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Article

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

1
Computer & Systems Engineering Department, School of Engineering & Technology, Badr University in Cairo, Entertainment Area, Badr City 11829, Egypt
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Computer & Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 Elsarayat St., Cairo 11517, Egypt
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School of Information Technology and Computer Science (ITCS), Nile University, 26th of July Corridor, Sheikh Zayed City 12677, Egypt
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Center for Informatics Science, Nile University, 26th of July Corridor, Sheikh Zayed City 12677, Egypt
5
Faculty of Computer Science, Misr International University, KM 28 Cairo—Ismailia Road Ahmed Orabi District, Cairo 11828, Egypt
*
Author to whom correspondence should be addressed.
On Leave from Computer & Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 Elsarayat St., Cairo 11517, Egypt.
Academic Editors: Hany Atlam and Nawfal Fadhel
Sensors 2022, 22(3), 1079; https://doi.org/10.3390/s22031079
Received: 28 December 2021 / Revised: 23 January 2022 / Accepted: 24 January 2022 / Published: 29 January 2022
(This article belongs to the Special Issue Security and Privacy in Internet of Things (IoT))
Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria. View Full-Text
Keywords: data security; triggers/actions; smart homes; software verification data security; triggers/actions; smart homes; software verification
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MDPI and ACS Style

Hamza, A.A.; Abdel Halim, I.T.; Sobh, M.A.; Bahaa-Eldin, A.M. HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps. Sensors 2022, 22, 1079. https://doi.org/10.3390/s22031079

AMA Style

Hamza AA, Abdel Halim IT, Sobh MA, Bahaa-Eldin AM. HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps. Sensors. 2022; 22(3):1079. https://doi.org/10.3390/s22031079

Chicago/Turabian Style

Hamza, Alyaa A., Islam T. Abdel Halim, Mohamed A. Sobh, and Ayman M. Bahaa-Eldin. 2022. "HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps" Sensors 22, no. 3: 1079. https://doi.org/10.3390/s22031079

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