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A method and system for discovering abnormal behavior based on big data machine learning

Active Publication Date: 2020-01-10
北京明朝万达科技股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] (1) The data source is single, and only the logs are analyzed and processed
[0011] (2) Unable to determine abnormal behavior and users in real time
[0012] (3) All rely on manual statistical analysis, which is costly and prone to wrong judgments on behavior

Method used

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  • A method and system for discovering abnormal behavior based on big data machine learning
  • A method and system for discovering abnormal behavior based on big data machine learning
  • A method and system for discovering abnormal behavior based on big data machine learning

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Embodiment Construction

[0054] Glossary:

[0055]Hadoop: distributed system infrastructure, the core design is HDFS and MapReduce. HDFS provides storage for massive data, and MapReduce provides calculation for massive data.

[0056] Spark: A general-purpose parallel computing framework similar to Hadoop MapReduce. Unlike MapReduce, the intermediate output results of jobs can be stored in memory, so that the calculation speed is faster and it is better applicable to algorithms that require iteration, such as data mining and machine learning.

[0057] Lambda Architecture: A real-time big data processing framework proposed by Nathan Marz, which integrates offline computing and real-time computing, integrates a series of architectural principles such as immutability, read-write separation, and complexity isolation, and can integrate various big data components such as Hadoop and Spark.

[0058] Sqoop: big data component, used for data transfer between big data platform and traditional relational databas...

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Abstract

The invention discloses a method and system for discovering abnormal behaviors based on big data machine learning. The method includes: preprocessing the original security log data; extracting feature data from the preprocessed results; aggregating the feature data class, determine the abnormal behavior library and normal behavior library; obtain the new behavior sample data in the new safety day data, and compare it with the normal behavior library and abnormal behavior library to determine whether it is normal behavior or abnormal behavior. New behavior sample data update described normal behavior library or abnormal behavior library; Repeat previous step, when described normal behavior library and abnormal behavior library have enough normal behavior and abnormal behavior sample data, use described normal behavior library and abnormal behavior The sample data in the behavior database is used to train the random forest model, and the abnormal behavior is judged by using the random forest model obtained through training. Through the solution of the invention, the problem of too few samples containing labels in the initial stage is solved, the accuracy of judgment is improved, and the occurrence of misjudgment is effectively prevented.

Description

technical field [0001] The invention relates to the field of data security, in particular to a method and system for discovering abnormal behaviors based on big data machine learning. Background technique [0002] Traditional network security and data security technologies, such as various software and hardware firewalls, generally adopt a "fence-style" protection strategy, which artificially adds many restrictions to the network and application systems. Any data access action needs to be filtered by all preset rules. It not only affects the user experience of the system, but also increases the operating burden of the system. In addition, in existing security software, generating a built-in rule generally requires multiple stages such as vulnerability discovery, attack simulation, packet analysis, feature extraction, and rule generation. As attack methods are constantly updated, such a rule generation process also needs to be repeated, which consumes a lot of labor costs. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/55G06F21/57H04L29/06H04L12/26G06K9/62
CPCH04L43/16H04L63/1416H04L63/1425G06F21/552G06F21/577G06F18/23213G06F18/24147
Inventor 李学进王志海魏力喻波何晋昊蒲鹏飞
Owner 北京明朝万达科技股份有限公司
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