Research on Intrusion Detection Based on Markov Chain Monte Carlo Method

He rapid development of the Internet has brought about exponential growth in the amount of data in the network. With the growth of data volume in the network and the increasing dependence of humans on the Internet, network intrusion has become increasingly frequent, posing a serious threat to network security. In view of this, network intrusion detection has gradually become an important research topic in the field of network security. Due to the increasing maturity of deep learning technology, a large amount of intrusion detection research is currently based on deep learning technology. Deep learning technology can certainly improve the accuracy of intrusion detection, but it requires a large amount of data and computing resources and can easily cause overfitting when the data volume is insufficient, thereby reducing the recognition rate of unknown intrusions. This paper proposes an intrusion detection algorithm based on Markov chain Monte Carlo method, which can complete network intrusion detection tasks with low resource consumption. Compared with the intrusion detection algorithm based on basic Extreme learning machine, this method has better detection performance.