Biblio

Software Defined Networking (SDN) is a paradigm shift that changes the working principles of IP networks by separating the control logic from routers and switches, and logically centralizing it within a controller. In this architecture the control plane (controller) communicates with the data plane (switches) through a control channel using a standards-compliant protocol, that is, OpenFlow. While having a centralized controller creates an opportunity to monitor and program the entire network, as a side effect, it causes the control plane to become a single point of failure. Denial of service (DoS) attacks or even heavy control traffic conditions can easily become real threats to the proper functioning of the controller, which indirectly detriments the entire network. In this paper, we propose a solution to reduce the control traffic generated primarily during table-miss events. We utilize the buffer\_id feature of the OpenFlow protocol, which has been designed to identify individually buffered packets within a switch, reusing it to identify flows buffered as a series of packets during table-miss, which happens when there is no related rule in the switch flow tables that matches the received packet. Thus, we allow the OpenFlow switch to send only the first packet of a flow to the controller for a table-miss while buffering the rest of the packets in the switch memory until the controller responds or time out occurs. The test results show that OpenFlow traffic is significantly reduced when the proposed method is used.

Detecting botnets and advanced persistent threats is a major challenge for network administrators. An important component of such malware is the command and control channel, which enables the malware to respond to controller commands. The detection of malware command and control channels could help prevent further malicious activity by cyber criminals using the malware. Detection of malware in network traffic is traditionally carried out by identifying specific patterns in packet payloads. Now bot writers encrypt the command and control payloads, making pattern recognition a less effective form of detection. This paper focuses instead on an effective anomaly based detection technique for bot and advanced persistent threats using a data mining approach combined with applied classification algorithms. After additional tuning, the final test on an unseen dataset, false positive rates of 0% with malware detection rates of 100% were achieved on two examined malware threats, with promising results on a number of other threats.