Data has become more and more important to individuals, organizations, and companies, and therefore, safeguarding these sensitive data in relational databases has become a critical issue. However, despite traditional security mechanisms, attacks directed to databases still occur. Thus, an intrusion detection system (IDS) specifically for the database that can provide protection from all possible malicious users is necessary. In this paper, we present a random forests (RF) method with weighted voting for the task of anomaly detection. RF is a graph-based technique suitable for modeling SQL queries, and weighted voting enhances its capabilities by balancing the voting impact of each tree. Experiments show that RF with weighted voting exhibits a more superior performance consistency, as well as better error rates with increasing number of trees, compared to conventional RF. Moreover, it outperforms all other stateof- The-art data mining algorithms in terms of false positive rate (0.076) and false negative rate (0.0028).
|Title of host publication||Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings|
|Editors||Ryszard Tadeusiewicz, Lotfi A. Zadeh, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Jacek M. Zurada|
|Number of pages||13|
|Publication status||Published - 2015|
|Event||14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015 - Zakopane, Poland|
Duration: 2015 Jun 14 → 2015 Jun 18
|Name||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|Other||14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015|
|Period||15/6/14 → 15/6/18|
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2015.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)