Abstract
With the continuous development of computer networks, the security of the network has become increasingly prominent. A major threat to network security is the intrusion of information systems through the network. Intrusion detection of the traditional intrusion detection and alarm technology is not sufficient. Based on neural network technology, this paper studies the intrusion detection and alarm correlation technology. Based on the research on the working principle and workflow of the existing intrusion detection system, a new neural network-based intrusion detection and alarm method is proposed. A neural network-based intrusion detection and alarm system is designed and implemented. Through the experiment of the system prototype, the results show that the intrusion detection and alarm system based on the neural network has a higher detection rate and a lower false alarm rate for intrusion behaviors such as denial of service attack and has higher detection ability for unknown attack behaviors.
Highlights
With the rapid development of network technology and the advent of the network era, network security has been becoming more and more important [1]
3.1 System design The intrusion detection alarm model based on the artificial neural network mainly includes the following modules: network packet interception module, protocol parsing module, message parsing module, artificial neural network detection, and response module, as shown in Fig. 3: The network packet interception module is responsible for intercepting data packets from the network, including data frames, IP packets, and packets transmitted on the network
For the attacks that do not appear in the training set, there is a high detection rate, which indicates that the artificial neural network-based intrusion detection can detect the unknown attack and the variant of the known attack well
Summary
With the rapid development of network technology and the advent of the network era, network security has been becoming more and more important [1]. One of the most popular network security models is the PPDR model, which focuses on network security policies. It includes four parts: policy, protection, detection, and response [4]. We use the advantages of artificial neural network self-learning, self-adaptation, nonlinearity, and robustness to study a simple and effective intrusion detection and alarm system based on artificial neural network. This is the purpose of studying the system, that is, designing and implementing intrusion detection and alarm model, which can detect known attacks, but has good detection capabilities for unknown attacks
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