Method for balancing low load and packet loss rate in DIDS based on reinforcement learning

A technology of reinforcement learning and packet loss rate, applied in the field of network security, can solve problems such as rising packet loss rate

Active Publication Date: 2020-09-22
陕西森印多西网络科技有限责任公司
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This task scheduling algorithm that emphasiz

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  • Method for balancing low load and packet loss rate in DIDS based on reinforcement learning
  • Method for balancing low load and packet loss rate in DIDS based on reinforcement learning
  • Method for balancing low load and packet loss rate in DIDS based on reinforcement learning

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[0059] The present invention will be described in detail below in conjunction with specific embodiments.

[0060] The present invention is a method for balancing low load and packet loss rate in DIDS based on reinforcement learning, which is specifically implemented according to the following steps:

[0061] Step 1. After the distributed intrusion detection system is started, before starting to work, first perform performance evaluation of each detection engine, collect its detection time (dt) and memory usage (mu) information on the test traffic, and add As a performance indicator of the detection engine. After testing all detection engines, they are divided into different levels according to their performance d, d=1,...,D, d values ​​differing within 10% can be classified as the same level;

[0062] Step 2. After starting work, when a data packet arrives and needs to be detected, the scheduler first obtains the data packet length and evaluates the load generated by the data packet...

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Abstract

The invention discloses a method for balancing a low load and a packet loss rate in DIDS based on reinforcement learning. The method comprises the following steps: calculating the immediate detectionrate, calculating the theoretical packet loss rats, calculating an average data packet arrival number; calculating the average waiting number of the data packets; calculating the packet latency probability, calculating the number of tasks allocated to the detection engine, calculating the probability that the detection engine is allocated to the tasks, calculating the working efficiency of the detection engine, calculating the overall working efficiency of the distributed intrusion detection system, and adjusting the balance of two contradictory indexes of low load and packet loss rate by detecting the probability that the detection engine is allocated to the detection tasks. The problem that in a task scheduling algorithm of a distributed intrusion detection system with a low load as a target disclosed in the prior art, the packet loss rate is possibly increased due to the fact that the low load is purely emphasized is solved.

Description

Technical field [0001] The invention belongs to the technical field of network security, and relates to a method for balancing low load and packet loss rate in DIDS based on reinforcement learning. Background technique [0002] Edge computing is the process of migrating computing tasks from network center nodes to network edge nodes. Due to the limited resources of terminal equipment in the edge computing environment, the distributed intrusion detection system needs to operate under a low load state. The prior art discloses a task scheduling algorithm for a distributed intrusion detection system that aims at low load. This task scheduling algorithm that emphasizes low load has the problem that it may increase the packet loss rate. Summary of the invention [0003] The purpose of the present invention is to provide a method for balancing low load and packet loss rate in DIDS (DIDS, Distributed Intrusion Detection System) based on reinforcement learning, which solves the distribut...

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Application Information

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IPC IPC(8): G06F9/50G06K9/62
CPCG06F9/5083G06F18/295Y02D30/50
Inventor 赵旭江晋赵子江
Owner 陕西森印多西网络科技有限责任公司
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