Artificial Immune Systems (AIS) are algorithms with the origin from principles and functioning of the innate immune system. These algorithms exploit the characteristics of biological immune systems like learning and memory as a way to formulate problem. To prevent and minimize the security risks, there is severe need to integrate the Artificially Immune Systems for the security of networks. In the recent years various AIS algorithms with fabulous functionality have been proposed. In order to give the comprehensive review of all the AIS algorithms meant for risk detection and give direction for further research, a review of the AIS algorithms is discussed in depth in this paper. Qualitatively, based on primary algorithms, we show that all these algorithms once done with classification for the first time do not check its validity of being correctly classified. So we found that deterministic DCA is best among all the existing techniques of AIS based risk detection and proposed Enhanced Dendric Cell Algorithm (EDCA) to make it more efficient.