| Reference : Machine Learning Techniques for Passive Network Inventory |
| Scientific journals : Article | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/5630 | |||
| Machine Learning Techniques for Passive Network Inventory | |
| English | |
François, Jérôme  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Abdelnur, Humberto J. [INRIA Nancy Grand Est France] | |
| State, Radu [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC)] | |
| Festor, Olivier [INRIA Nancy Grand Est France] | |
| 2010 | |
| IEEE Transactions on Network and Service Management | |
| IEEE | |
| 7 | |
| 4 | |
| 244 - 257 | |
| Yes (verified by ORBilu) | |
| 1932-4537 | |
| [en] Being able to fingerprint devices and services, i.e., remotely identify running code, is a powerful service for both security assessment and inventory management. This paper describes two novel fingerprinting techniques supported by isomorphic based distances which are adapted for measuring the similarity between two syntactic trees. The first method leverages the support vector machines paradigm and requires a learning stage. The second method operates in an unsupervised manner thanks to a new classification algorithm derived from the ROCK and QROCK algorithms. It provides an efficient and accurate classification. We highlight the use of such classification techniques for identifying the remote running applications. The approaches are validated through extensive experimentations on SIP (Session Initiation Protocol) for evaluating the impact of the different parameters and identifying the best configuration before applying the techniques to network traces collected by a real operator. | |
| http://hdl.handle.net/10993/5630 | |
| 10.1109/TNSM.2010.1012.0352 |
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