| LDR | | 03316cmm u2200529Ii 4500 |
| 001 | | 000000317314 |
| 003 | | OCoLC |
| 005 | | 20230525182329 |
| 006 | | m d |
| 007 | | cr cnu---unuuu |
| 008 | | 191004t20192019dcua ob 000 0 eng d |
| 020 | |
▼a 9780309496124
▼q (electronic bk.) |
| 020 | |
▼a 0309496128
▼q (electronic bk.) |
| 020 | |
▼z 9780309496094
▼q (paperback) |
| 020 | |
▼z 0309496098
▼q (paperback) |
| 035 | |
▼a 2264538
▼b (N$T) |
| 035 | |
▼a (OCoLC)1121628374 |
| 040 | |
▼a N$T
▼b eng
▼e rda
▼e pn
▼c N$T
▼d N$T
▼d CUS
▼d 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a HV6773.15.C97 |
| 050 | 4 |
▼a Q325.5 |
| 082 | 04 |
▼a 006.31
▼2 23 |
| 100 | 1 |
▼a Casola, Linda Clare,
▼d 1982-,
▼e rapporteur. |
| 245 | 10 |
▼a Robust machine learning algorithms and systems for detection and mitigation of adversarial attacks and anomalies :
▼b proceedings of a workshop /
▼c Linda Casola and Dionna Ali, rapporteurs ; Intelligence Community Studies Board ; Computer Science and Telecommunications Board, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine. |
| 260 | |
▼a Washington, DC :
▼b the National Academies Press,
▼c [2019]. |
| 300 | |
▼a 1 online resource (xii, 69 pages) :
▼b color illustrations |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
| 338 | |
▼a online resource
▼b cr
▼2 rdacarrier |
| 504 | |
▼a Includes bibliographical references (pages 53-54). |
| 505 | 0 |
▼a Introduction -- Plenary session -- Adversarial attacks -- Detection and mitigation of adversarial attacks and anomalies -- Enablers of machine learning algorithms and systems -- Recent trends i machine learning, parts 1 and 2 -- Plenary session -- Recent trends in machine learning, part 3 -- Machine learning systems --References -- Appendixes |
| 520 | |
▼a "The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11-12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop"--Publisher's description |
| 588 | |
▼a Online resource; title from PDF title page (National Academies Press, viewed March 26, 2019). |
| 590 | |
▼a Master record variable field(s) change: 050, 650 |
| 650 | 0 |
▼a Machine learning
▼v Congresses. |
| 650 | 0 |
▼a Computer algorithms
▼v Congresses. |
| 650 | 0 |
▼a Cyberterrorism
▼x Prevention
▼v Congresses. |
| 650 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a Computer security. |
| 650 | 0 |
▼a Computer networks
▼x Security measures. |
| 700 | 1 |
▼a Ali, Dionna,
▼e rapporteur. |
| 710 | 2 |
▼a National Academies of Sciences, Engineering, and Medicine (U.S.).
▼b Intelligence Community Studies Board,
▼e issuing body. |
| 710 | 2 |
▼a National Academies of Sciences, Engineering, and Medicine (U.S.).
▼b Computer Science and Telecommunications Board,
▼e issuing body. |
| 711 | 2 |
▼a Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies (Workshop)
▼d (2018 :
▼c Berkeley, Ca.),
▼j author. |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2264538 |
| 938 | |
▼a EBSCOhost
▼b EBSC
▼n 2264538 |
| 990 | |
▼a 관리자 |
| 994 | |
▼a 92
▼b N$T |