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Voice over IP (VoIP) has become a major paradigm for
providing flexible telecommunication
services and reducing operational costs. The
large-scale deployment of VoIP has been leveraged
by the high-speed broadband access to the Internet
and the standardization of dedicated protocols.
However, VoIP faces multiple security issues
including vulnerabilities inherited from the
IP layer as well as specific ones. Our objective is
to design, implement and validate new models
and architectures for performing proactive defense,
monitoring and intrusion detection in VoIP
networks. Our work combines two domains: network
security and artificial intelligence. We reinforce
existent security mechanisms by working on three
axes: a machine learning approach for VoIP
signaling traffic monitoring, a VoIP specific
honeypot and a security event correlation model for
intrusion detection.
providing flexible telecommunication
services and reducing operational costs. The
large-scale deployment of VoIP has been leveraged
by the high-speed broadband access to the Internet
and the standardization of dedicated protocols.
However, VoIP faces multiple security issues
including vulnerabilities inherited from the
IP layer as well as specific ones. Our objective is
to design, implement and validate new models
and architectures for performing proactive defense,
monitoring and intrusion detection in VoIP
networks. Our work combines two domains: network
security and artificial intelligence. We reinforce
existent security mechanisms by working on three
axes: a machine learning approach for VoIP
signaling traffic monitoring, a VoIP specific
honeypot and a security event correlation model for
intrusion detection.