A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Bro: a system for detecting network intruders in real-time
Computer Networks: The International Journal of Computer and Telecommunications Networking
The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Model-based estimation of buffer overflow probabilities from measurements
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Information-Theoretic Measures for Anomaly Detection
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
A taxonomy of DDoS attack and DDoS defense mechanisms
ACM SIGCOMM Computer Communication Review
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Characterization of network-wide anomalies in traffic flows
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
An architecture for generating semantics-aware signatures
SSYM'05 Proceedings of the 14th conference on USENIX Security Symposium - Volume 14
On the stationarity of TCP bulk data transfers
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
On the estimation of buffer overflow probabilities from measurements
IEEE Transactions on Information Theory
Statistical anomaly detection with sensor networks
ACM Transactions on Sensor Networks (TOSN)
Global modeling of backbone network traffic
INFOCOM'10 Proceedings of the 29th conference on Information communications
The Journal of Supercomputing
Information Sciences: an International Journal
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We introduce an Internet traffic anomaly detection mechanism based on large deviations results for empirical measures. Using past traffic traces we characterize network traffic during various time-of-day intervals, assuming that it is anomaly-free. We present two different approaches to characterize traffic: (i) a model-free approach based on the method of types and Sanov's theorem, and (ii) a model-based approach modeling traffic using a Markov modulated process. Using these characterizations as a reference we continuously monitor traffic and employ large deviations and decision theory results to "compare" the empirical measure of the monitored traffic with the corresponding reference characterization, thus, identifying traffic anomalies in real-time. Our experimental results show that applying our methodology (even short-lived) anomalies are identified within a small number of observations. Throughout, we compare the two approaches presenting their advantages and disadvantages to identify and classify temporal network anomalies. We also demonstrate how our framework can be used to monitor traffic from multiple network elements in order to identify both spatial and temporal anomalies. We validate our techniques by analyzing real traffic traces with time-stamped anomalies.