LUARM: An Audit Engine for Insider Misuse Detection

LUARM: An Audit Engine for Insider Misuse Detection

G. Magklaras (University of Plymouth, UK), S. Furnell (University of Plymouth, UK) and M. Papadaki (University of Plymouth, UK)
Copyright: © 2011 |Pages: 13
DOI: 10.4018/jdcf.2011070103
OnDemand PDF Download:
No Current Special Offers


Logging User Actions in Relational Mode (LUARM) is an open source audit engine for Linux. It provides a near real-time snapshot of a number of user action data such as file access, program execution and network endpoint user activities, all organized in easily searchable relational tables. LUARM attempts to solve two fundamental problems of the insider IT misuse domain. The first concerns the lack of insider misuse case data repositories that could be used by post-case forensic examiners to aid an incident investigation. The second problem relates to how information security researchers can enhance their ability to specify accurately insider threats at system level. This paper presents LUARM’s design perspectives and a ’post mortem’ case study of an insider IT misuse incident. The results show that the prototype audit engine has good potential to provide a valuable insight into the way insider IT misuse incidents manifest on IT systems and can be a valuable complement to forensic investigators of IT misuse incidents.
Article Preview

Insider Threat Specification

Threat specifications follow the principles of intrusion specification, a concept which is not new in the information security world. Techniques to describe threats exist for an entire range of information security products, from anti-virus software to several intrusion detection/prevention systems (IDS/IPS) (Bace, 2000) where threats are specified by anomaly detection, pattern matching (also known as misuse detection) mechanisms or a heuristic-based combination of the two. Insider Threat Specification is the process of using a standardized vocabulary to describe in an abstract way how the aspects and behaviour of an insider relate to a security policy defined misuse scenario. Figure 1 shows the information flow of a typical IT misuse detection system. The security specialist translates the Security (and resulting monitoring policy) into a set of misuse scenario signatures, standard descriptions of IT misuse acts that describe the behaviour of a user at process execution, filesystem and network endpoint level (Magklaras et al., 2006). The misuse scenario signatures and collected audit data (Bace, 2000) from the IT infrastructure are fed into a misuse detection engine.

Figure 1.

Information flow in an insider misuse detection system


Vital to insider threat specification is the structure and content of the audit record, at the center of Figure 1. If the audit record is incomplete, in terms of the type of information we need to log or unavailable, because the data are vanished due to bad system design or intentional data corruption, the specification of insider threats is useless. This is one of the primary objectives that LUARM tries to address by providing an evidence rich and reliable audit record format.


Insider Misuse Detection Auditing Requirements

Bace (2000) discusses intrusion detection (and hence misuse detection) as an audit reduction problem. Audit reduction is the process of filtering the relevant information out of the audit records, in order to infer a partially or fully realized threat and excluding information that is irrelevant or redundant. The structure of an audit record is important for a misuse detection system. A good structure has well defined fields that can be easily parsed. Moreover, the structure of the audit record should easily facilitate relational type queries. It is necessary for the information to be applied on the disjunction (OR), conjunction (AND), and negation (NOT) operators, in order to increase the query versatility and speed of response.

Complete Article List

Search this Journal:
Volume 13: 6 Issues (2021): 2 Released, 4 Forthcoming
Volume 12: 4 Issues (2020)
Volume 11: 4 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing