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020 _a9783031023545
_9978-3-031-02354-5
024 7 _a10.1007/978-3-031-02354-5
_2doi
050 4 _aQA76.9.A25
072 7 _aUR
_2bicssc
072 7 _aUTN
_2bicssc
072 7 _aCOM053000
_2bisacsh
072 7 _aUR
_2thema
072 7 _aUTN
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082 0 4 _a005.8
_223
100 1 _aYao, Danfeng (Daphne).
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981129
245 1 0 _aAnomaly Detection as a Service
_h[electronic resource] :
_bChallenges, Advances, and Opportunities /
_cby Danfeng (Daphne) Yao, Xiaokui Shu, Long Cheng, Salvatore J. Stolfo.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXV, 157 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Information Security, Privacy, and Trust,
_x1945-9750
505 0 _aPreface -- Acknowledgments -- Introduction -- Threat Models -- Local vs. Global Program Anomaly Detection -- Program Analysis in Data-driven Anomaly Detection -- Anomaly Detection in Cyber-Physical Systems -- Anomaly Detection on Network Traffic -- Automation and Evaluation for Anomaly Detection Deployment -- Anomaly Detection from the Industry's Perspective -- Exciting New Problems and Opportunities -- Bibliography -- Authors' Biographies -- Index.
520 _aAnomaly detection has been a long-standing security approach with versatile applications, ranging from securing server programs in critical environments, to detecting insider threats in enterprises, to anti-abuse detection for online social networks. Despite the seemingly diverse application domains, anomaly detection solutions share similar technical challenges, such as how to accurately recognize various normal patterns, how to reduce false alarms, how to adapt to concept drifts, and how to minimize performance impact. They also share similar detection approaches and evaluation methods, such as feature extraction, dimension reduction, and experimental evaluation. The main purpose of this book is to help advance the real-world adoption and deployment anomaly detection technologies, by systematizing the body of existing knowledge on anomaly detection. This book is focused on data-driven anomaly detection for software, systems, and networks against advanced exploits and attacks, but also touches on a number of applications, including fraud detection and insider threats. We explain the key technical components in anomaly detection workflows, give in-depth description of the state-of-the-art data-driven anomaly-based security solutions, and more importantly, point out promising new research directions. This book emphasizes on the need and challenges for deploying service-oriented anomaly detection in practice, where clients can outsource the detection to dedicated security providers and enjoy the protection without tending to the intricate details.
650 0 _aData protection.
_97245
650 1 4 _aData and Information Security.
_931990
700 1 _aShu, Xiaokui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981130
700 1 _aCheng, Long.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981131
700 1 _aStolfo, Salvatore J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981132
710 2 _aSpringerLink (Online service)
_981133
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031002373
776 0 8 _iPrinted edition:
_z9783031012266
776 0 8 _iPrinted edition:
_z9783031034824
830 0 _aSynthesis Lectures on Information Security, Privacy, and Trust,
_x1945-9750
_981134
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02354-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c85111
_d85111