Federated Learning for Privacy-Preserving Intrusion Detection in Software-Defined Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10255160" target="_blank" >RIV/61989100:27740/24:10255160 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10516460" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10516460</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2024.3395997" target="_blank" >10.1109/ACCESS.2024.3395997</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Federated Learning for Privacy-Preserving Intrusion Detection in Software-Defined Networks

Popis výsledku v původním jazyce

Software-defined networking (SDN) is an innovative network technology. It changed the world of computer networking by providing solutions to many challenges. SDN provides programmability, easy and centralized network management, dynamic configuration, and improved security. Although SDN offers remarkable benefits but it provides centralized network management which is prone to attacks. So, intrusion detection systems (IDS) are essential to detect and prevent security attacks in SDN. Traditional IDS follow a centralized machine learning approach which causes vulnerabilities in IDS. Old-style IDS lack data privacy preservation, and solution for training data unavailability due to privacy. Federated learning (FL) is a distributed machine learning approach which provides a collaborative training approach without data sharing. In FL, training is performed on multiple nodes creating a global model without sharing the data. To address challenges and the limitations of traditional IDS, we proposed a FL based multi class classification IDS for SDN. FL delivers an efficient and scalable solution to address challenges of traditional IDS. The proposed model enhances security of SDN by not requiring the centralization of data. To test the impact and efficiency of proposed model, we used a latest and realistic cybersecurity dataset. We also compared the proposed model with state of art existing multi class classification studies. The results and their comparison with existing studies highlight the potential of proposed model to enhance network security while providing a privacy-preserving learning environment for intrusion detection.

Název v anglickém jazyce

Federated Learning for Privacy-Preserving Intrusion Detection in Software-Defined Networks

Popis výsledku anglicky

Software-defined networking (SDN) is an innovative network technology. It changed the world of computer networking by providing solutions to many challenges. SDN provides programmability, easy and centralized network management, dynamic configuration, and improved security. Although SDN offers remarkable benefits but it provides centralized network management which is prone to attacks. So, intrusion detection systems (IDS) are essential to detect and prevent security attacks in SDN. Traditional IDS follow a centralized machine learning approach which causes vulnerabilities in IDS. Old-style IDS lack data privacy preservation, and solution for training data unavailability due to privacy. Federated learning (FL) is a distributed machine learning approach which provides a collaborative training approach without data sharing. In FL, training is performed on multiple nodes creating a global model without sharing the data. To address challenges and the limitations of traditional IDS, we proposed a FL based multi class classification IDS for SDN. FL delivers an efficient and scalable solution to address challenges of traditional IDS. The proposed model enhances security of SDN by not requiring the centralization of data. To test the impact and efficiency of proposed model, we used a latest and realistic cybersecurity dataset. We also compared the proposed model with state of art existing multi class classification studies. The results and their comparison with existing studies highlight the potential of proposed model to enhance network security while providing a privacy-preserving learning environment for intrusion detection.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

12

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

69551-69567

Kód UT WoS článku

001230490200001

EID výsledku v databázi Scopus

2-s2.0-85192196829