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
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
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Synergetic Computers & Cognition
Synergetic Computers & Cognition
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
Statistical techniques for detecting traffic anomalies through packet header data
IEEE/ACM Transactions on Networking (TON)
Internet traffic behavior profiling for network security monitoring
IEEE/ACM Transactions on Networking (TON)
A large-scale hidden semi-Markov model for anomaly detection on user browsing behaviors
IEEE/ACM Transactions on Networking (TON)
Profiling and identification of P2P traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking
Spatio-temporal network anomaly detection by assessing deviations of empirical measures
IEEE/ACM Transactions on Networking (TON)
Accurate anomaly detection through parallelism
IEEE Network: The Magazine of Global Internetworking - Special issue title on recent developments in network intrusion detection
Understanding data center traffic characteristics
ACM SIGCOMM Computer Communication Review
Towards trusted cloud computing
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Network prefix-level traffic profiling: Characterizing, modeling, and evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking
Profiling-By-Association: a resilient traffic profiling solution for the internet backbone
Proceedings of the 6th International COnference
DTRAB: combating against attacks on encrypted protocols through traffic-feature analysis
IEEE/ACM Transactions on Networking (TON)
Anomaly Detection in Network Traffic Based on Statistical Inference and \alpha-Stable Modeling
IEEE Transactions on Dependable and Secure Computing
Parametric methods for anomaly detection in aggregate traffic
IEEE/ACM Transactions on Networking (TON)
Training a neural-network based intrusion detector to recognize novel attacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Traffic modeling for telecommunications networks
IEEE Communications Magazine
Impact of Packet Sampling on Portscan Detection
IEEE Journal on Selected Areas in Communications
Integrated access control and intrusion detection for Web servers
IEEE Transactions on Parallel and Distributed Systems
Profiling-as-a-Service in Multi-tenant Cloud Computing Environments
ICDCSW '12 Proceedings of the 2012 32nd International Conference on Distributed Computing Systems Workshops
Advanced probabilistic approach for network intrusion forecasting and detection
Expert Systems with Applications: An International Journal
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Cloud computing represents a new paradigm where computing resources are offered as services in the world via communication Internet. As many new types of attacks are arising at a high frequency, the cloud computing services are exposed to an increasing amount of security threats. To reduce security risks, two approaches of the network traffic anomaly detection in cloud communications have been presented, which analyze dynamic characteristics of the network traffic based on the synergetic neural networks and the catastrophe theory. In the former approach, a synergetic dynamic equation with a group of the order parameters is used to describe the complex behaviors of the network traffic system in cloud communications. When this equation is evolved, only the order parameter determined by the primary factors can converge to 1. Then, the anomaly can be detected. In the latter approach, a catastrophe potential function is introduced to describe the catastrophe dynamic process of the network traffic in cloud communications. When anomalies occur, the state of the network traffic will deviate from the normal one. To assess the deviation, an index named as catastrophe distance is defined. The network traffic anomaly can be detected by the value of this index. We evaluate the performance of these two approaches using the standard Defense Advanced Research Projects Agency data sets. Experimental results show that our approaches can effectively detect the network traffic anomaly and achieve the high detection probability and the low false alarms rate.