IDS Malicious Flow Classification

I-Hsien Liu, Cheng-Hsiang Lo, Ta-Che Liu, Jung-Shian Li, Chuan-Gang Liu, Chu-Fen Li
2020 Journal of Robotics, Networking and Artificial Life (JRNAL)  
A B S T R A C T 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
more » ... ata 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. . He teaches communication courses and his research interests include wired and wireless network protocol design, network security, and network management. He is currently involved in funded research projects dealing with optical network, VANET, Cloud security and resource allocation, and IP QoS architectures. He is the deputy director general
doi:10.2991/jrnal.k.200528.006 fatcat:7va2ph5nmvhlnf5q2q6c3tosaa