Cost-Effective Malware Detection as a Service over Serverless Cloud Using Deep Reinforcement Learning

Yoni Birman, Shaked Hindi, Gilad Katz, Asaf Shabtai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The current trends of cloud computing in general, and serverless computing in particular, affect multiple aspects of organizational activity. Organizations of all sizes are transitioning parts of their operations off-premise in order to reduce costs and scale their operations more efficiently. The field of network security is no exception, with many organizations taking advantage of the distributed and scalable cloud environment. Since the charging model for serverless computing is "pay as you go" (i.e., payment per action), a reduction in the number of required computations translates into significant cost savings. This understanding is also relevant to the field of malware detection, where organizations often deploy multiple types of detectors to increase detection accuracy. In this study, we utilize deep reinforcement learning to reduce computational costs in the cloud by selectively querying only a subset of available detectors. We demonstrate that our approach is not only effective both for on-premise and cloud-based computing architectures, but that applying it to serverless computing can reduce costs by an order of magnitude while maintaining near-optimal performance.

Original languageAmerican English
Title of host publicationProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
EditorsLaurent Lefevre, Carlos A. Varela, George Pallis, Adel N. Toosi, Omer Rana, Rajkumar Buyya
Pages420-429
Number of pages10
ISBN (Electronic)9781728160955
DOIs
StatePublished - 1 May 2020
Event20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020 - Melbourne, Australia
Duration: 11 May 202014 May 2020

Publication series

NameProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020

Conference

Conference20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
Country/TerritoryAustralia
CityMelbourne
Period11/05/2014/05/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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