Malfinder: Accelerated Malware Classification System through Filtering on Manycore System

2015 ◽  
Keyword(s):  
Classification System ◽  
Malware Classification

scholarly journals Ensemble Malware Classification System Using Deep Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 721 ◽  
Author(s):  
Barath Narayanan Narayanan ◽  
Venkata Salini Priyamvada Davuluru
Keyword(s):  
Neural Networks ◽  
Classification System ◽  
Short Term Memory ◽  
Ensemble Classification ◽  
Machine Language ◽  
Support Vector ◽  
Automated Classification ◽  
Malware Classification ◽  
Lstm Network ◽  
Computational Resources

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft’s Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using Long Short-Term Memory (LSTM) networks after converting them into sequences. In addition, features are extracted from these architectures (CNNs and LSTM) and are classified using a support vector machine or logistic regression. An accuracy of 97.2% is achieved using LSTM network for distinguishing assembly files, 99.4% using CNN architecture for classifying compiled files and an overall accuracy of 99.8% using the proposed ensemble approach thereby setting a new benchmark. An independent and automated classification system for assembly and/or compiled files provides the luxury to anti-malware industry experts to choose the type of system depending on their available computational resources.

Malware Classification System to Support Decision Making of App Installation on Android OS

2015 ◽  
Vol 42 (12) ◽  
pp. 1611-1622
Author(s):  
Hong Ryeol Ryu ◽  
Yun Jang ◽  
Taekyoung Kwon
Keyword(s):  
Decision Making ◽  
Classification System ◽  
Malware Classification ◽  
Android Os ◽  
Support Decision Making

International Research and Variation in Views of Ethnicity

2012 ◽  
Vol 2 (2) ◽  
pp. 66-72
Author(s):  
Ella Inglebret ◽  
Amy Skinder-Meredith ◽  
Shana Bailey ◽  
Carla Jones ◽  
Ashley France
Keyword(s):  
United States ◽  
Classification System ◽  
International Research ◽  
The United States ◽  
Research Articles ◽  
Journal Articles ◽  
Research Participants ◽  
The Past ◽  
International Scale ◽  
Publication Patterns

The authors in this article first identify the extent to which research articles published in three American Speech-Language-Hearing Association (ASHA) journals included participants, age birth to 18 years, from international backgrounds (i.e., residence outside of the United States), and go on to describe associated publication patterns over the past 12 years. These patterns then provide a context for examining variation in the conceptualization of ethnicity on an international scale. Further, the authors examine terminology and categories used by 11 countries where research participants resided. Each country uses a unique classification system. Thus, it can be expected that descriptions of the ethnic characteristics of international participants involved in research published in ASHA journal articles will widely vary.

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