Biblio

The essential part of an SDN-based network are flow rules that enable network elements to steer and control the traffic and deploy policy enforcement points with a fine granularity at any entry-point in a network. Such applications, implemented with the usage of OpenFlow rules, are already integral components of widely used SDN controllers such as Floodlight or OpenDayLight. The implementation details of network policies are reflected in the composition of flow rules and leakage of such information provides adversaries with a significant attack advantage such as bypassing Access Control Lists (ACL), reconstructing the resource distribution of Load Balancers or revealing of Moving Target Defense techniques. In this demo [4, 5] we present our open-source scanner SDNMap and demonstrate the findings discussed in the paper "Adversarial Network Forensics in Software Defined Networking" [6]. On two real world examples, Floodlight's Access Control Lists (ACL) and Floodlight's Load Balancer (LBaaS), we show that severe security issues arise with the ability to reconstruct the details of OpenFlow rules on the data-plane.

Software Defined Networking (SDN), and its popular implementation OpenFlow, represent the foundation for the design and implementation of modern networks. The essential part of an SDN-based network are flow rules that enable network elements to steer and control the traffic and deploy policy enforcement points with a fine granularity at any entry-point in a network. Such applications, implemented with the usage of OpenFlow rules, are already integral components of widely used SDN controllers such as Floodlight or OpenDayLight. The implementation details of network policies are reflected in the composition of flow rules and leakage of such information provides adversaries with a significant attack advantage such as bypassing Access Control Lists (ACL), reconstructing the resource distribution of Load Balancers or revealing of Moving Target Defense techniques. In this paper we introduce a new attack vector on SDN by showing how the detailed composition of flow rules can be reconstructed by network users without any prior knowledge of the SDN controller or its architecture. To our best knowledge, in SDN, such reconnaissance techniques have not been considered so far. We introduce SDNMap, an open-source scanner that is able to accurately reconstruct the detailed composition of flow rules by performing active probing and listening to the network traffic. We demonstrate in a number of real-world SDN applications that this ability provides adversaries with a significant attack advantage and discuss ways to prevent the introduced reconnaissance techniques. Our SDNMap scanner is able to reconstruct flow rules between network endpoints with an accuracy of over 96%.

Advanced targeted cyber attacks often rely on reconnaissance missions to gather information about potential targets and their location in a networked environment to identify vulnerabilities which can be exploited for further attack maneuvers. Advanced network scanning techniques are often used for this purpose and are automatically executed by malware infected hosts. In this paper we formally define network deception to defend reconnaissance and develop RDS (Reconnaissance Deception System), which is based on SDN (Software Defined Networking), to achieve deception by simulating virtual network topologies. Our system thwarts network reconnaissance by delaying the scanning techniques of adversaries and invalidating their collected information, while minimizing the performance impact on benign network traffic. We introduce approaches to defend malicious network discovery and reconnaissance in computer networks, which are required for targeted cyber attacks such as Advanced Persistent Threats (APT). We show, that our system is able to invalidate an attackers information, delay the process of finding vulnerable hosts and identify the source of adversarial reconnaissance within a network, while only causing a minuscule performance overhead of 0.2 milliseconds per packet flow on average.