Detection and robustness evaluation of android malware classifiers

Anupama, M. L.; Vinod, P.; Visaggio, Corrado Aaron; Arya, Monika; Joseph, Philomina; Raphael, Rincy; Pinhero, Anson; Ajith, K. S.; Mathiyalagan, P.

doi:10.1007/s11416-021-00390-2

Cited by 10 publications

(1 citation statement)

References 44 publications

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“…In several studies, the process of feature selection often narrows down to a subset of attributes, often exclusively focusing on either static or dynamic features. For instance, studies Roy et al [106], Li et al [122], and Feng et al [42] tend to emphasize the selection of static features, while studies Anupama et al [63], Liu et al [41], and Arora&Peddoju [12] direct their attention toward dynamic ones. The primary concern arises from the fact that when the feature selection algorithm is exclusively applied to one type of feature, it may overlook the intricate interplay and synergies that exist between static and dynamic attributes.…”

Section: ) Feature Selection Mechanismmentioning

confidence: 99%

Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

Yunmar,

Kusumawardani,

Widyawan

et al. 2024

IEEE Access

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Over the last decade, numerous research efforts have been dedicated to countering malicious mobile applications. Given its market share, Android OS has been the primary target for most of these apps. Researchers have devised numerous solutions to protect Android devices and their users, categorizing them into static and dynamic approaches. Each of these approaches has its own advantages and disadvantages. The hybrid approach aims to combine the benefits of both. This study closely examines the hybrid solutions proposed between 2012 and 2023, highlighting their strengths and limitations. The objective of this study is to provide a comprehensive review of existing research on Android malware detection using a hybrid approach. Our review identifies several issues related to hybrid detection approaches, including datasets, feature utilization and selection, working environments, detection order mechanisms, integrity of the detection step, detection algorithms, and the use of automated input generation. Key findings of this study include: (i) the majority of studies have not adequately addressed on-device detection and have overlooked the importance of system usability, (ii) many studies rely on outdated datasets that do not accurately represent the current threat landscape, (iii) there is a need for a methodology to detect zero-day attacks, and (iv) most research has not paid attention to the impact of automated input generation on malware behavior and code coverage. We also discuss some open issues and future directions on the aspect that will help to substantiate the hybrid approach study.INDEX TERMS Android malware, heuristic-based detection, hybrid approach.

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Section: ) Feature Selection Mechanismmentioning

confidence: 99%

Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

Yunmar,

Kusumawardani,

Widyawan

et al. 2024

IEEE Access

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A comprehensive review on permissions-based Android malware detection

Sharma,

Arora

2024

Int. J. Inf. Secur.

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The first Android-ready "G1" phone debuted in late October 2008. Since then, the growth of Android malware has been explosive analogous to the rise in the popularity of Android. The major positive aspect of Android has been its open-source nature, which empowers app developers to expand their work. But at the same time, authors with malicious intentions pose grave threats to users. In the presence of such threats, Android malware detection is the need of an hour. Consequently, researchers have proposed various techniques involving static, dynamic, and hybrid analysis to address such threats using numerous features in the last decade. But the feature that most researchers have extensively used to perform malware analysis and detection in Android security is Android permission. Hence, to provide a clarified overview of the latest and past work done in Android malware analysis and detection, we perform a comprehensive literature review using permissions as a central feature or in combination with other components by collecting and analyzing 200 studies from January 2009 to February 2023. We extracted information such as the choice opted by researchers between analysis or detection, techniques used to select or rank the permissions feature set, features used along with permissions, detection models employed, the malware datasets used by researchers, and lastly, the limitations and challenges in the field of Android malware detection to propose some future research directions. Additionally, based on the information extracted, we answer the six research questions designed considering the above factors.

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Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization

Mehrabi Hashjin,

Amiri,

Mohammadzadeh

et al. 2024

Cluster Comput

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This paper presents a unique hybrid classifier that combines deep neural networks with a type-III fuzzy system for decision-making. The ensemble incorporates ResNet-18, Efficient Capsule neural network, ResNet-50, the Histogram of Oriented Gradients (HOG) for feature extraction, neighborhood component analysis (NCA) for feature selection, and Support Vector Machine (SVM) for classification. The innovative inputs fed into the type-III fuzzy system come from the outputs of the mentioned neural networks. The system’s rule parameters are fine-tuned using the Improved Chaos Game Optimization algorithm (ICGO). The conventional CGO’s simple random mutation is substituted with wavelet mutation to enhance the CGO algorithm while preserving non-parametricity and computational complexity. The ICGO was evaluated using 126 benchmark functions and 5 engineering problems, comparing its performance with well-known algorithms. It achieved the best results across all functions except for 2 benchmark functions. The introduced classifier is applied to seven malware datasets and consistently outperforms notable networks like AlexNet, ResNet-18, GoogleNet, and Efficient Capsule neural network in 35 separate runs, achieving over 96% accuracy. Additionally, the classifier’s performance is tested on the MNIST and Fashion-MNIST in 10 separate runs. The results show that the new classifier excels in accuracy, precision, sensitivity, specificity, and F1-score compared to other recent classifiers. Based on the statistical analysis, it has been concluded that the ICGO and propose method exhibit significant superiority compared to the examined algorithms and methods. The source code for ICGO is available publicly at https://nimakhodadadi.com/algorithms-%2B-codes. Graphical abstract

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Detection and robustness evaluation of android malware classifiers

Cited by 10 publications

References 44 publications

Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

A comprehensive review on permissions-based Android malware detection

Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization

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