Rare association rule mining for data stream

Vanamala, Sunitha; Sree, L. Padma; Bhavani, S. Durga

doi:10.1109/iccct2.2014.7066696

Cited by 6 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3%-10%), the confidence is always set to maximum. The confidence value is an indication of how often the rule has been found to be true, and setting it to 100% ensures the discovery of the most interesting as well as reliable association rules [79] within the limits of SR.…”

Section: Establishing Support Valuementioning

confidence: 99%

Discovering and utilising expert knowledge from security event logs

Khan

Parkinson

2019

Journal of Information Security and Applications

View full text Add to dashboard Cite

Vulnerability assessment and security configuration of computer systems is heavily dependent on human experts, which are widely attributed as being in short supply. This can result in a system being left insecure because of the lack of easily accessible experience and specialist resources. While performing security tasks, human experts often revert to a system's event logs to establish security information (configuration changes, errors, etc.). However, finding and exploiting knowledge from event logs is a challenging and time-consuming task for non-experts. Hence there is a strong need to provide mechanisms to make the process easier for security experts, as well as providing tools for those with significantly less security expertise. In this paper, we present a novel technique to process security event logs of a system that have been evaluated and configured by a security expert, extract key domain knowledge indicative of human decision making, and automatically apply acquired knowledge to previously unseen systems by non-experts to propose security improvements. The proposed solution utilises rule mining algorithms to extract security actions from event log entries. The set of identified rules is represented as a domain action model. The domain model and problem instance generated from a previously unseen system can then be used to produce a plan-of-action, which can be exploited by non-professionals to improve their system's security. Empirical analysis is subsequently performed on 21 event logs, where the acquired domain model and identified plans are discussed in terms of accuracy and performance.

show abstract

Section: Establishing Support Valuementioning

confidence: 99%