This paper presents a proficient method for extracting the HLAC-like features. Different masks from 2D-monochrome image sequences will be used for extracting the features. The Mutual Information Quotient (MIQ) is the basis for selecting the most relevant features. In this study, a bank of Bayesian classifier is adopted for recognition. The distinguished features are given to a bank of seven parallel Bayesian classifiers. For recognlzmg a particular facial expression, each Bayesian classifier is trained. The outputs of all classifiers are then combined using a maximum function. The test will be performed on images from the JAFFE database.

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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