Malware classification using deep learning

Cheng Hsiang Lo, Ta Che Liu, I-Hsien Liu, Jung Shian Li, Chuan Gang Liu, Chu Fen Li

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

We’ll display two different kinds of experiments, which are NIDS-based and Dynamic-based analysis shows how artificial intelligence (AI) helps us detecting and classify malware. On the NIDS-based intrusion detection, we use CICIDS2017 as a research dataset, embedding high dimensional features and find out redundant features in the raw dataset by Random Forest algorithm, reach 99.93% accuracy and 0.3% of the false alert rate. We extract the function calls in malware data by the method proposed in this paper to generate text data. The algorithm n-gram and TF-IDF are used to process text data, converts them into numeric features, and by another feature selection methods, we reduce the training time, achieve 87.08% accuracy, and save 87.97% training time in Dynamic-based analysis.

Original languageEnglish
Pages (from-to)126-129
Number of pages4
JournalProceedings of International Conference on Artificial Life and Robotics
Volume2020
DOIs
Publication statusPublished - 2020
Event25th International Conference on Artificial Life and Robotics, ICAROB 2020 - Beppu, Oita, Japan
Duration: 2020 Jan 132020 Jan 16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Information Systems
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation

Fingerprint

Dive into the research topics of 'Malware classification using deep learning'. Together they form a unique fingerprint.

Cite this