Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F19%3A00108866" target="_blank" >RIV/00216224:14610/19:00108866 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8470942/" target="_blank" >https://ieeexplore.ieee.org/document/8470942/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/COMST.2018.2871866" target="_blank" >10.1109/COMST.2018.2871866</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Popis výsledku v původním jazyce

This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation.

Název v anglickém jazyce

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Popis výsledku anglicky

This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

IEEE Communications Surveys & Tutorials

ISSN

1553-877X

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

640-660

Kód UT WoS článku

000459730200024

EID výsledku v databázi Scopus

2-s2.0-85054236310