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 language | English |
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Pages (from-to) | 126-129 |
Number of pages | 4 |
Journal | Proceedings of International Conference on Artificial Life and Robotics |
Volume | 2020 |
DOIs | |
Publication status | Published - 2020 |
Event | 25th International Conference on Artificial Life and Robotics, ICAROB 2020 - Beppu, Oita, Japan Duration: 2020 Jan 13 → 2020 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