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

Hamza, Alyaa A.; Abdel Halim, Islam Tharwat; Sobh, Mohamed A.; Bahaa-Eldin, Ayman M.

doi:10.3390/s22031079

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

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

Alyaa A. Hamza

^1,2,*

Islam Tharwat Abdel Halim

^3,4

Mohamed A. Sobh

² and

Ayman M. Bahaa-Eldin

^5,†

Computer & Systems Engineering Department, School of Engineering & Technology, Badr University in Cairo, Entertainment Area, Badr City 11829, Egypt

Computer & Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 Elsarayat St., Cairo 11517, Egypt

School of Information Technology and Computer Science (ITCS), Nile University, 26th of July Corridor, Sheikh Zayed City 12677, Egypt

⁴

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))

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

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

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

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

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Access Map by Country/Region

Author Biographies

Alyaa A. Hamza

Alyaa A. Hamza received her B.Sc. and M.Sc. in Computers and Systems Engineering, Faculty of Engineering, Mansoura University, in 2011 and 2017. She is currently pursuing a Ph.D. degree in Computer and Systems Engineering, Faculty of Engineering, Ain Shams University, Cairo. Her research interests are wireless Networks, network security, programming language, and machine learning. She is currently an assistant lecturer in computer and systems engineering with the Faculty of Engineering, Badr University in Cairo (BUC), Cairo, Egypt.

Islam Tharwat Abdel Halim

Islam Tharwat Abdel Halim received his B.Sc. and M.Sc. degrees and the Ph.D. degree in computer and systems engineering from the Faculty of Engineering, Ain Shams University, Cairo, Egypt, in 2004, 2011, and 2019, respectively. His Ph.D thesis was granted the best thesis award. He is currently an Assistant Professor in the School of Information Technology and Computer Science, Nile University (NU), Giza, Egypt. He has authored many research articles in various refereed conferences and journals, including IEEE Intelligent Transportation Systems Magazine, Computer Networks (Elsevier), and Wireless Networks (Springer). He is an active member in IEEE Computer Society and he is currently appointed as the Chair of the Awareness and Customer Advocacy Subcommittee in the Distinguished Visitor Program (DVP), IEEE Computer Society. His current research interests include IoT, Fog/Edge computing, mobile and wireless networks with emphasis on security and performance evaluation in vehicular communication and 5G networks.

Mohamed A. Sobh

Mohamed A. Sobh received his B.Sc., M.Sc., and Ph.D. degrees in Computer and Systems Engineering from the Faculty of Engineering, Ain Shams University, Cairo, Egypt, in 1996, 2001, and 2007, respectively. He is currently an associate professor of computer and systems engineering with Ain Shams University. He has authored or co-authored many research works, which have been published in well-known international refereed journals and conferences such as IEEE, Springer, and Elsevier, that are indexed by ISI, SCOPUS, and Science Direct.

Ayman M. Bahaa-Eldin

Ayman M. Bahaa-Eldin is a professor in the Computer and Systems Eng. Dept., Ain Shams University. Prof. Bahaa-Eldin received his B.Sc., M.Sc., and PhD. in Computer Engineering from Ain Shams University in 1995, 1999, and 2004, respectively. He has been a visiting professor in several local and international universities. He was appointed as the managing director of the Egyptian Universities Network (EUN), The Egyptian National Research and Education Network (NREN), from 2010 until 2014. His fields of research are cryptography, computer networks, and computer and network security. He has published more than 60 papers in refereed international journals and conferences, managed and participated in several national and international research projects, and participated in the reviewing process of many international journals and conferences.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Author Biographies

Further Information

Guidelines

MDPI Initiatives

Follow MDPI