Fog-Based Ransomware Detection for Internet of Medical Things Using Lighweight Machine Learning Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021582" target="_blank" >RIV/62690094:18450/24:50021582 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-97-4677-4_17" target="_blank" >http://dx.doi.org/10.1007/978-981-97-4677-4_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-97-4677-4_17" target="_blank" >10.1007/978-981-97-4677-4_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fog-Based Ransomware Detection for Internet of Medical Things Using Lighweight Machine Learning Algorithms

Popis výsledku v původním jazyce

Instances of severe cyber threats such as aggressive attacks,malware, and ransomware have been causing significant harm to computer systems, servers, and various applications across diverse industriesand enterprises. These security issues are of paramount importance andrequire immediate attention. To address these concerns, it is crucial todetect and classify ransomware effectively for prompt response and prevention. This research employs deep learning algorithms to achieve thisgoal by applying three methods which are DNN, LSTM and Bi-LSTM.The approach involves analyzing the behavior patterns of ransomwareand identifying distinctive features that can differentiate between various types of ransomware families. The performance of the models isassessed using a dataset containing instances of ransomware attacks,demonstrating their capability to accurately detect and classify ransomware. Essentially, the study aims to enhance cybersecurity measuresby leveraging advanced techniques in artificial intelligence to combat thegrowing threats posed by ransomware attacks.

Název v anglickém jazyce

Fog-Based Ransomware Detection for Internet of Medical Things Using Lighweight Machine Learning Algorithms

Popis výsledku anglicky

Instances of severe cyber threats such as aggressive attacks,malware, and ransomware have been causing significant harm to computer systems, servers, and various applications across diverse industriesand enterprises. These security issues are of paramount importance andrequire immediate attention. To address these concerns, it is crucial todetect and classify ransomware effectively for prompt response and prevention. This research employs deep learning algorithms to achieve thisgoal by applying three methods which are DNN, LSTM and Bi-LSTM.The approach involves analyzing the behavior patterns of ransomwareand identifying distinctive features that can differentiate between various types of ransomware families. The performance of the models isassessed using a dataset containing instances of ransomware attacks,demonstrating their capability to accurately detect and classify ransomware. Essentially, the study aims to enhance cybersecurity measuresby leveraging advanced techniques in artificial intelligence to combat thegrowing threats posed by ransomware attacks.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Artificial Intelligence, Theory and Applications

ISBN

978-981-9746-76-7

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

200-211

Název nakladatele

Springer Nature

Místo vydání

Berlín

Místo konání akce

Hradec Králové, Czech Republic

Datum konání akce

10. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001315630900017