Intrusion detection is a hot topic in network security. This paper proposes
an intrusion detection method based on improved artificial bee colony
algorithm with elite-guided search equations (IABC elite) and Backprogation
(BP) neural net works. The IABC elite algorithm is based on the depth first
search framework and the elite-guided search equations, which enhance the
exploitation ability of artificial bee colony algorithm and accelerate the
convergence. The IABC elite algorithm is used to optimize the initial weight
and threshold value of the BP neural networks, avoiding the BP neural
networks falling into a local optimum during the training process and
improving the training speed. In this paper, the BP neural networks optimized
by IABC elite algorithm is applied to intrusion detection. The simulation on
the NSL-KDD dataset shows that the intrusion detection system based on the
IABC elite algorithm and the BP neural networks has good classification and
high intrusion detection ability.
Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market
