Intrusion detection method based on network abnormal traffic identification
A technology of intrusion detection and traffic identification, applied in the direction of neural learning methods, biological neural network models, instruments, etc., can solve problems such as network traffic increase, poor performance of intrusion detection systems, adverse effects of intrusion detection system performance, etc., and achieve execution time improvement , wide applicability, and the effect of increasing the false alarm rate
Pending Publication Date: 2021-07-23
STATE GRID HEBEI ELECTRIC POWER CO LTD +1
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[0004] The present invention aims at the problem that the rapid increase in the number of smart device users in recent years has led to a sharp increase in network traffic, the huge data has adversely affected the performance of the intrusion detection system, and the redundant and irrelevant information in the traffic has led to poor performance of the intrusion detection system. Propose an intrusion detection method based on network abnormal traffic identification
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[0031] refer to figure 1 , an intrusion detection method based on abnormal network traffic identification is described in detail, including the following steps:
[0032] 1) Separate the optimal features from the dataset.
[0033] (1.1) CSA is an improved cuckoo search algorithm that mimics the natural behavior of cuckoos, that is, certain cuckoos "obligately parasitize" and lay eggs in the nests of other host birds. The researchers used three rules to define CSA so that it can be implemented as a computer algorithm:
[0034] a) The best nests that lay high-quality eggs will be passed on to the next generation;
[0035] b) There are multiple pre-determined host nests, and the cuckoo egg-laying recognition probability Pa∈(0,1);
[0036] c) when this happens, the egg is either removed or discarded and a new one built;
[0037] For the above rules, CSA is implemented in the following way: A single egg in a nest represents a candidate solution. Thus, a cuckoo can lay only one ...
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In recent years, the number of users of intelligent equipment is rapidly increased, so the network flow is greatly increased, huge data has an adverse effect on the performance of an intrusion detection system, and redundancy and irrelevant information in the flow cause the poor performance of the intrusion detection system; in order to solve the above problems, the invention provides an intrusion detection method based on network abnormal traffic identification. The method comprises the steps: adopting an improved cuckoo search algorithm for feature selection, and screening out the most accurate and effective features from an original data set to serve as a set of optimal features; taking the selected features as input of an evolutionary neural network, in order to overcome parameter limitation of the artificial neural network and avoid falling into a local minimum value, adopting a multivariate universe optimization algorithm to train the artificial neural network, and obtaining the optimal classification effect; finally, inputting a test data set into the trained artificial neural network, predicting and evaluating abnormal traffic detection, and constructing an abnormal traffic intrusion detection model based on feature selection and an evolutionary neural network.
Description
technical field [0001] The invention relates to the field of how to improve the performance of an intrusion detection system, and is a flow abnormal intrusion detection method. Background technique [0002] In recent years, the number of smart device users has increased rapidly, leading to a sharp increase in network traffic, and also brought some security problems, such as various known and unknown network attacks. An intrusion detection system (IDS) is one of the best ways to detect an attack because it involves a software or hardware system that tracks, evaluates and detects internal and external activity. The huge amount of data adversely affects the performance of intrusion detection systems, and the redundant and irrelevant information found in its traffic is also the reason for the poor performance of intrusion detection systems. Therefore, how to improve the performance of the intrusion detection system has become an urgent problem to be solved. [0003] Traffic an...
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Login to View More IPC IPC(8): H04L29/06G06N3/00G06N3/04G06N3/08
CPCG06N3/006G06N3/04G06N3/08H04L63/1425
Inventor 李启蒙高丽芳连阳阳杨会峰孙辰军王静郭少勇王少影
Owner STATE GRID HEBEI ELECTRIC POWER CO LTD