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See detailAdvanced Interest Flooding Attacks in Named-Data Networking
Signorello, Salvatore UL; Marchal, Samuel; François, Jérôme et al

Scientific Conference (2017, October 30)

The Named-Data Networking (NDN) has emerged as a clean-slate Internet proposal on the wave of Information-Centric Networking. Although the NDN’s data-plane seems to offer many advantages, e.g., native ... [more ▼]

The Named-Data Networking (NDN) has emerged as a clean-slate Internet proposal on the wave of Information-Centric Networking. Although the NDN’s data-plane seems to offer many advantages, e.g., native support for multicast communications and flow balance, it also makes the network infrastructure vulnerable to a specific DDoS attack, the Interest Flooding Attack (IFA). In IFAs, a botnet issuing unsatisfiable content requests can be set up effortlessly to exhaust routers’ resources and cause a severe performance drop to legitimate users. So far several countermeasures have addressed this security threat, however, their efficacy was proved by means of simplistic assumptions on the attack model. Therefore, we propose a more complete attack model and design an advanced IFA. We show the efficiency of our novel attack scheme by extensively assessing some of the state-of-the-art countermeasures. Further, we release the software to perform this attack as open source tool to help design future more robust defense mechanisms. [less ▲]

Detailed reference viewed: 149 (12 UL)
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See detailEfficient Learning of Communication Profiles from IP Flow Records
Hammerschmidt, Christian UL; Marchal, Samuel; Pellegrino, Gaetano et al

Poster (2016, November)

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information ... [more ▼]

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information often comes with using simpler models on aggregated data (e.g. IP flow records) due to time and space constraints. A step towards utilizing IP flow records more effectively are stream learning techniques. We propose a method to collect a limited yet relevant amount of data in order to learn a class of complex models, finite state machines, in real-time. These machines are used as communication profiles to fingerprint, identify or classify hosts and services and offer high detection rates while requiring less training data and thus being faster to compute than simple models. [less ▲]

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See detailBehavioral Clustering of Non-Stationary IP Flow Record Data
Hammerschmidt, Christian UL; Marchal, Samuel; State, Radu UL et al

Poster (2016, October)

Detailed reference viewed: 122 (5 UL)