Advanced Temporal-Difference Learning for Intrusion Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86094771" target="_blank" >RIV/61989100:27240/15:86094771 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ifacol.2015.07.005" target="_blank" >http://dx.doi.org/10.1016/j.ifacol.2015.07.005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2015.07.005" target="_blank" >10.1016/j.ifacol.2015.07.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced Temporal-Difference Learning for Intrusion Detection

Popis výsledku v původním jazyce

Nowadays intrusion detection for cyber security is the dynamically researched area. The main purpose of the intrusion detection is to distinguish normal usage of analyzed system from different forms of misuses and abnormal behaviors. The big amount of intrusion detection approaches such as soft computing and machine learning algorithms was made. In spite of visible progress, there are still many opportunities to improve state-of-the-art techniques. This paper presents the new intrusion detection technique that is based on temporal-difference learning for Markov decision processes. Actually, this method is the advanced form of the existing temporal-difference based approach named Temporal-Difference based Sequence Anomaly Detection (or TD_SAD). Due to this fact, our approach is called TD_SAD2. It is shown that the proposed approach can achieve at least comparable accuracy for intrusion detection by benchmarking with existing leading approaches.

Název v anglickém jazyce

Advanced Temporal-Difference Learning for Intrusion Detection

Popis výsledku anglicky

Nowadays intrusion detection for cyber security is the dynamically researched area. The main purpose of the intrusion detection is to distinguish normal usage of analyzed system from different forms of misuses and abnormal behaviors. The big amount of intrusion detection approaches such as soft computing and machine learning algorithms was made. In spite of visible progress, there are still many opportunities to improve state-of-the-art techniques. This paper presents the new intrusion detection technique that is based on temporal-difference learning for Markov decision processes. Actually, this method is the advanced form of the existing temporal-difference based approach named Temporal-Difference based Sequence Anomaly Detection (or TD_SAD). Due to this fact, our approach is called TD_SAD2. It is shown that the proposed approach can achieve at least comparable accuracy for intrusion detection by benchmarking with existing leading approaches.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

IFAC Proceedings Volumes (IFAC-PapersOnline). Volume 48

ISBN

—

ISSN

1474-6670

e-ISSN

—

Počet stran výsledku

5

Strana od-do

43-48

Název nakladatele

IFAC

Místo vydání

London

Místo konání akce

Krakov

Datum konání akce

13. 5. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—