SEDMDroid: An Enhanced Stacking Ensemble Framework for Android Malware Detection

Zhu, Huijuan; Li, Yang; Li, Ruidong; Li, Jianqiang; You, Zhu‐Hong; Song, Houbing

doi:10.1109/tnse.2020.2996379

Cited by 91 publications

(34 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detection techniques that uses dynamic analysis were resource consuming and the techniques that use static analysis suffers from tracking runtime behavior. H. Zhu [16] et al proposed a stacking ensemble model for malware detection. Static features are extracted from android apps and a feature vector is generated.…”

Section: Literature Reviewmentioning

confidence: 99%

Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Lakshmanarao

Shashi

2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Android is the most widely used operating system in smartphones. Mobile users can download and access apps easily from the play store. Due to lack of security awareness and risk associated with mobile apps, malware apps would be downloaded by normal users in general. The consequences after installing a malware app are unpredictable. Malware apps can gather user personal data, browsing history, user profiles, user sensitive data like passwords. Hence, android malware detection is essential for providing security to mobile users. Android malware detection using machine learning is done either by extracting static features (opcodes, permissions, intents, system commands) or by extracting dynamic features (log behavior, system calls, dataflow). In this paper, opcode sequences are extracted from malware and benign apps, and Recurrent Neural Networks are proposed on extracted sequences. Benign apps are collected from the play store, apkpure.com and malware apps are collected from the virus share website. The proposed Recurrent Neural Network model could achieve 96% accuracy for android malware detection.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Lakshmanarao

Shashi

2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…The authors of [175] proposed a DL-based method employing an ensemble of base MLP classifiers and a fusion SVM classifier. In [176], the authors presented an algorithm that classified malware samples (even unknown) into families.…”

Section: E Ensemble Classifiersmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

“…Zhu et al [23] created the SEDMDroid framework to identify Android malware using an upgraded deep-learning stacked ensemble technique. This dual-layered classifier architecture used an MLP classifier on the first tier and an SVM fusion classifier on the second.…”

Section: Plos Onementioning

confidence: 99%

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer

2021

View full text Add to dashboard Cite

Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on this consumer reliance and vulnerabilities present in the system. Hackers often use confidential user data to exploit users for advertising, extortion, and theft. Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization. Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android application features. JS is used to determine the optimal configurations of RVFL to improve classification performance. RVFL+JS minimizes the runtime of the execution of the optimized models with the best performance metrics, based on a dataset consisting of 11,598 multi-class applications and 471 static and dynamic features.

show abstract

SEDMDroid: An Enhanced Stacking Ensemble Framework for Android Malware Detection

Cited by 91 publications

References 50 publications

Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer

Contact Info

Product

Resources

About