Host-based intrusion detection systems (HIDSs), especially anomaly-based, have received much attention over the past few decades. Over time, however, the existing data sets used for evaluation of a HIDS have lost most of their relevance due to the substantial development of computer systems. To fill this gap, ADFA Linux data set (ADFA-LD) is recently released, which is composed of thousands of system call traces collected from a contemporary Linux local server and expects to be a new benchmark for evaluating a HIDS. In this paper, we perform a preliminary analysis of ADFA-LD, in an attempt to extract useful information for developing new host-based anomaly detection systems (HADSs). In accordance with the general concerns arising from the community, some typical features are analysed particularly against ADFA-LD, such as length, common pattern and frequency. Furthermore, we implement a simple k nearest neighbour (kNN)-based HADS to be evaluated using ADFA-LD. The experimental results show that, although an acceptable performance can be acquired for a few types of attack, there is still a long way to fully understand the complex behaviour resulting from a modern computer system and, finally, realise more intelligent HADSs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.