A Survey of Android Malware Static Detection Technology Based on Machine Learning

Wu, Qing; Zhu, Xueling; Liu, Bo

doi:10.1155/2021/8896013

Cited by 27 publications

(20 citation statements)

References 69 publications

(61 reference statements)

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“…The unavailability of standard benchmark datasets was related to malicious samples compared to benign. In addition, there is no suitable classifier, that has the ability to detect new dangerous apps effectively [17,36]. The number of features was limited and varied.…”

Section: General Overviewmentioning

confidence: 99%

Malware Classification Approaches Utilizing Binary and Text Embedding of Permissions

Zyout

Shatnawi

Najadat

2022

Preprint

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The development of mobile applications is rapidly increasing with the advances in smartphone technology. These applications are intended to assist mobile users in accomplishing various daily tasks including e-commerce and online services. Many mobile applications were also developed to attack smartphone users. Hence, new challenges have been found such as the difficulty to distinguish between benign and malicious malware categories.In this paper, two approaches for classifying malware applications are presented, including Conv1d (Conventional one dimension) and LSTM (Long short Time Memory). The proposed approaches have modeled and solved the malware classification problem using the natural language processing method. Two encoding techniques, binary and text encoding, were conducted on android permissions as a preprocessing phase. In addition, two other classifiers, the Support Vector Machine (SVM) and K-Nearest Neighbor(KNN) were also reported. The two main approaches were evaluated on well-known dataset, the CICMalDroid2020. The Conv1d-approach and LSTM-approach using text encoding achieved the best classification, i.e., high precision and accuracy (98.16\%, 97.72\% and 96.63\%, 96.69\%, respectively). The Conv1d-approach on binary classification has outperformed the LSTM-approach model when compared with Mal-prem and datasets. The Conv1d-approach achieved the best performance, with 99.3\%,99.80\%, 98.90\%, and 98.95\% of accuracy.

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Section: General Overviewmentioning

confidence: 99%

Malware Classification Approaches Utilizing Binary and Text Embedding of Permissions

Zyout

Shatnawi

Najadat

2022

Preprint

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“…The objective of theie workr was to create a user profiling method for the mobile identification of malware. The vulnerability of any malware identification strategy has already been noted and debated [74]. With the spread of.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Malware Detection in Android Operating Systems

Hamdi

Ibrahim

Omar

et al. 2021

AJRCoS

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Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static.

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“…Security personnel use various technological approaches to detect and classify Android malware. Such approaches can be categorized as static, dynamic, and mixed forms of analyses, which are performed with the aid of machine learning [5]. Over recent years, most studies on static feature extraction focused on extracting features, such as bytecode, sound, images, log records, code execution paths, control flow graphs, and data flow graphs.…”

Section: Introductionmentioning

confidence: 99%

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Huang¹,

Li²,

Qiao³

et al. 2022

Electronics

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Currently, among the millions of Android applications, there exist numerous malicious programs that pose significant threats to people’s security and privacy. Therefore, it is imperative to develop approaches for detecting Android malware. Recently developed malware detection methods usually rely on various features, such as application programming interface (API) sequences, images, and permissions, thereby ignoring the importance of source code and the associated comments, which are not usually included in malware. Therefore, we propose Android-SEM, which is an Android source code semantic enhancement model based on transfer learning. Our proposed model is built upon the Transformer architecture to achieve a pretraining framework for generating code comments from malware source code. The performance of the pretraining framework is optimized using a generative adversarial network. Our proposed model relies on a novel regression model-based filter to retain high-quality comments and source code for feature fusion pertinent to semantic enhancement. Creatively, and contrary to conventional methods, we incorporated a quantum support vector machine (QSVM) for classifying malicious Android code by combining quantum machine learning and classical deep learning models. The results proved that Android-SEM achieves accuracy levels of 99.55% and 99.01% for malware detection and malware categorization, respectively.

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A Survey of Android Malware Static Detection Technology Based on Machine Learning

Cited by 27 publications

References 69 publications

Malware Classification Approaches Utilizing Binary and Text Embedding of Permissions

Malware Classification Approaches Utilizing Binary and Text Embedding of Permissions

A Comprehensive Study of Malware Detection in Android Operating Systems

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

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