Fog-Based Ransomware Detection for Internet of Medical Things Using Lighweight Machine Learning Algorithms
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Fog-Based Ransomware Detection for Internet of Medical Things Using Lighweight Machine Learning Algorithms
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Artificial Intelligence, Theory and Applications
ISBN
978-981-9746-76-7
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
12
Pages from-to
200-211
Publisher name
Springer Nature
Place of publication
Berlín
Event location
Hradec Králové, Czech Republic
Event date
Jul 10, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001315630900017