| Title | Research on Network Malicious Code Detection and Provenance Tracking in Future Network |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Liu, Lan, Lin, Jun, Wang, Qiang, Xu, Xiaoping |
| Conference Name | 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) |
| Keywords | Analytical models, Behavior features, classification algorithm, Classification algorithms, composability, Computational modeling, computer network security, cyber physical systems, detection method, feature extraction, feature selection, feature selection methods, host node, invasive software, machine learning algorithms, malicious code detection, Malware, malware detection system, matching classification method, Mobile communication, mobile network, network coding, network controller, network malicious code detection, Network security, optimal feature subset, pattern classification, Predictive Metrics, propagation model, pubcrawl, Resiliency, SDN network, security, software defined networking, switch node |
| Abstract | with the development of SDN, ICN and 5G networks, the research of future network becomes a hot topic. Based on the design idea of SDN network, this paper analyzes the propagation model and detection method of malicious code in future network. We select characteristics of SDN and analyze the features use different feature selection methods and sort the features. After comparison the influence of running time by different classification algorithm of different feature selection, we analyze the choice of reduction dimension m, and find out the different types of malicious code corresponding to the optimal feature subset and matching classification method, designed for malware detection system. We analyze the node migration rate of malware in mobile network and its effect on the outbreak of the time. In this way, it can provide reference for the management strategy of the switch node or the host node by future network controller. |
| DOI | 10.1109/QRS-C.2018.00055 |
| Citation Key | liu_research_2018 |
Research on Network Malicious Code Detection and Provenance Tracking in Future Network