abstract: The volume and frequency of cyber attacks have exploded in recent years. Organizations subscribe to multiple threat intelligence feeds to increase their knowledge base and better equip their security teams with the latest information in threat intelligence domain. Though such subscriptions add intelligence and can help in taking more informed decisions, organizations have to put considerable efforts in facilitating and analyzing a large number of threat indicators. This problem worsens further, due to a large number of false positives and irrelevant events detected as threat indicators by existing threat feed sources. It is often neither practical nor cost-effective to analyze every single alert considering the staggering volume of indicators. The very reason motivates to solve the overcrowded threat indicators problem by prioritizing and filtering them.
To overcome above issue, I explain the necessity of determining how likely a reported indicator is malicious given the evidence and prioritizing it based on such determination. Confidence Score Measurement system (CSM) introduces the concept of confidence score, where it assigns a score of being malicious to a threat indicator based on the evaluation of different threat intelligence systems. An indicator propagates maliciousness to adjacent indicators based on relationship determined from behavior of an indicator. The propagation algorithm derives final confidence to determine overall maliciousness of the threat indicator. CSM can prioritize the indicators based on confidence score; however, an analyst may not be interested in the entire result set, so CSM narrows down the results based on the analyst-driven input. To this end, CSM introduces the concept of relevance score, where it combines the confidence score with analyst-driven search by applying full-text search techniques. It prioritizes the results based on relevance score to provide meaningful results to the analyst. The analysis shows the propagation algorithm of CSM linearly scales with larger datasets and achieves 92% accuracy in determining threat indicators. The evaluation of the result demonstrates the effectiveness and practicality of the approach. / Dissertation/Thesis / Masters Thesis Computer Science 2017
| Identifer | oai:union.ndltd.org:asu.edu/item:44998 |
| Date | January 2017 |
| Contributors | Modi, Ajay (Author), Ahn, Gail-Joon (Advisor), Zhao, Ziming (Committee member), Doupe, Adam (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 91 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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