Loading...
Please wait, while we are loading the content...
Similar Documents
Classification of Log Files with Limited Labeled Data
| Content Provider | ACM Digital Library |
|---|---|
| Author | Engel, Thomas State, Radu Hommes, Stefan |
| Abstract | We address the problem of anomaly detection in log files that consist of a huge number of records. In order to achieve this task, we demonstrate label propagation as a semi-supervised learning technique. The strength of this approach lies in the small amount of labelled data that is needed to label the remaining data. This is an advantage since labelled data needs human expertise which comes at a high cost and becomes infeasible for big datasets. Even though our approach is generally applicable, we focus on the detection of anomalous records in firewall log files. This requires a separation of records into windows which are compared using different distance functions to determine their similarity. Afterwards, we apply label propagation to label a complete dataset in only a limited number of iterations. We demonstrate our approach on a realistic dataset from an ISP. |
| Starting Page | 1 |
| Ending Page | 6 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450326728 |
| DOI | 10.1145/2554666.2554668 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-15 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Log files Kullback-leibler divergence Label propagation Firewall |
| Content Type | Text |
| Resource Type | Article |
