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Malware Classification Possibility based on Sequence Information
Tae-Uk Yun, Chan-Soo Park, Tae-Gyu Hwang, Sung Kwon Kim
http://doi.org/10.5626/JOK.2017.44.11.1125
LSTM(Long Short-term Memory) is a kind of RNN(Recurrent Neural Network) in which a next-state is updated by remembering the previous states. The information of calling a sequence in a malware can be defined as system call function that is called at each time. In this paper, we use calling sequences of system calls in malware codes as input for malware classification to utilize the feature remembering previous states via LSTM. We run an experiment to show that our method can classify malware and measure accuracy by changing the length of system call sequences.
Bias-Based Predictor to Improve the Recommendation Performance of the Rating Frequency Weight-based Baseline Predictor
Collaborative Filtering is limited because of the cost that is required to perform the recommendation (such as the time complexity and space complexity). The RFWBP (Rating Frequency Weight-based Baseline Predictor) that approximates the precision of the existing methods is one of the efficiency methods to reduce the cost. But, the following issues need to be considered regarding the RFWBP: 1) It does not reduce the error because the RFWBP does not learn for the recommendation, and 2) it recommends all of the items because there is no condition for an appropriate recommendation list when only the RFWBP is used for the achievement of efficiency. In this paper, the BBP (Bias-Based Predictor) is proposed to solve these problems. The BBP reduces the error range, and it determines some of the cases to make an appropriate recommendation list, thereby forging a recommendation list for each case.
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