An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network

Abstract

The rapid development and popularization of the network have brought many problems to network security. Intrusion detection technology is often used as an effective security technology to protect the network. The deep belief network (DBN), as a classic model of deep learning, has good classification performance and is often used in the field of intrusion detection. However, the network structure of DBN is generally set through practical experience. For the optimization problem of the DBN-based intrusion detection classification model (DBN-IDS), this paper proposes a new joint optimization algorithm to optimize the DBN's network structure. First, we design a particle swarm optimization (PSO) based on the adaptive inertia weight and learning factor. Second, we use the fish swarm behavior of cluster, foraging, and other behaviors to optimize the PSO to find the initial optimization solution. Then, based on the initial optimization solution, we use the genetic operators with self-adjusting crossover probability and mutation probability to optimize the PSO to search the global optimization solution. Finally, the global optimization solution constructed by the above-mentioned joint optimization algorithm is used as the network structure of the intrusion detection classification model. The experimental results show that compared with other DBN-IDS optimization algorithms, our algorithm shortens the average detection time by at least 24.69% on the premise of increasing the average training time by 6.9%; compared with the tested classification algorithms, our DBN-IDS improves the average classification accuracy by at least 1.3% and up to 14.80% in the five-category classification, which is proved to be an efficient DBN-IDS optimization method.

Keywords

• Intrusion detection
• deep belief network
• particle swarm optimization
• artificial fish swarm algorithm
• genetic algorithm