Intrusion detection system and intrusion detection method for energy Internet

Abstract

The invention discloses an intrusion detection system and an intrusion detection method for an energy Internet. A feature selection module is used for forming a reduced data set by removing features which have no contribution or low contribution to an intrusion detection model from an original data set; a data preprocessing module is used for carrying out further data preprocessing on the reduceddata set and processing the reduced data set into a data set which can be received and processed by a deep learning model; and an intrusion detection module is used for training, optimizing and testing the deep learning model by using the data set obtained by the data preprocessing module, and then detecting intrusion flow by using the deep learning model. According to the intrusion detection system for the energy Internet, through testing on a real data set of the energy Internet, the numerical result shows that the intrusion detection system disclosed by the invention is very effective in detection of various network threats in the energy Internet, and is superior to most of existing intrusion detection system schemes.

Description

technical field [0001] The present invention relates to the technical field of energy Internet, in particular to an energy Internet-oriented intrusion detection system and a method thereof. Background technique [0002] The Energy Internet is an emerging field of the Internet of Things, such as figure 1 As shown, it is defined as a networked system composed of various smart energy infrastructures, including control centers, distributed renewable energy systems, decentralized energy storage, and energy consumption (such as industrial, commercial, residential, etc.). The goal of the Energy Internet is to coordinate existing distributed energy systems, thereby optimizing the energy efficiency of generation, transmission, and consumption of all these energy systems. [0003] Energy Internet has many development prospects and is facing more and more network security challenges. Because the Energy Internet integrates a series of heterogeneous and vulnerable communication network...

AI Technical Summary

Problems solved by technology

With the development and advancement of intrusion detection systems for decades, traditional network intrusion detection methods use known types of attack samples to train intrusion detection models in an offline manner. Although they have a high detection rate for known attack types, but Unable to identify new types of attacks on the network, such an intrusion detection system has the disadvantages of slow system establishment and high cost of model update. Facing the ever-expanding network and endless attacks, it lacks adaptability and scalability, making it difficult to Detect emerging types of attacks on the network, for example:

[0006] The analysis of intrusion detection system based on prior knowledge mainly focuses on the analysis of intrusion behavior and system status. A big problem is that it cannot detect potential attack operations that exploit system vulnerabilities or conform to protocol specifications;

[0007] Statistical-based intrusion detection usually adopts analytical and statistically relevant methods to analyze intrusion detection, although they do not require any prior knowledge about the attack, but they need time to find the accurate statistical distribution;

[0008] Intrusion detection systems based on traditional machine learning cannot handle high-dimensional data. When the amount of data is large, there are problems such as slow convergence and high complexity.

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