DDoS-attack detection using artificial neural networks in Matlab

2019

Closed access Metadata (CSL-JSON)
DOI 10.1117/12.2536478
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

There are research results of artificial neural networks usage for solving a hardly formalized objective – detection of a DDoS attacks on the computer network information resource in this article. An analysis of existing methods, technologies and tools for detecting DDoS attacks and protecting from them is carried out. Several feed forward neural networks are simulated. The architecture of the neural network which provides high-precision detection is presented.