Demonstrating cognitive packet network resilience to worm attacks
Proceedings of the 17th ACM conference on Computer and communications security
A study on QoS of VoIP networks: a random neural network (RNN) approach
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
An initiative for a classified bibliography on G-networks
Performance Evaluation
DDoS detection and traceback with decision tree and grey relational analysis
International Journal of Ad Hoc and Ubiquitous Computing
Distributed defence against denial of service attacks: a practical view
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
Bibliography on G-networks, negative customers and applications
Mathematical and Computer Modelling: An International Journal
Securing business processes using security risk-oriented patterns
Computer Standards & Interfaces
Strengthening the security of cognitive packet networks
International Journal of Advanced Intelligence Paradigms
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Due to the simplicity of the concept and the availability of attack tools, launching a DoS attack is relatively easy, while defending a network resource against it is disproportionately difficult. The first step of a protection scheme against DoS must be the detection of its existence, ideally before the destructive traffic build-up. In this paper we propose a DoS detection approach which uses the maximum likelihood criterion with the random neural network (RNN). Our method is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using likelihood averaging and different architectures of RNNs. We present and compare seven variations of it and evaluate our experimental results obtained in a large networking testbed.