Intrusion detection based on neural networks and Artificial Bee Colony algorithm
Quan Qian, Jing Cai, Rui Zhang · 2014
Intrusion detection, as a dynamic security protection technology, is able to defense the internal and external network attacks. Using Artificial Bee Colony algorithm to optimize the parameters of neural network is to avoid the neural network falling into a local optimum, can solve the problem of slow convergence speed of the neural network algorithm. Also Artificial Bee Colony algorithm can deal with the problem of finding the optimal solutions in a very short period of time. In this paper, An Artificial Bee Colony optimized neural network algorithm is applied to intrusion detection. And the experimental results shows that the optimized method has better detection accuracy and efficiency than the single BP neural network.