Juan Carlos Vallejo

TL;DR: A reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks.

TL;DR: A distributed intrusion detection system organized as a reputation system where the reputation of each node is assigned by self-organizing maps (SOM) trained for detecting intrusions and the implementation of SOM algorithm is proposed using the energy-efficient SORU (Stream Oriented Reconfigurable Unit) co-processor developed by the research group.

TL;DR: This work proposes a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity, based on detecting outliers using clustering, in this case self-organizing maps.

TL;DR: This work presents a framework for intrusion detection in WMNs that is orthogonal to the network protocols, based on a reputation system, that allows to isolate ill-behaved nodes by rating their reputation as low, and distributed agents based on unsupervised learning algorithms (self-organizing maps), that are able to detect deviations from the normal behavior.

TL;DR: This work proposes a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol that provides a mechanism to effectively detect and confine common attacks, and allows bad reputation feedback to the network.