Evasion Attack on Text Classified Training Datasets
D. Suja Mary1, M. Suriakala2
1D. Suja Mary, M.SC, M.Phil, Part Time Research Scholar, Assistant Professor, Department of Computer Applications, J.H.A Agarsen College, Madhavaram, Chennai (Tamil Nadu), India.
2Dr. M. Suriakala, M.SC, M.Phil, Ph.D, Assistant Professor, Department of Computer Science, Government Arts College for Men, Nandanam, Chennai (Tamil Nadu), India.
Manuscript received on 15 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 06 September 2019 | PP: 45-50 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F10090886S19/19©BEIESP | DOI: 10.35940/ijeat.F1009.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Machine learning algorithms are widespread used in real world training data classification and detection malware. The learning algorithms to detect malware adversarial manipulated training datasets in evasion. The evasion attacker has certain knowledge on training datasets either internal in deploying time attack or external attack do based on adversarial knowledge. Evasion attack targeted document properties features malware. To present this paper, to do an evasion attack on collected text documents using extraction keyword and find mean words using Naive Bayes models . Also to analyses different machine learning algorithms classification on evasion attacked training datasets and discussed defense methods to prevent training dataset from evasion attack.
Keywords: Adversarial Learning, Machine Learning, Malware, Evasion Attack.
Scope of the Article: Machine Learning