A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
The nature of statistical learning theory
The nature of statistical learning theory
A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
A nonlinear, recurrence-based approach to traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Characterizing and Classifying Card-Sharing Traffic through Wavelet Analysis
INCOS '11 Proceedings of the 2011 Third International Conference on Intelligent Networking and Collaborative Systems
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
A blocker-proof conditional access system
IEEE Transactions on Consumer Electronics
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In the last years, the interest in methods and techniques for circumventing the security of the available digital video broadcasting systems is continuously increasing. Digital TV providers are struggling to restrict access to their video contents only to authorized users, by deploying more and more sophisticated conditional access systems. At the state-of-the-art, the most significant menace is the card-sharing activity which exploits a known weakness allowing an authorized subscriber to provide access to digital contents to a potentially large group of unauthorized ones connected over a communication network. This is usually realized by using ad hoc customized devices. Detecting the presence of these illegal systems on a network, by recognizing their related traffic is an issue of primary importance. Unfortunately, to avoid the identification of such traffic, payload obfuscation strategies based on encryption are often used, hindering packet inspection techniques. This paper presents a strategy for the detection of card-sharing traffic, empowered by machine-learning-driven traffic classification techniques and based on the natural capability of wavelet analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas have been used for the proof-of-concept implementation of an SVM-based binary classification scheme that relies only on time regularities of the traffic and not on the packet contents and hence is immune to payload obfuscation techniques.