Online and scalable adaptive cyber defense

Benjamin W. Priest, George Cybenko, Satinder Singh, Massimiliano Albanese, Peng Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter introduces cyber security researchers to key concepts in the data streaming and sketching literature that are relevant to Adaptive Cyber Defense (ACD) and Moving Target Defense (MTD). We begin by observing the challenges met in the big data realm. Particular attention is paid to the need for compact representations of large datasets, as well as designing algorithms that are robust to changes in the underlying dataset. We present a summary of the key research and tools developed in the data stream and sketching literature, with a focus on practical applications. Finally, we present several concrete extensions to problems related to ACD applications throughout this book, with a focus on improving scalability.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages232-261
Number of pages30
DOIs
StatePublished - Jan 1 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11830 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Sketching
Scalability
Streaming Data
Moving Target
Concretes
Data Streams
Large Data Sets
Big data
Concepts

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Priest, B. W., Cybenko, G., Singh, S., Albanese, M., & Liu, P. (2019). Online and scalable adaptive cyber defense. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 232-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11830 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-30719-6_10
Priest, Benjamin W. ; Cybenko, George ; Singh, Satinder ; Albanese, Massimiliano ; Liu, Peng. / Online and scalable adaptive cyber defense. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, 2019. pp. 232-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Priest, BW, Cybenko, G, Singh, S, Albanese, M & Liu, P 2019, Online and scalable adaptive cyber defense. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11830 LNCS, Springer Verlag, pp. 232-261. https://doi.org/10.1007/978-3-030-30719-6_10

Online and scalable adaptive cyber defense. / Priest, Benjamin W.; Cybenko, George; Singh, Satinder; Albanese, Massimiliano; Liu, Peng.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, 2019. p. 232-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11830 LNCS).

Research output: Chapter in Book/Report/Conference proceedingChapter

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Priest BW, Cybenko G, Singh S, Albanese M, Liu P. Online and scalable adaptive cyber defense. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2019. p. 232-261. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-30719-6_10