Static Feature Selection for IoT Malware Detection


  • Nguyen Ngoc Toan
  • Luong The Dung
  • Dang Quang Thang



feature selection, opcode, IoT malware, malware detection, machine learning

Tóm tắt

AbstractOur world has recently witnessed the
explosive growth of IoT networks as one of the
pillars of the 4th industrial revolution. Malware on
IoT devices also grows accordingly in number and
sophisticated techniques. Therefore, it is necessary
to come up with more efficient approaches to IoT
malware detection with machine learning models
that can be used in solutions using limited
resources. In this paper, we study and evaluate the
efficiency of using a weight of term frequency–
inverse document frequency model in feature
selection method combined with an effective
machine learning model in IoT malware detection
based on opcode sequence features. We performed
experiments on a MIPS ELF dataset that included
4,511 malicious samples with main four classes and
4,393 benign programs. Experiment results show
that our proposed method has very good
performance on the above dataset with detection
and classification accuracy which are 99.8% and
95.8% respectively while the models only use 20
opcodes that have the highest weight values.
Tóm tắt Cuộc cách mạng công nghiệp lần thứ 4
với sự phát triển của các thiết bị IoT đã và đang ảnh
hưởng sâu rộng đến các lĩnh vực trong đời sống xã
hội. Các mã độc trên thiết bị IoT ngày càng gia tăng
về số lượng và sử dụng các kỹ thuật lẩn tránh tinh
vi. Điều này đòi hỏi cần có các phương pháp tiếp cận
hiệu quả hơn trong phát hiện mã độc trên thiết bị
IoT với các mô hình học máy hiệu quả, có khả năng
ứng dụng trong các giải pháp đảm bảo an toàn
thông tin có tài nguyên hạn chế. Trong bài báo này,
chúng tôi nghiên cứu và đánh giá hiệu quả của việc
xác định trọng số trong tìm kiếm truy xuất thông tin
trong phương pháp trích chọn đặc trưng kết hợp
mô hình học máy hiệu quả cho việc phát hiện mã
độc IoT dựa trên đặc trưng chuỗi opcode. Chúng tôi
đã tiến hành thử nghiệm với một tập dữ liệu MIPS
ELF gồm 4.511 mẫu độc hại với 4 loại chính và
4.393 chương trình lành tính. Các kết quả thực
nghiệm đã chứng minh rằng phương pháp của bài
báo đề xuất cho kết quả tốt đối với tập dữ liệu nêu
trên, tỉ lệ phát hiện và phân 4 loại mã độc cao nhất
tương ứng là 99.8% và 95.8% khi chỉ cần sử dụng
20 opcode có giá trị trọng số cao nhất.


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How to Cite

Toan, N. N. ., Dung, L. T., & Thang, D. Q. (2022). Static Feature Selection for IoT Malware Detection. Journal of Science and Technology on Information Security, 1(15), 74-84.