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Books on the topic 'Security of machine learning classifiers'

Author: Grafiati
Published: 14 March 2023

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1

Learning kernel classifiers: Theory and algorithms. Cambridge, Mass: MIT Press, 2002.

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2

Chen, Xiaofeng, Willy Susilo, and Elisa Bertino, eds. Cyber Security Meets Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6726-5.

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3

Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7.

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4

Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62460-6.

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5

Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7.

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6

Chen, Xiaofeng, Xinyi Huang, and Jun Zhang, eds. Machine Learning for Cyber Security. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30619-9.

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7

Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20102-8.

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8

Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20096-0.

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9

Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1.

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10

Dolev, Shlomi, Oded Margalit, Benny Pinkas, and Alexander Schwarzmann, eds. Cyber Security Cryptography and Machine Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78086-9.

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11

Wang, Gang, Arridhana Ciptadi, and Ali Ahmadzadeh, eds. Deployable Machine Learning for Security Defense. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87839-9.

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12

Halak, Basel, ed. Machine Learning for Embedded System Security. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94178-9.

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13

Dolev, Shlomi, Jonathan Katz, and Amnon Meisels, eds. Cyber Security, Cryptology, and Machine Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07689-3.

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14

Dinur, Itai, Shlomi Dolev, and Sachin Lodha, eds. Cyber Security Cryptography and Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94147-9.

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15

Wang, Gang, Arridhana Ciptadi, and Ali Ahmadzadeh, eds. Deployable Machine Learning for Security Defense. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59621-7.

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16

Dolev, Shlomi, Vladimir Kolesnikov, Sachin Lodha, and Gera Weiss, eds. Cyber Security Cryptography and Machine Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49785-9.

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17

Dolev, Shlomi, and Sachin Lodha, eds. Cyber Security Cryptography and Machine Learning. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60080-2.

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18

Dolev, Shlomi, Danny Hendler, Sachin Lodha, and Moti Yung, eds. Cyber Security Cryptography and Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20951-3.

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19

-P, Tsai Jeffrey J., ed. Intrusion detection: A machine learning approach. London: Imperial College Press, 2011.

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20

Thomas, Tony, Athira P. Vijayaraghavan, and Sabu Emmanuel. Machine Learning Approaches in Cyber Security Analytics. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1706-8.

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21

Krishna, P. Venkata, Sasikumar Gurumoorthy, and Mohammad S. Obaidat. Social Network Forensics, Cyber Security, and Machine Learning. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1456-8.

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22

Maloof, Marcus A., ed. Machine Learning and Data Mining for Computer Security. London: Springer-Verlag, 2006. http://dx.doi.org/10.1007/1-84628-253-5.

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23

Network anomaly detection: A machine learning perspective. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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24

Tanwar, Sarvesh, Sumit Badotra, and Ajay Rana. Machine Learning, Blockchain, and Cyber Security in Smart Environments. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003240310.

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25

Bhattacharjee, Arup, Samir Kr Borgohain, Badal Soni, Gyanendra Verma, and Xiao-Zhi Gao, eds. Machine Learning, Image Processing, Network Security and Data Sciences. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6315-7.

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26

Bhattacharjee, Arup, Samir Kr Borgohain, Badal Soni, Gyanendra Verma, and Xiao-Zhi Gao, eds. Machine Learning, Image Processing, Network Security and Data Sciences. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6318-8.

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27

Mena, Jesus. Machine learning forensics for law enforcement, security, and intelligence. Boca Raton, FL: Taylor & Francis, 2011.

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28

Doriya, Rajesh, Badal Soni, Anupam Shukla, and Xiao-Zhi Gao, eds. Machine Learning, Image Processing, Network Security and Data Sciences. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5868-7.

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29

Khare, Nilay, Deepak Singh Tomar, Mitul Kumar Ahirwal, Vijay Bhaskar Semwal, and Vaibhav Soni, eds. Machine Learning, Image Processing, Network Security and Data Sciences. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24352-3.

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30

Khare, Nilay, Deepak Singh Tomar, Mitul Kumar Ahirwal, Vijay Bhaskar Semwal, and Vaibhav Soni, eds. Machine Learning, Image Processing, Network Security and Data Sciences. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24367-7.

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31

Yu, Philip S. Machine Learning in Cyber Trust: Security, Privacy, and Reliability. Boston, MA: Springer-Verlag US, 2009.

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32

Lecuyer, Mathias. Security, Privacy, and Transparency Guarantees for Machine Learning Systems. [New York, N.Y.?]: [publisher not identified], 2019.

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33

Shi, Kevin. Cryptographic approaches to security and optimization in machine learning. [New York, N.Y.?]: [publisher not identified], 2020.

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34

Machine learning forensics for law enforcement, security, and intelligence. Boca Raton, FL: Taylor & Francis, 2011.

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35

Herbrich, Ralf. Learning Kernel Classifiers: Theory and Algorithms. MIT Press, 2018.

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36

Herbrich, Ralf. Learning Kernel Classifiers: Theory and Algorithms. MIT Press, 2022.

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37

Herbrich, Ralf, and Francis Bach. Learning Kernel Classifiers: Theory and Algorithms. MIT Press, 2001.

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38

Herbrich, Ralf. Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning). The MIT Press, 2001.

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39

(Editor), Peter J. Bartlett, Bernhard Schölkopf (Editor), Dale Schuurmans (Editor), and Alex J. Smola (Editor), eds. Advances in Large-Margin Classifiers (Neural Information Processing). The MIT Press, 2000.

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40

Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described by a set of features. These techniques can be applied to learn knowledge required for a variety of problems in computational linguistics ranging from part-of-speech tagging and syntactic parsing to word-sense disambiguation and anaphora resolution. Finally, this article reviews the applications to a variety of these problems, such as morphology, part-of-speech tagging, word-sense disambiguation, syntactic parsing, semantic parsing, information extraction, and anaphora resolution.
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41

Vidales, A. Machine Learning with Matlab. Supervised Learning: Knn Classifiers, Ensemble Learning, Random Forest, Boosting and Bagging. Independently Published, 2019.

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42

Joseph, Anthony D., Blaine Nelson, J. D. Tygar, and Benjamin I. P. Rubinstein. Adversarial Machine Learning. Cambridge University Press, 2019.

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43

Joseph, Anthony D., Blaine Nelson, Benjamin I. P. Rubinstein, and J. D. Tygar. Adversarial Machine Learning. Cambridge University Press, 2019.

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44

Chen, Xiaofeng, Elisa Bertino, and Willy Susilo. Cyber Security Meets Machine Learning. Springer Singapore Pte. Limited, 2021.

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45

Chen, Xiaofeng, Elisa Bertino, and Willy Susilo. Cyber Security Meets Machine Learning. Springer, 2022.

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46

Nautiyal, Lata. Machine Learning for Cyber Security. de Gruyter GmbH, Walter, 2022.

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47

Nautiyal, Lata. Machine Learning for Cyber Security. de Gruyter GmbH, Walter, 2022.

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48

Halak, Basel. Machine Learning for Embedded System Security. Springer International Publishing AG, 2022.

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49

AI, Machine Learning and Deep Learning: A Security Perspective. Taylor & Francis Group, 2023.

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50

AI, Machine Learning and Deep Learning: A Security Perspective. Taylor & Francis Group, 2023.

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