Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

Aurangzeb, Sana; Aleem, Muhammad

doi:10.1038/s41598-023-30028-w

Cited by 27 publications

(9 citation statements)

References 32 publications

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“…One such detection algorithm which used ensemble learning method namely voting classi er for detection of malware samples [11]. The limitations are many in traditional security mechanisms and to overcome it.…”

Section: Continuous Monitoring Of System Callsmentioning

confidence: 99%

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

Nallabala

2024

Preprint

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Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models.

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Section: Continuous Monitoring Of System Callsmentioning

confidence: 99%

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

Nallabala

2024

Preprint

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“…The literature on intrusion detection on Android devices encompasses three primary methodologies, static, dynamic, and hybrid, applied to malware detection in Android applications. Static techniques like DroidSieve [39]. Dynamic techniques [40][41][42], leveraging machine and deep learning, analyze application-layer features.…”

Section: Related Work Based On Machine Learningmentioning

confidence: 99%

ZTA: A Novel Zero Trust Framework for Detection and Prevention of Malicious Android Applications

Nazir,

Iqbal,

Muhammad

2024

Preprint

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The proliferation of preloaded apps and third-party applications has raised significant concerns regarding user security and privacy. This study introduces the Zero Trust Architecture (ZTA), a novel approach to addressing the issues caused by poorly designed vulnerable mobile applications. ZTA employs a comprehensive strategy to safeguard user privacy, incorporating thorough app permission analysis alongside dynamic behavior assessment. The proposed framework closely monitors real-time app behavior and leverages the MITRE ATT&CK framework to identify security threats, vulnerabilities, and essential Tactics, Techniques, and Procedures (TTPs). The ZTA empowers users to make informed choices about app usage, promote data security, and cultivate a safer environment. Through this framework, users can confidently decide on app usage, ensuring the safeguarding of their data and fostering a more secure mobile app ecosystem. This research presents a proactive approach to privacy-conscious app utilization, bolstering user trust in the continually evolving realm of Android applications.

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“…Their study included an Enhanced PCA Algorithm, illuminating creative strategies to address the escalating difficulties in malware identification. The analysis and classification of malware for Android that is obfuscated was covered in depth by Aurangzeb and Aleem [44]. They used ensemble voting and deep learning approaches to provide insights into the changing malware obfuscation and detection landscape.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Android Malware Using Machine Learning and Siamese Shot Learning Technique for Security

Almarshad,

Zakariah,

Gashgari

et al. 2023

IEEE Access

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Android malware security tools that can swiftly identify and categorize various malware classes to create rapid response strategies have been trendy in recent years. Although many application fields have demonstrated the usefulness of implementing Machine Learning and deep learning methods to provide automation and self-learning services, the scarcity of data for malware samples has been cited as a hurdle in creating efficient deep learning-based solutions. In this paper, a one-shot learning-based Siamese neural network is proposed to overcome this issue, as it can both identify malware assaults and categorize malware into multiple categories. The Drebin dataset, which is divided into benign and harmful components, is used in our suggested methodology. The efficiency of the suggested strategy is evaluated through a database dataset made up of 9476 goodware applications and 5560 Android malware apps. The five critical phases of its implementation are pre-processing, data partitioning, model architecture, training, and assessment. In both the training and testing phases, Siamese networks are trained to rank sample similarity, and the accuracy is determined using N-way one-shot tasks. According to the experiment's findings, our Siamese Shot model fared better than the other standard approaches, obtaining an accuracy of 98.9%. Additionally, the most wellliked platforms are Keras and TensorFlow.

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Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

Cited by 27 publications

References 32 publications

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

ZTA: A Novel Zero Trust Framework for Detection and Prevention of Malicious Android Applications

Detection of Android Malware Using Machine Learning and Siamese Shot Learning Technique for Security

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