Systematization of Knowledge from Intrusion Detection Models - October 2014

Public Audience
Purpose: To highlight project progress. Information is generally at a higher level which is accessible to the interested public. All information contained in the report (regions 1-3) is a Government Deliverable/CDRL.

PI(s):  Huaiyu Dai, Andy Meneely
Researchers:

Xiaofan He, Yufan Huang, Nuthan Munaiah, Kevin Campusano Gonzalez

HARD PROBLEM(S) ADDRESSED

• Security Metrics and Models - The project aims to establish common criteria for evaluating and systematizing knowledge contributed by research on intrusion detection models.
• Resilient Architectures - Robust intrusion detection models serve to make large systems more resilient to attack.
• Scalability and Composability - Intrusion detection models deal with large data sets every day, so scale is always a significant concern.
• Humans - A key aspect of intrusion detection is interpreting the output and acting upon it, which inherently involves humans. Furthermore, intrusion detection models are ultimately simulations of human behavior.

PUBLICATIONS
Report papers written as a results of this research. If accepted by or submitted to a journal, which journal. If presented at a conference, which conference.

None as of yet.

ACCOMPLISHMENT HIGHLIGHTS

• We have collected and have begun analysis on nearly 300 technical papers on intrusion detection. We are in the process of classifying major quantitative metrics used in existing intrusion detection models and systems. We are also refining our research questions to explore how researchers currently evaluate their intrusion detection models.