Biblio

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.

The quantitative security effectiveness analysis is a difficult problem for the research of network address randomization techniques. In this paper, a system model and an attack model are proposed based on general attacks' attack processes and network address randomization's technical principle. Based on the models, the network address randomization's security effectiveness is quantitatively analyzed from the perspective of the attacker's attack time and attack cost in both static network address and network address randomization cases. The results of the analysis show that the security effectiveness of network address randomization is determined by the randomization frequency, the randomization space, the states of hosts in the target network, and the capabilities of the attacker.

This paper will suggest a robust method for a network layer Moving Target Defense (MTD) using symmetric packet scheduling rules. The MTD is implemented and tested on a Supervisory Control and Data Acquisition (SCADA) network testbed. This method is shown to be efficient while providing security benefits to the issues faced by the static nature of SCADA networks. The proposed method is an automated tool that may provide defense in depth when be used in conjunction with other MTDs and traditional security devices.