The remarkable ability to separate and identify self and non-self in a given problem space,
makes negative selection a fascinating concept of artificial immune system. Therefore …

With the increase in available data from computer systems and their security threats, interest
in anomaly detection has increased as well in recent years. The need to diagnose faults and …

Intrusion detection systems are crucial in fighting against various network attacks. By
monitoring the network behavior in real time, possible attack attempts can be detected and …

Artificial immune system (AIS) as a good prototype for developing Machine Learning (ML) is
a promising candidate to design Intrusion Detection System (IDS). Two of its prevalent …

The negative selection algorithm (NSA) is an important algorithm for generating immune
detectors in artificial immune systems. However, the original NSA randomly generates …

Disease phenotypes are generally caused by the failure of gene modules which often have
similar biological roles. Through the study of biological networks, it is possible to identify the …

Community detection is one of the most challenging and interesting problems in many
research areas. Being able to detect highly linked communities in a network can lead to …

Compared with the traditional negative selection algorithms produce detectors randomly in
whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly …

This paper proposes a self-adaptive mobile web service (MWS) discovery approach based
on the modified negative selection algorithm (M-NSA) to improve the effectiveness and …

Inspired by biological immune systems, the field of artificial immune system (AIS),
particularly the negative selection algorithm (NSA), has been proved effective in solving …