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A Review on Security Attacks and Protective Strategies of Machine Learning

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Emerging Trends in Computing and Expert Technology (COMET 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 35))

Abstract

Machine learning is the powerful techniques in computing and Linguistics Science. It is broadly applied in various domains such as computer vision, pattern recognition, image processing, network security and Natural language processing. Machine learning (ML) techniques have generated huge social impacts in a variety of applications such as malware detection, spam detection and health care. It is also more sensitive towards security attack. In supervised learning algorithm Machine Learning Models mainly depend upon the large input datasets, called training data and testing data. The slight modifications in the input data will affect the model performance to a greater extent. Many research works have been carried out by analysing various security threats against ML algorithms such as Naïve Bayes Algorithm, Decision tree, Support Vector Machine and Artificial Deep Neural Networks. In this paper, we have explained taxonomy of security threats happened in training and testing stage of learning. Finally we have presented various defending techniques, counter measures which are used in training and testing phases, few security policies to prevent adversarial attacks and make a robust Machine Learning model.

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Authors and Affiliations

  1. SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    K. Meenakshi & G. Maragatham

Authors
  1. K. Meenakshi
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  2. G. Maragatham
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Corresponding author

Correspondence to K. Meenakshi .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, India

    D. Jude Hemanth

  2. Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India

    V. D. Ambeth Kumar

  3. Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India

    S. Malathi

  4. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  5. Faculty of Computer Science, "Alexandru Ioan Cuza" University of Iasi, Iasi, Romania

    Bogdan Patrut

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Meenakshi, K., Maragatham, G. (2020). A Review on Security Attacks and Protective Strategies of Machine Learning. In: Hemanth, D.J., Kumar, V.D.A., Malathi, S., Castillo, O., Patrut, B. (eds) Emerging Trends in Computing and Expert Technology. COMET 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-030-32150-5_109

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