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In this paper, we analyze an anomaly basedintrusion detection system (IDS) for outlier detection in hardware profile using statistical techniques: Chi-square distribution, Gaussian mixture distribution and Principal component analysis. Anomaly detection based methods can detect new intrusions but they suffer from false alarms. Host based Intrusion Detection Systems (HIDSs) use anomaly detection to identify malicious attacks i.e. intrusion. The features are shown by large set of dimensions and the system becomes extremely slow during processing this huge amount of data (especially, host based). We show the comparative results using three different approaches: Principal Component Analysis (PCA), Chi-square distribution andcluster with Gaussian mixture distribution. We get good results using these techniques.