C4.5: programs for machine learning
C4.5: programs for machine learning
Protocol Analysis in Intrusion Detection Using Decision Tree
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Online adaptive decision trees
Neural Computation
Adaptive building of decision trees by reinforcement learning
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
Automatic Adaptation and Analysis of SIP Headers Using Decision Trees
Principles, Systems and Applications of IP Telecommunications. Services and Security for Next Generation Networks
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Analysis of techniques for protection against spam over internet telephony
EUNICE'07 Proceedings of the 13th open European summer school and IFIP TC6.6 conference on Dependable and adaptable networks and services
Computer Networks: The International Journal of Computer and Telecommunications Networking
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
SPRT for SPIT: using the sequential probability ratio test for spam in VoIP prevention
AIMS'12 Proceedings of the 6th IFIP WG 6.6 international autonomous infrastructure, management, and security conference on Dependable Networks and Services
Outbound SPIT filter with optimal performance guarantees
Computer Networks: The International Journal of Computer and Telecommunications Networking
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With the spread of new and innovative Internet services such as SIP-based communications, the challenge of protecting and defending these critical applications has been raised. In particular, SIP firewalls attempt to filter the signaling unwanted activities and attacks based on the knowledge of the SIP protocol. Optimizing the SIP firewall configuration at real-time by selecting the best filtering rules is problematic because it depends on both natures of the legal traffic and the unwanted activities. More precisely, we do not know exactly how the unwanted activities are reflected in the SIP messages and in what they differ from the legal ones. In this paper, we address the case of Spam over Internet Telephony (SPIT) mitigation. We propose an adaptive solution based on extracting signatures from learnt decision trees. Our simulations show that quickly learning the optimal configuration for a SIP firewall leads to reduce at lowest the unsolicited calls as reported by the users under protection. Our results promote the application of machine learning algorithms for supporting network and service resilience against such new challenges.