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If a computer node is infected by a virus, worm or a backdoor, then this is a security risk for the complete network structure where the node is associated. Existing Network Intrusion Detection Systems (NIDS) provide a certain amount of support for the identification of such infected nodes but suffer from the need of plenty of communication and computational power. In this article, we present a novel approach called AGNOSCO to support the identification of infected nodes through the usage of artificial ant colonies. It is shown that AGNOSCO overcomes the communication and computational power problem while identifying infected nodes properly.
The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been insp
The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customi
Ant species such as Temnothorax albipennis select a new nest site in a distributed fashion that, if modeled correctly, can serve as useful information for site selection algorithms for robotic swarms and other applications. Studying and replicating t
When testing for infections, the standard method is to test each subject individually. If testing methodology is such that samples from multiple subjects can be efficiently combined and tested at once, yielding a positive results if any one subject i
The paper presents an exponential pheromone deposition approach to improve the performance of classical Ant System algorithm which employs uniform deposition rule. A simplified analysis using differential equations is carried out to study the stabili