Network intrusion detection system based on machine learning

Abstract

The invention relates to a network intrusion detection system based on machine learning. The system comprises a network intrusion data acquisition module, an intrusion data classification processing module, a network training initialization data selection module, a generalized neural network module, a network training data selection module and a result output module; results are obtained through intrusion data acquisition, classification, selection, training and prediction for result output and alarming. The system has the beneficial effects that the data is from network intrusion data, and the algorithm aims to effectively cluster the intrusion data. Compared with a traditional algorithm, the intrusion detection accuracy and effectiveness are improved, a computer network security protection system is perfected, the security coefficient of the network is greatly improved, and the labor cost is saved.

Description

technical field [0001] The invention relates to the field of network intrusion detection, and is a network intrusion detection system based on machine learning. Background technique [0002] In recent years, Internet technology has developed rapidly, and with the emergence of new technologies, the threat of cyber attacks has also increased. Internet network intrusion is an attempt to destroy the integrity, confidentiality or availability of computer and network system resources. Intrusion detection is to collect and analyze information through several key points in a computer network or computer system, and find out whether there is a violation of security policies in the network or system. [0003] Conventional intrusion object detection methods include host-based intrusion detection systems, network-based intrusion detection systems, and hybrid intrusion detection systems. Conventional methods have great limitations and cannot accurately distinguish misoperations and intr...

AI Technical Summary

Problems solved by technology

[0003] Conventional intrusion object detection methods include host-based intrusion detection systems, network-based intrusion detection systems, and hybrid intrusion detection systems. Conventional methods have great limitations and cannot accurately distinguish misoperations and intrusion behaviors, resulting in low efficiency. Adds a huge workload to network maintainers, and the lack of self-security may lead to the system being exploited by hackers

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