DDoS attacks bring serious economic and technical damage to networks and enterprises. Timely detection and mitigation are therefore of great importance. However, when flow monitoring systems are used for intrusion detection, as it is often the case in campus, enterprise and backbone networks, timely data analysis is constrained by the architecture of NetFlow and IPFIX. In their current architecture, the analysis is performed after certain timeouts, which generally delays the intrusion detection for several minutes. This paper presents a functional extension for both NetFlow and IPFIX flow exporters, to allow for timely intrusion detection and mitigation of large flooding attacks. The contribution of this paper is threefold. First, we integrate a lightweight intrusion detection module into a flow exporter, which moves detection closer to the traffic observation point. Second, our approach mitigates attacks in near real-time by instructing firewalls to filter malicious traffic. Third, we filter flow data of malicious traffic to prevent flow collectors from overload. We validate our approach by means of a prototype that has been deployed on a backbone link of the Czech national research and education network CESNET.

The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e. g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases.

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