IDS malicious flow classification

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

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


We will display two different kinds of experiments, which are Network-based Intrusion Detection System (NIDS)-based and dynamic-based analysis shows how artificial intelligence helps us detecting and classify malware. On the NID, 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 Term Frequency-Inverse Document Frequency (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)103-106
Number of pages4
JournalJournal of Robotics, Networking and Artificial Life
Issue number2
Publication statusPublished - 2020 Sept

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications


Dive into the research topics of 'IDS malicious flow classification'. Together they form a unique fingerprint.

Cite this