| Title | Identifying NAT Devices to Detect Shadow IT: A Machine Learning Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Nassar, Reem, Elhajj, Imad, Kayssi, Ayman, Salam, Samer |
| Conference Name | 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA) |
| Keywords | composability, Device Identification, feature extraction, Internet, IP networks, machine learning, Metrics, Network Address Translation, Network security, Object recognition, pubcrawl, resilience, Resiliency, Timing, Traffic analysis, Windows Operating System Security, Wireless communication |
| Abstract | Network Address Translation (NAT) is an address remapping technique placed at the borders of stub domains. It is present in almost all routers and CPEs. Most NAT devices implement Port Address Translation (PAT), which allows the mapping of multiple private IP addresses to one public IP address. Based on port number information, PAT matches the incoming traffic to the corresponding "hidden" client. In an enterprise context, and with the proliferation of unauthorized wired and wireless NAT routers, NAT can be used for re-distributing an Intranet or Internet connection or for deploying hidden devices that are not visible to the enterprise IT or under its oversight, thus causing a problem known as shadow IT. Thus, it is important to detect NAT devices in an intranet to prevent this particular problem. Previous methods in identifying NAT behavior were based on features extracted from traffic traces per flow. In this paper, we propose a method to identify NAT devices using a machine learning approach from aggregated flow features. The approach uses multiple statistical features in addition to source and destination IPs and port numbers, extracted from passively collected traffic data. We also use aggregated features extracted within multiple window sizes and feed them to a machine learning classifier to study the effect of timing on NAT detection. Our approach works completely passively and achieves an accuracy of 96.9% when all features are utilized. |
| DOI | 10.1109/AICCSA53542.2021.9686910 |
| Citation Key | nassar_identifying_2021 |
Identifying NAT Devices to Detect Shadow IT: A Machine Learning Approach