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Intrusion Detection based upon learning methods is an attractive approach in research community. These researches have two critical concerns: secure information gathering and accurate detection method. Here we used system calls together with their arguments as a suitable pattern for describing behavior of each process. In security applications, these patterns must be collected safely, so we proposed...
Intrusion detection systems play increasingly essential roles in modern society. With all various approaches used to Anomaly Detection, we investigate Artificial Immune Systems (AIS) to model anomaly detection. One new field of AIS is called Danger Theory. Since traditional Anomaly Detection Methods produce a large number of false alarms, AIS methods based on Danger Theory are suitable options for...
Artificial Immune Systems have long been used in the field of computer security and especially in Intrusion Detection systems. The first generation of AISs which is inspired only by the adaptive immune system has some weak points and suffers from excessive amount of false alarms. The next generation of AISs is inspired by “Danger Theory” and tries to mimic the innate immune system along with the adaptive...
Nowadays anomaly detection based on Artificial Immune system (AIS) is highly regarded among the research community and Clonal selection algorithm is one of the most interesting fields of it. In this paper we model self-region by a number of spheres with fix radius. We have assumed anomaly detectors as spherical shape with random central point and variable radius out of this region. Detection rate...
In this research a hybrid feature selection technique based on genetic and simulated annealing algorithms is proposed. this approach is evaluated by using Bayesian classifier on a dataset of hand-printed Farsi characters which includes 100 samples for each 33 hand-printed characters. The acquired results have been improved by correction of Simulated Annealing through considering two minimum and maximum...
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