Malware Detection in Internet of Things (IoT) Devices Using Deep Learning.
Sensors (Basel)
; 22(23)2022 Nov 29.
Article
em En
| MEDLINE
| ID: mdl-36502007
ABSTRACT
Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
/
Internet das Coisas
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Paquistão