Detection of Advanced Persistent Threat Using Machine-Learning Correlation Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00101837" target="_blank" >RIV/00216224:14330/18:00101837 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0167739X18307532#" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167739X18307532#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.future.2018.06.055" target="_blank" >10.1016/j.future.2018.06.055</a>

Result language

angličtina

Original language name

Detection of Advanced Persistent Threat Using Machine-Learning Correlation Analysis

Original language description

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented system is able to predict APT in its early steps with a prediction accuracy of 84.8%.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

<a href="/en/project/OFMASUN201301" target="_blank" >OFMASUN201301: CIRC - Mobile dedicated devices to fulfilling ability to respond to cyber incidents</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Future Generation Computer Systems

ISSN

0167-739X

e-ISSN

1872-7115

Volume of the periodical

89

Issue of the periodical within the volume

Dec

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

349-359

UT code for WoS article

000444360500028

EID of the result in the Scopus database

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