Since the events of 9/11 2001 in the US the world public awareness to possible terrorist attacks on water supply systems has increased dramatically, causing the security of drinking water distribution systems to become a major concern around the globe. Among the different threats, a deliberate chemical or biological contaminant injection is the most difficult to address, both as a consequence of the uncertainty surrounding the type of the injected contaminant and its consequences, as well as the uncertainty of location and time of the injection. In principle, a pollutant can be injected at any water distribution system connection (node) using a pump or a mobile pressurized tank. Although backflow preventers provide an obstacle to such actions, they do not exist at all connections, and at some might not be functional. This paper describes recent effort modeling of Avi Ostfeld's research team on water distribution systems event detection. The basic event detection framework is entitled AEDA (Aquatic Event Detection Algorithm) which utilizes Artificial Neural Networks (ANNs) for studying the interactions between multivariate water quality parameters and detecting possible outliers. Other layers on top of AEDA explore tradeoffs among contamination event parameters and improving its performance capabilities. Those and AEDA are reviewed in this paper.