Deep Learning Based Malapps Detection in Android Powered Mobile Cyber-Physical System

Sayed, Moinul Islam; Saha, Sajal; Haque, Anwar

doi:10.1109/icnc57223.2023.10074208

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Several authors have investigated the impact of the DL model on cyberattack detection in CPS. For instance, [27] conducted a comparative analysis of various state-of-the-art deep learning techniques for the classification and categorization of malicious applications. The proposed method involves using an ensemble dynamic weighted voting model to accurately detect and categorize a diverse range of malicious applications using the CCCS-CIC and Mal-2020 datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Abdullahi,

Alhussian,

Aziz

et al. 2024

IEEE Access

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Section: Literature Reviewmentioning

confidence: 99%

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Abdullahi,

Alhussian,

Aziz

et al. 2024

IEEE Access

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A Comparative Performance Analysis of Android Malware Classification Using Supervised and Semi-supervised Deep Learning

Hossain,

Rahman

et al. 2023

2023 16th International Conference on Security of Information and Networks (SIN)

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Android malware analysis and detection: A systematic review

Dahiya,

Singh,

Shrivastava

2023

Expert Systems

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Android malware has been emerged as a significant threat, which includes exposure of confidential information, misrepresentation of facts and execution of applications without the knowledge of the users. Malware analysis plays an essential role in dealing with the unlawful behaviour of such malicious applications. Android malware analysis involves examining and understanding malware behaviour and its characteristics. It also includes potential adversarial impacts on Android devices. This paper presents a quick understanding and a holistic view of malware detection and analysis. The current investigation conducted a systematic literature review (SLR) to recognize the salient shifts in malware detection by examining a range of scholarly journals and conference papers. The SLR investigated 99 articles published between the years 2018 and 2023. The key observation of this SLR is that static analysis is the most implemented approach for detecting Android malware; Apktool and Androguard are the most frequently used tools. This study also conceded that deep learning and machine learning models have more potential to analyse the malicious behaviour of malware. Certain challenges are faced in Android malware analysis, that is, obfuscation techniques, dynamic code loading, and issues related to experimented datasets. Further, this study focuses on the following areas: the definition of the sample set, data optimisation and processing, feature extraction, machine learning application, and classifier validation. This investigation differs from previous analyses of Android malware detection by emphasizing additional methods based on machine learning.

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Deep Learning Based Malapps Detection in Android Powered Mobile Cyber-Physical System

Cited by 3 publications

References 20 publications

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

A Comparative Performance Analysis of Android Malware Classification Using Supervised and Semi-supervised Deep Learning

Android malware analysis and detection: A systematic review

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