Host Intrusion Detection Systems (HIDSs) are critical tools needed to provide in-depth security to computer systems. Quantitative metrics for HIDSs are necessary for comparing HIDSs or determining the optimal operational point of a HIDS. While HIDSs and Network Intrusion Detection Systems (NIDSs) greatly differ, similar evaluations have been performed on both types of IDSs by assessing metrics associated with the classification algorithm (e.g., true positives, false positives). This dissertation motivates the necessity of additional characteristics to better describe the performance and effectiveness of HIDSs.

The proposed additional characteristics are the ability to collect data where an attack manifests (visibility), the ability of the HIDS to resist attacks in the event of an intrusion (attack resiliency), the ability to timely detect attacks (efficiency), and the ability of the HIDS to avoid interfering with the normal functioning of the system under supervision (transparency). For each characteristic, we propose corresponding quantitative evaluation metrics.

To measure the effect of visibility on the detection of attacks, we introduce the probability of attack manifestation and metrics related to data quality (i.e., relevance of the data regarding the attack to be detected). The metrics were applied empirically to evaluate filesystem data, which is the data source for many HIDSs.

To evaluate attack resiliency we introduce the probability of subversion, which we estimate by measuring the isolation between the HIDS and the system under supervision. Additionally, we provide methods to evaluate time delays for efficiency, and performance overhead for transparency. The proposed evaluation methods are then applied to compare two HIDSs.

Finally, we show how to integrate the proposed measurements into a cost framework. First, mapping functions are established to link operational costs of the HIDS with the metrics proposed for efficiency and transparency. Then we show how the number of attacks detected by the HIDS not only depends on detection accuracy, but also on the evaluation results of visibility and attack resiliency.