IDS Malicious Flow Classification

Journal of Robotics, Networking and Artificial Life (JRNAL)

关键词: NIDS;    dynamic analysis;    deep learning;   

DOI  :  10.2991/jrnal.k.200528.006

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown