Name
Abstract | ||
|---|---|---|
A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expression (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities.In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naïve solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naïve solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CSCI.2017.2 | 2017 International Conference on Computational Science and Computational Intelligence (CSCI) |
Keywords | Field | DocType |
Cyber Defense Technologies and Strategies,Novel Security Tools,Network Security,Innovative Tools for Cyber Defense Technologies,Intrusion Detection Techniques,Full/Regular Research Papers,CSCI-ISCW | False positive rate,Early detection,Regular expression,Computer security,Computer science,Compromise,Adversary,True positive rate | Journal |
Volume | ISSN | ISBN |
abs/1712.07671 | This will be in the proceedings of the 4th Annual Conf. on
Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16,
2017 in Las Vegas, Nevada, USA | 978-1-5386-2653-5 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin E. Doak | 1 | 3 | 1.14 |
| Joe B. Ingram | 2 | 4 | 1.15 |
| Sam A. Mulder | 3 | 0 | 0.34 |
| J. H. Naegle | 4 | 1 | 0.77 |
| Jonathan A. Cox | 5 | 1 | 0.82 |
| James B. Aimone | 6 | 1 | 1.39 |
| Kevin R. Dixon | 7 | 1 | 0.69 |
| Conrad D. James | 8 | 11 | 5.57 |
| David R. Follett | 9 | 0 | 0.34 |