A Denial of Service Detector based on Maximum Likelihood Detection and the Random Neural Network

Öke, Gülay; Loukas, George

doi:10.1093/comjnl/bxm066

Cited by 48 publications

(13 citation statements)

References 33 publications

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“…This generic defence was further improved by using prioritization and rate-limiting instead of simple dropping [12,19]. The same authors have also introduced a DoS detection mechanism that makes use of on-line statistics collected by the CPN protocol's monitoring system and fused them with a RNN [18]. More analytically, the scheme uses input features to capture both the instantaneous behaviour and the longer-term statistical properties of the traffic.…”

Section: The Cognitive Packet Network (Cpn)mentioning

confidence: 99%

Demonstrating cognitive packet network resilience to worm attacks

Sakellari

Gelenbe

2010

Proceedings of the 17th ACM Conference on Computer and Communications Security

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The need for network stability and reliability has led to the growth of autonomic networks [2] that can provide more stable and more reliable communications via on-line measurement, learning and adaptation. A promising architecture is the Cognitive Packet Network (CPN) [5] that rapidly adapts to varying network conditions and user requirements using QoS driven reinforcement learning algorithms that drive the routing control. Contrary to conventional mechanisms, the users rather than the nodes, control the routing by specifying their desired QoS requirements (QoS Goals), such as Minimum Delay, Maximum Bandwidth, Minimum Cost, etc., and the network then routes each user's traffic individually based on their specific needs and on a "glocal" view. In CPN the user has the ability to explore the network for its own needs, and evaluate its own impact on the network as a whole and vice-versa, and then take appropriate decisions. CPN routing has been evaluated extensively under normal operating conditions and has proven to be very adaptive to network changes such as congestion. Here we show how CPN can respond and survive to catastrophic node failures caused by the spread of network worms. This survival is based on two complementary approaches that are run concurrently: one the one hand, each user attempts to concurrently and adaptively avoid paths which are infected, and secondly patching algorithms are continuously run to repair the network. Experiments show that this approach assures the stability of network communications throughout the course of an attack.

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Section: The Cognitive Packet Network (Cpn)mentioning

confidence: 99%

Demonstrating cognitive packet network resilience to worm attacks

Sakellari

Gelenbe

2010

Proceedings of the 17th ACM Conference on Computer and Communications Security

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“…(See [12] for further details of equations (1)-(9)) Where, i Λ denotes the rates of exogenous excitatory and i λ denotes inhibitory signal inputs into neuron i [4] [8] [12] [13].…”

Section: Overview Of New Rnn Modelmentioning

confidence: 99%

Voice Quality in VoIP Networks Based on Random Neural Networks

Larijani

Radhakrishnan

2010

2010 Ninth International Conference on Networks

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The growth of Internet has led to the development of many new applications and technologies. Voice over Internet Protocol (VoIP) is one of the fastest growing applications. Calculating the quality of calls has been a complex task. The ITU E-Model gives a framework to measure quality of VoIP calls but the MOS element is a subjective measure. In this paper, we discuss a novel method using Random Neural Network (RNN) to accurately predict the perceived quality of voice and more importantly to perform this on real-time traffic to overcome the drawbacks of available methods. The novelty of this model is that RNN model provides a non-intrusive method to accurately predict and monitor perceived voice quality for both listening and conversational voice. This method has learning capabilities and this makes it possible for it to adapt to any network changes without human interference. Our novel model uses three input variables (neurons) delay, jitter, and packet loss and the codec used was G711.a. Results show a good degree of accuracy in calculating Mean Option Score (MOS), compared to Perceptual Evaluation of Speech Quality (PESQ) algorithm. WAN emulation software WANem was used to generate different samples for testing and training the RNN.

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“…The Cognitive Packet Network: A Survey 7 resilience against DoS attacks, the same authors have also introduced a DoS detection mechanism that makes use of on-line statistics collected by the CPN protocol's monitoring system and fused them with a RNN [54]. More analytically, the scheme uses input features to capture both the instantaneous behaviour and the longer-term statistical properties of the traffic.…”

Section: The Computer Journal 2009mentioning

confidence: 99%

The Cognitive Packet Network: A Survey

Sakellari

2009

The Computer Journal

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Current and future multimedia networks require connections under specific quality of service (QoS) constraints which can no longer be provided by the best-effort Internet. Therefore, 'smarter'networks have been proposed in order to cover this need. The cognitive packet network (CPN) is a routing protocol that provides QoS-driven routing and performs self-improvement in a distributed manner, by learning from the experience of special packets, which gather on-line QoS measurements and discover new routes. The CPN was first introduced in 1999 and has been used in several applications since then. Here we provide a comprehensive survey of its variations, applications and experimental performance evaluations.

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A Denial of Service Detector based on Maximum Likelihood Detection and the Random Neural Network

Cited by 48 publications

References 33 publications

Demonstrating cognitive packet network resilience to worm attacks

Demonstrating cognitive packet network resilience to worm attacks

Voice Quality in VoIP Networks Based on Random Neural Networks

The Cognitive Packet Network: A Survey

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