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Extracting best feature set to reinforce discrimination is always a challenge in machine learning. In this paper, a method named General Locally Linear Combination (GLLC) is proposed to extract automatic features using a deep autoencoder and also to reconstruct a sample based on the other samples sparsely in a low-dimensional space. Extracting features along with the discrimination ability of the...
Previous research has shown that hidden Markov model (HMM) is a compelling option for malware identification. However, some advanced metamorphic malware have proven to be more challenging to detect with these techniques. In this paper, we separated the importance of the some part of the malware files to train the HMMs aiming at extracting the significant sequences of malware opcodes. These parts have...
Identifying the most characterizing features of observed data is critical for minimizing the classification error. Feature selection is the process of identifying a small subset of highly predictive features out of a large set of candidate features. In the literature, many feature selection methods approach the task as a search problem, where each state in the search space is a possible feature subset...
Malware is a malicious code which is developed to harm a computer or network. The number of malwares is growing so fast and this amount of growth makes the computer security researchers invent new methods to protect computers and networks. There are three main methods used to malware detection: Signature based, Behavioral based and Heuristic ones. Signature based malware detection is the most common...
In supervised learning scenarios, feature selection has been studied widely in the literature. Here, feature selection is considered as an empirical strategy of restricting state space and lessen the complexity of hypothesis. In this work we introduce the environment as a one player game and improve a reinforcement learning method to traverse the state space and learn from experiments. In this way...
Feature subset selection plays a key role in both dimensionality and noise reduction. Moreover, it is often used to enhance accuracy in classification and clustering problems while decreasing their complexity. Inspired by Markov Decision Process, the presented paper considers feature subset selection as a one player game and uses Reinforcement Learning paradigm to select best features. In order to...
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