Author : Matthew Schofield
Affiliation : Rowan University
Country : USA
Category : Computer Science & Information Technology
Volume, Issue, Month, Year : 11, 01, January, 2021
Abstract :
Malicious software is constantly being developed and improved, so detection and classification of malicious applications is an ever-evolving problem. Since traditional malware detection techniques fail to detect new or unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the Windows system API (Application Program Interface) calls. This research uses a database of 5385 instances of API call streams labeled with eight types of malware of the source malicious application. We use a 1-Dimensional CNN by mapping API call streams as categorical and term frequency-inverse document frequency (TF-IDF) vectors respectively. We achieved accuracy scores of 98.17% using TF-IDF vector and 95.40% via categorical vector. The proposed 1-D CNN outperformed other traditional classification techniques with overall accuracy score of 91.0%.
Keyword : Convolutional Neural Network, Malware Classification, Windows API Calls, Term Frequency Inverse Document Frequency Vectors
For More Details : https://aircconline.com/csit/papers/vol11/csit110106.pdf
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