CONVOLUTIONAL NEURAL NETWORK FOR MALWARE CLASSIFICATION BASED ON API CALL SEQUENCE

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