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This paper proposes a methodology for host-based anomaly detection using asemi-supervised algorithm namely one-class classifier combined with a PCA-basedfeature extraction technique called Eigentraces on system call trace data. Theone-class classification is based on generating a set of artificial data usinga reference distribution and combining the target class probability functionwith artificial class density function to estimate the target class densityfunction through the Bayes formulation. The benchmark dataset, ADFA-LD, isemployed for the simulation study. ADFA-LD dataset contains thousands of systemcall traces collected during various normal and attack processes for the Linuxoperating system environment. In order to pre-process and to extract features,windowing on the system call trace data followed by the principal componentanalysis which is named as Eigentraces is implemented. The target classprobability function is modeled separately by Radial Basis Function neuralnetwork and Random Forest machine learners for performance comparison purposes.The simulation study showed that the proposed intrusion detection system offershigh performance for detecting anomalies and normal activities with respect toa set of well-accepted metrics including detection rate, accuracy, and missedand false alarm rates.

Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data