A FEATURE REPRESENTATION METHOD BASED ON HETEROGENEOUS INFORMATION NETWORK FOR ANDROID MALWARE DETECTION

DOI: 10.18173/2354-1059.2020-0047

  • Thai Thi Thanh Van
  • Nguyen Van Phac
  • Truong Quoc Quan
  • Le Van Hung
Từ khóa: malware, android, heterogeneous, classification, machine learning.

Tóm tắt

The rapid growth in number, sophistication, and diversity of Android malware poses a great difficulty in extracting and analyzing features and behaviors. The traditional approach, which using only API calls and permissions to extract features, has no longer yielded meaningful results. In this research, we propose a method that utilizes both information about API function calls and the relationships between API functions. First, we represent the relationship between API functions using a heterogeneous information network (HIN). Then, we use the concept of meta-path to extract information features from HIN. Finally, a machine learning algorithm is used to build classification models. Experimental results on a practical dataset of Android applications show that the proposed method gives more reliable results than the existing ones. 

điểm /   đánh giá
Phát hành ngày
2021-05-10
In ra
Tập. 10 Số. 65 (2020)
Chuyên mục
BAI BÁO