Enhancing Security Monitoring with AI-Enabled Log Collection and NLP Modules on a Unified Open Source Platform

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148783" target="_blank" >RIV/00216305:26220/23:PU148783 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/eeict.2023.217" target="_blank" >10.13164/eeict.2023.217</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Security Monitoring with AI-Enabled Log Collection and NLP Modules on a Unified Open Source Platform

Popis výsledku v původním jazyce

The number of computer attacks continues to increase daily, posing significant challenges to modern security administrators to provide security in their organizations. With the rise of sophisticated cyber threats, it is becoming increasingly difficult to detect and prevent attacks using traditional security measures. As a result, security monitoring solutions such as Security Information and Event Management (SIEM) have become a critical component of modern security infrastructures. However, these solutions still face limitations, and administrators are constantly seeking ways to enhance their capabilities to effectively protect their cyber units. This paper explores how advanced deep learning techniques can help boost security monitoring capabilities by utilizing them throughout all stages of log processing. The presented platform has the potential to fundamentally transform and bring about a significant change in the field of security monitoring with advanced AI capabilities. The study includes a detailed comparison of modern log collection platforms, with the goal of determining the most effective approach. The key benefits of the proposed solution are its scalability and multipurpose nature. The platform integrates an open source solution and allows the organization to connect any event log sources or the entire SIEM solution, normalize and filter data, and use this data to train and deploy different AI models to perform different security monitoring tasks more efficiently.

Název v anglickém jazyce

Enhancing Security Monitoring with AI-Enabled Log Collection and NLP Modules on a Unified Open Source Platform

Popis výsledku anglicky

The number of computer attacks continues to increase daily, posing significant challenges to modern security administrators to provide security in their organizations. With the rise of sophisticated cyber threats, it is becoming increasingly difficult to detect and prevent attacks using traditional security measures. As a result, security monitoring solutions such as Security Information and Event Management (SIEM) have become a critical component of modern security infrastructures. However, these solutions still face limitations, and administrators are constantly seeking ways to enhance their capabilities to effectively protect their cyber units. This paper explores how advanced deep learning techniques can help boost security monitoring capabilities by utilizing them throughout all stages of log processing. The presented platform has the potential to fundamentally transform and bring about a significant change in the field of security monitoring with advanced AI capabilities. The study includes a detailed comparison of modern log collection platforms, with the goal of determining the most effective approach. The key benefits of the proposed solution are its scalability and multipurpose nature. The platform integrates an open source solution and allows the organization to connect any event log sources or the entire SIEM solution, normalize and filter data, and use this data to train and deploy different AI models to perform different security monitoring tasks more efficiently.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected Papers

ISBN

978-80-214-6154-3

ISSN

2788-1334

e-ISSN

—

Počet stran výsledku

4

Strana od-do

217-221

Název nakladatele

Brno University of Technology; The Faculty of Electrical Engineering and Communication

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

25. 4. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—