Hierarchical network attack identification and unknown attack detection method based on deep learning

A layered network and unknown attack technology, applied in the field of computer networks, can solve the problems of inability to classify network attacks, low detection accuracy, and high false alarm rate, and achieve the effect of improving detection accuracy

Active Publication Date: 2020-01-14
UNIV OF SCI & TECH OF CHINA
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Anomaly detection only needs normal traffic as input data, and the data requirements are smal...

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  • Hierarchical network attack identification and unknown attack detection method based on deep learning
  • Hierarchical network attack identification and unknown attack detection method based on deep learning
  • Hierarchical network attack identification and unknown attack detection method based on deep learning

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Embodiment Construction

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0019] Existing rule-based network security detection methods face problems such as unknown attack threats, high labor costs, high professional requirements, and long analysis cycles; at the same time, anomaly-based security detection methods face low detection accuracy and cannot detect attacks classification and other issues. Therefore, the present invention proposes a network attack classification and unknown attack detection method based on dee...

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Abstract

The invention discloses a hierarchical network attack identification and unknown attack detection method based on deep learning, which comprises the following steps of: learning behavior modes of normal flow and abnormal flow by using an auto-encoder so as to judge whether the flow to be detected is the normal flow or the abnormal flow by using the learned auto-encoder; using a deep neural networkas a discrimination model to discriminate whether the attack type of the to-be-detected flow is a certain known attack type or an unknown type; and fusing the results of the auto-encoder and the discrimination model to complete network attack classification and unknown attack detection. According to the method, normal flow can be identified, known abnormal flow can be classified, novel unknown network attacks can be detected, and the detection accuracy is improved by combining the advantages of the generation model and the discrimination model.

Description

Technical field [0001] The invention relates to the field of computer network technology, and in particular to a method for layered network attack identification and unknown attack detection based on deep learning. Background technique [0002] With the rapid development of network technology, the Internet has covered all aspects of social life and has had a profound impact on social life. Although the widespread application of the Internet has promoted the rapid development of social economy, it has also become an important medium for hackers and criminals to spread malware and carry out network attacks. In recent years, new network attack methods targeting network protocols and application vulnerabilities have emerged one after another. Network security detection has become one of the important tasks in network supervision and operation and maintenance. In particular, the issue of unknown zero-day network attack detection has received widespread attention. [0003] Tradit...

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Application Information

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IPC IPC(8): H04L29/06G06N20/00G06N3/04G06N3/08G06K9/62
CPCH04L63/1416G06N20/00G06N3/08G06N3/045G06F18/24
Inventor 姜晓枫陈翔杨坚谭小彬张勇东
Owner UNIV OF SCI & TECH OF CHINA
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