The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Risk Factors to Retrieve Anomaly Intrusion Information and Profile User Behavior
Abstract
The use of network traffic audit data for retrieving anomaly intrusion information and profiling user behavior has been studied previously, but the risk factors associated with attacks remain unclear. This study aimed to identify a set of robust risk factors via the bootstrap resampling and logistic regression modeling methods based on the KDD-cup 1999 data. Of the 46 examined variables, 16 were identified as robust risk factors, and the classification showed similar performances in sensitivity, specificity, and correctly classified rate in comparison with the KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm with all variables. The study emphasizes that the bootstrap simulation and logistic regression modeling techniques offer a novel approach to understanding and identifying risk factors for better information protection on network security.
Related Content
|
Chaymaâ Boutahiri, Ayoub Nouaiti, Aziz Bouazi, Abdallah Marhraoui Hsaini.
© 2024.
14 pages.
|
|
Imane Cheikh, Khaoula Oulidi Omali, Mohammed Nabil Kabbaj, Mohammed Benbrahim.
© 2024.
30 pages.
|
|
Tahiri Omar, Herrou Brahim, Sekkat Souhail, Khadiri Hassan.
© 2024.
19 pages.
|
|
Sekkat Souhail, Ibtissam El Hassani, Anass Cherrafi.
© 2024.
14 pages.
|
|
Meryeme Bououchma, Brahim Herrou.
© 2024.
14 pages.
|
|
Touria Jdid, Idriss Chana, Aziz Bouazi, Mohammed Nabil Kabbaj, Mohammed Benbrahim.
© 2024.
16 pages.
|
|
Houda Bentarki, Abdelkader Makhoute, Tőkési Karoly.
© 2024.
10 pages.
|
|
|