Abstract：Aiming at the the low quality of feature clustering and excessive redundant alarms in IDS, an IDS alerts clustering algorithm
based on novel chaotic particle swarm optimization is proposed. It combines the characteristics of chaotic PSO algorithms,
adaptive inertia weight coefficient, and non-linear dynamic learning factor, so as to make particles move between the state of chaos
and stable. It guarantees the particle motion inertia, and approaches the optimal value. It also can overcome the problems of premature
convergence and "inert" reaction of PSO algorithm, and help the center of cluster to find the global optimal solution. The experiment
results show that the improvement of particle swarm parameters improves the quality of feature clustering in IDS alarm, and
has higher detection rate and lower false detection rate.
2016, Vol. 1 
