Anomaly detection for automated security log analysis : Comparison of existing techniques and tools
Abstract: Logging security-related events is becoming increasingly important for companies. Log messages can be used for surveillance of a system or to make an assessment of the dam- age caused in the event of, for example, an infringement. Typically, large quantities of log messages are produced making manual inspection for finding traces of unwanted activity quite difficult. It is therefore desirable to be able to automate the process of analysing log messages. One way of finding suspicious behavior within log files is to set up rules that trigger alerts when certain log messages fit the criteria. However, this requires prior knowl- edge about the system and what kind of security issues that can be expected. Meaning that any novel attacks will not be detected with this approach. It can also be very difficult to determine what normal behavior and abnormal behavior is. A potential solution to this problem is machine learning and anomaly-based detection. Anomaly detection is the pro- cess of finding patterns which do not behave like defined notion of normal behavior. This thesis examines the process of going from raw log data to finding anomalies. Both existing log analysis tools and the creation of our own proof-of-concept implementation are used for the analysis. With the use of labeled log data, our implementation was able to reach a precision of 73.7% and a recall of 100%. The advantages and disadvantages of creating our own implementation as opposed to using an existing tool is presented and discussed along with several insights from the field of anomaly detection for log analysis.
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