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. 2022 Nov 29;22(23):9305.
doi: 10.3390/s22239305.

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning

Sharjeel Riaz  1 , Shahzad Latif  1 , Syed Muhammad Usman  2 , Syed Sajid Ullah  3 , Abeer D Algarni  4 , Amanullah Yasin  2 , Aamir Anwar  5 , Hela Elmannai  4 , Saddam Hussain  6
Affiliations

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning

Sharjeel Riaz et al. Sensors (Basel). .

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%.

Keywords: CNN; Internet of Things; LSTM; malware detection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed system model for hybrid deep learning malware detection algorithms.
Figure 2
Figure 2
Proposed flow for hybrid machine learning malware detection algorithms.
Figure 3
Figure 3
Opcode extraction.
Figure 4
Figure 4
Testing time of machine learning algorithms.
Figure 5
Figure 5
(a) Confusion matrix for Hybrid CNN-LSTM (b) Confusion matrix for PSO-KNN.
See this image and copyright information in PMC

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