Adversarial machine learning:
"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 inferen...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2019
|
| Schlagworte: | |
| Zusammenfassung: | "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"... |
| Beschreibung: | Includes bibliographical references and index Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xii, 325 Seiten Illustrationen, Diagramme |
| ISBN: | 9781107043466 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV044523251 | ||
| 003 | DE-604 | ||
| 005 | 20220621 | ||
| 007 | t | ||
| 008 | 171005s2019 xxka||| |||| 00||| eng d | ||
| 010 | |a 017026016 | ||
| 020 | |a 9781107043466 |c Hb.: £ 64.99 |9 978-1-107-04346-6 | ||
| 035 | |a (OCoLC)993877181 | ||
| 035 | |a (DE-599)BVBBV044523251 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxk |c GB | ||
| 049 | |a DE-91G |a DE-706 |a DE-83 | ||
| 050 | 0 | |a Q325.5 | |
| 082 | 0 | |a 006.3/1 |2 23 | |
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a ST 277 |0 (DE-625)143643: |2 rvk | ||
| 084 | |a DAT 708f |2 stub | ||
| 100 | 1 | |a Joseph, Anthony D. |e Verfasser |0 (DE-588)1188483501 |4 aut | |
| 245 | 1 | 0 | |a Adversarial machine learning |c Anthony D. Joseph (University of California, Berkeley), Blaine Nelson (Google), Benjamin I.P. Rubinstein (University of Melbourne), J.D. Tygar (University of California, Berkeley) |
| 264 | 1 | |a Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore |b Cambridge University Press |c 2019 | |
| 300 | |a xii, 325 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 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"... | ||
| 650 | 4 | |a COMPUTERS / Security / General / bisacsh | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Computer security | |
| 650 | 4 | |a COMPUTERS / Security / General | |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computersicherheit |0 (DE-588)4274324-2 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Computersicherheit |0 (DE-588)4274324-2 |D s |
| 689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Nelson, Blaine |e Verfasser |0 (DE-588)1188485997 |4 aut | |
| 700 | 1 | |a Rubinstein, Benjamin I. P. |e Verfasser |0 (DE-588)1188485393 |4 aut | |
| 700 | 1 | |a Tygar, J.D. |e Verfasser |0 (DE-588)1188485938 |4 aut | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-029922704 | ||
Datensatz im Suchindex
| _version_ | 1804177868139266048 |
|---|---|
| any_adam_object | |
| author | Joseph, Anthony D. Nelson, Blaine Rubinstein, Benjamin I. P. Tygar, J.D |
| author_GND | (DE-588)1188483501 (DE-588)1188485997 (DE-588)1188485393 (DE-588)1188485938 |
| author_facet | Joseph, Anthony D. Nelson, Blaine Rubinstein, Benjamin I. P. Tygar, J.D |
| author_role | aut aut aut aut |
| author_sort | Joseph, Anthony D. |
| author_variant | a d j ad adj b n bn b i p r bip bipr j t jt |
| building | Verbundindex |
| bvnumber | BV044523251 |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.5 |
| callnumber-search | Q325.5 |
| callnumber-sort | Q 3325.5 |
| callnumber-subject | Q - General Science |
| classification_rvk | ST 300 ST 277 |
| classification_tum | DAT 708f |
| ctrlnum | (OCoLC)993877181 (DE-599)BVBBV044523251 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03161nam a2200517 c 4500</leader><controlfield tag="001">BV044523251</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220621 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">171005s2019 xxka||| |||| 00||| eng d</controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="a">017026016</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781107043466</subfield><subfield code="c">Hb.: £ 64.99</subfield><subfield code="9">978-1-107-04346-6</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)993877181</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV044523251</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxk</subfield><subfield code="c">GB</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-83</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">Q325.5</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/1</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 277</subfield><subfield code="0">(DE-625)143643:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 708f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Joseph, Anthony D.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1188483501</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adversarial machine learning</subfield><subfield code="c">Anthony D. Joseph (University of California, Berkeley), Blaine Nelson (Google), Benjamin I.P. Rubinstein (University of Melbourne), J.D. Tygar (University of California, Berkeley)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xii, 325 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Hier auch später erschienene, unveränderte Nachdrucke</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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"...</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS / Security / General / bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer security</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS / Security / General</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Computersicherheit</subfield><subfield code="0">(DE-588)4274324-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Computersicherheit</subfield><subfield code="0">(DE-588)4274324-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nelson, Blaine</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1188485997</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rubinstein, Benjamin I. P.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1188485393</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tygar, J.D.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1188485938</subfield><subfield code="4">aut</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-029922704</subfield></datafield></record></collection> |
| id | DE-604.BV044523251 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:54:55Z |
| institution | BVB |
| isbn | 9781107043466 |
| language | English |
| lccn | 017026016 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029922704 |
| oclc_num | 993877181 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-706 DE-83 |
| owner_facet | DE-91G DE-BY-TUM DE-706 DE-83 |
| physical | xii, 325 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Joseph, Anthony D. Verfasser (DE-588)1188483501 aut Adversarial machine learning Anthony D. Joseph (University of California, Berkeley), Blaine Nelson (Google), Benjamin I.P. Rubinstein (University of Melbourne), J.D. Tygar (University of California, Berkeley) Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore Cambridge University Press 2019 xii, 325 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Hier auch später erschienene, unveränderte Nachdrucke "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"... COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Computersicherheit (DE-588)4274324-2 gnd rswk-swf Computersicherheit (DE-588)4274324-2 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Nelson, Blaine Verfasser (DE-588)1188485997 aut Rubinstein, Benjamin I. P. Verfasser (DE-588)1188485393 aut Tygar, J.D. Verfasser (DE-588)1188485938 aut |
| spellingShingle | Joseph, Anthony D. Nelson, Blaine Rubinstein, Benjamin I. P. Tygar, J.D Adversarial machine learning COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Maschinelles Lernen (DE-588)4193754-5 gnd Computersicherheit (DE-588)4274324-2 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4274324-2 |
| title | Adversarial machine learning |
| title_auth | Adversarial machine learning |
| title_exact_search | Adversarial machine learning |
| title_full | Adversarial machine learning Anthony D. Joseph (University of California, Berkeley), Blaine Nelson (Google), Benjamin I.P. Rubinstein (University of Melbourne), J.D. Tygar (University of California, Berkeley) |
| title_fullStr | Adversarial machine learning Anthony D. Joseph (University of California, Berkeley), Blaine Nelson (Google), Benjamin I.P. Rubinstein (University of Melbourne), J.D. Tygar (University of California, Berkeley) |
| title_full_unstemmed | Adversarial machine learning Anthony D. Joseph (University of California, Berkeley), Blaine Nelson (Google), Benjamin I.P. Rubinstein (University of Melbourne), J.D. Tygar (University of California, Berkeley) |
| title_short | Adversarial machine learning |
| title_sort | adversarial machine learning |
| topic | COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Maschinelles Lernen (DE-588)4193754-5 gnd Computersicherheit (DE-588)4274324-2 gnd |
| topic_facet | COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Maschinelles Lernen Computersicherheit |
| work_keys_str_mv | AT josephanthonyd adversarialmachinelearning AT nelsonblaine adversarialmachinelearning AT rubinsteinbenjaminip adversarialmachinelearning AT tygarjd adversarialmachinelearning |