Name
Abstract | ||
|---|---|---|
Big Data Cyber Security Analytics (BDCA) leverages big data technologies for collecting, storing, and analyzing a large volume of security events data to detect cyber-attacks. Accuracy and response time, being the most important quality concerns for BDCA, are impacted by changes in security events data. Whilst it is promising to adapt a BDCA system's architecture to the changes in security events data for optimizing accuracy and response time, it is important to consider large search space of architectural configurations. Searching a large space of configurations for potential adaptation incurs an overwhelming adaptation time, which may cancel the benefits of adaptation. We present an adaptation approach, QuickAdapt, to enable quick adaptation of a BDCA system. QuickAdapt uses descriptive statistics (e.g., mean and variance) of security events data and fuzzy rules to (re) compose a system with a set of components to ensure optimal accuracy and response time. We have evaluated QuickAdapt for a distributed BDCA system using four datasets. Our evaluation shows that on average QuickAdapt reduces adaptation time by 105× with a competitive adaptation accuracy of 70% as compared to an existing solution. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICECCS.2019.00016 | 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS) |
Keywords | Field | DocType |
big data, cyber security, adaptation, accuracy | Descriptive statistics,Computer science,Computer security,Fuzzy logic,Quality of service,Response time,Feature extraction,Analytics,Big data,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-7281-4647-8 | 0 | 0.34 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faheem Ullah | 1 | 7 | 1.89 |
| Muhammad Ali Babar | 2 | 2349 | 157.18 |