Random neural networks with multiple classes of signals
Neural Computation
Technical opinion: Information system security management in the new millennium
Communications of the ACM
Design and performance of cognitive packet networks
Performance Evaluation
Networks with Cognitive Packets
MASCOTS '00 Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Cognitive Packet Networks: QoS and Performance
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
An Autonomic Approach to Denial of Service Defence
WOWMOM '05 Proceedings of the First International IEEE WoWMoM Workshop on Autonomic Communications and Computing (ACC'05) - Volume 02
A self-aware approach to denial of service defence
Computer Networks: The International Journal of Computer and Telecommunications Networking
Steps toward self-aware networks
Communications of the ACM - Barbara Liskov: ACM's A.M. Turing Award Winner
The Computer Journal
Protection Against Denial of Service Attacks
The Computer Journal
Demonstrating cognitive packet network resilience to worm attacks
Proceedings of the 17th ACM conference on Computer and communications security
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
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Route selection in cognitive packet networks CPNs occurs continuously for active flows and is driven by the users' choice of a quality of service QoS goal. Because routing occurs concurrently to packet forwarding, CPN flows are able to better deal with unexpected variations in network status, while still achieving the desired QoS. Random neural networks RNNs play a key role in CPN routing and are responsible to the next-hop decision making of CPN packets. By using reinforcement learning, RNNs' weights are continuously updated based on expected QoS goals and information that is collected by packets as they travel on the network experiencing the current network conditions. CPN's QoS performance had been extensively investigated for a variety of operating conditions. Its dynamic and self-adaptive properties make them suitable for withstanding availability attacks, such as those caused by worm propagation and denial-of-service attacks. However, security weaknesses related to confidentiality and integrity attacks have not been previously examined. Here, we look at related network security threats and propose mechanisms that could enhance the resilience of CPN to confidentiality, integrity and availability attacks.