The original publication is available at www.springerlink.comInternational audienceThis paper addresses the fingerprinting of devices that speak a common, yet unknown to the fingerprinting engine, protocol. We consider a behavioral approach, where the fingerprinting of an unknown protocol is based on detecting and exploiting differences in the observed behavior from two or more devices. Our approach assumes zero knowledge about the syntax and state machine underlying the protocol. The main contribution of this paper consists in a two phased method. The first phase identifies the different message types using an unsupervised support vector clustering algorithm. The second phase is leveraging recent advances in tree support kernel in order to learn and differentiate different implementations of that protocol. The key idea is to represent behavior in terms of trees and learn the distinctive subtrees that are specific to one particular device. Our solution is passive and does not assume active and stimulus triggered behavior templates. We instantiate our solution to the particular case of a VoIP specific protocol (SIP) and validate it using extensive data sets collected on a large size VoIP testbed

Abstract-VoIP networks are in a major deployment phase and are becoming widely spread out due to their extended functionality and cost efficiency. Meanwhile, as VoIP traffic is transported over the Internet, it is the target of a range of attacks that can jeopardize its proper functionality. In this paper we describe our work in a VoIP specific security assessment framework. Such an assessment is automated with integrated discovery actions, data management and security attacks allowing to perform VoIP specific penetration tests. These tests are important because they permit to search and detect existing vulnerabilities or misconfigured devices and services. Our main contributions consist in an elaborated network information model capable to be used in VoIP assessment, an extensible assessment architecture and its implementation, as well as in a comprehensive framework for defining and composing VoIP specific attacks.

Security assessment tasks and intrusion detection systems do rely on automated fingerprinting of devices and services. Most current fingerprinting approaches use a signature matching scheme, where a set of signatures are compared with traffic issued by an unknown entity. The entity is identified by finding the closest match with the stored signatures. These fingerprinting signatures are found mostly manually, requiring a laborious activity and needing advanced domain specific expertise. In this paper we describe a novel approach to automate this process and build flexible and efficient fingerprinting systems able to identify the source entity of messages in the network. We follow a passive approach without need to interact with the tested device. Application level traffic is captured passively and inherent structural features are used for the classification process. We describe and assess a new technique for the automated extraction of protocol fingerprints based on arborescent features extracted from the underlying grammar. We have successfully applied our technique to the Session Initiation Protocol (SIP) used in Voice over IP signalling.