - 187
EP - 193
BT - Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
PB - ACM
ER -