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00000cam c2200205 c 4500
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000005103333
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20220907105630
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220405s2017 uk eng
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020
▼a 9781107043466 (hardback)
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035
▼a (KERIS)REF000018391222
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040
▼a DLC
▼b eng
▼c DLC
▼d 211070
▼e rda -
050
▼a Q325.5
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090
▼a Q325.5
▼b J83 -
245
▼a Adversarial machine learning /
▼d Anthony D. Joseph ,
▼e Blaine Nelson ,
▼e Benjamin I.P. Rubinstein ,
▼e J.D. Tygar -
260
▼a Cambridge, United Kingdom ;
▼a New York, NY :
▼b Cambridge University Press,
▼c 2017 -
300
▼a 325 p ;
▼c 25 cm -
504
▼a Includes bibliographical references and index.
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520
▼a "Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race"--
▼c Provided by publisher. -
653
▼a Machine learning.
-
653
▼a Computer security.
-
700
▼a Joseph, Anthony D.
-
700
▼a Nelson, Blaine Nelson
-
700
▼a Rubinstein, Benjamin I.P. Rubinstein
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상세정보
Adversarial machine learning
QR코드| 자료유형 : | 단행본 |
|---|---|
| ISBN : | 9781107043466 (hardback) |
| 분류기호 : | Q325.5 |
| 서명/저자사항 : | Adversarial machine learning / Anthony D. Joseph , Blaine Nelson , Benjamin I.P. Rubinstein , J.D. Tygar |
| 발행사항 : | Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2017 |
| 형태사항 : | 325 p ; 25 cm |
| 서지주기 : | Includes bibliographical references and index. |
| 요약 : | "Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race"-- Provided by publisher. |
| 개인저자 : | Joseph, Anthony D. |
| 개인저자 : | Nelson, Blaine Nelson |
| 개인저자 : | Rubinstein, Benjamin I.P. Rubinstein |
| 언어 | 영어 |
예약
- 1. 예약현황은 홈페이지 로그인 후 예약 페이지에 확인 가능합니다.
- 2. 도착 통보된 예약자료 대출을 원하지 않는 경우에는 예약 현황에서 취소할 수 있습니다.
- 3. 기타 문의사항은 도서관에 문의 바랍니다.
무인예약대출
- 1. 무인예약대출 현황은 홈페이지 로그인 후 무인예약대출 페이지에 확인 가능합니다.
- 2. 무인예약대출자료 대출을 원하지 않는 경우에는 무인예약대출 페이지에서 신청 또는 접수상태인 경우만 취소할 수 있습니다.
- 3. 희망대출일은 신청일로부터 최대 1주일 까지 가능합니다.
- 4. 희망대출일을 선택하지 않은 경우 대출대기 통보 후 1주일까지 기기에서 대출가능합니다.
- 5. 기타 문의사항은 도서관에 문의 바랍니다.
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