Demonstrating cognitive packet network resilience to worm attacks

The need for network stability and reliability has led to the growth of autonomic networks 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) 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