Android malware analysis approach based on control flow graphs and machine learning algorithms

Atici, Mehmet Ali; Sağıroğlu, Şeref; Doğru, İbrahim Alper

doi:10.1109/isdfs.2016.7473512

Cited by 25 publications

(14 citation statements)

References 20 publications

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“…Naive Bayes and KNN machine learning algorithms were used in this study to classify permissions [23]. Atici et al developed a static system based on machine learning algorithms and control flow graphs of Dalvik byte codes for Android malware analysis [24]. In this study, grammatical expressions consisting of control flow graphs of Android malicious software were used as an input vector [24].…”

Section: Static Analysis Methodsmentioning

confidence: 99%

Web-Based Android Malicious Software Detection and Classification System

Doğru¹,

Kiraz²

2018

Applied Sciences

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Android is the most used operating system (OS) by mobile devices. Since applications uploaded to Google Play and other stores are not analyzed comprehensively, it is not known whether the applications are malicious software or not. Therefore, there is an urgent need to analyze these applications regarding malicious software. Moreover, mobile devices have limited resources to analyze the applications. In this study, a malicious detection system named “Web-Based Android Malicious Software Detection and Classification System” was developed. The system is based on client-server architecture, static analysis and web-scraping methods. The proposed system overcomes the resource restriction issue, as well as providing third-party service support by means of client-server architecture. Based on the performance evaluation conducted in this research, the developed system’s success rate is 97.62% on benign and malicious datasets.

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Section: Static Analysis Methodsmentioning

confidence: 99%

Web-Based Android Malicious Software Detection and Classification System

Doğru¹,

Kiraz²

2018

Applied Sciences

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“…[94], [109], [115], [119], [138], [139], [142], [144], [145], [147]- [149], [154], [163], [167], [177], [188], [193], [202], [206], [214]- [216], [218], [244], [248] Linear Model (LM)…”

Section: ) Machine Learning Models and Algorithms Used In Android Maunclassified

“…[90], [94], [102], [105], [108], [109], [119], [136], [142], [145]- [147], [149], [153], [163] [166], [173], [185], [187], [189], [190], [ 192], [195], [202], [206], [214]- [216], [219], [244], [245] K-means Simple, fast, and easy to implement.…”

Section: ) Machine Learning Models and Algorithms Used In Android Mamentioning

confidence: 99%

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A Review of Android Malware Detection Approaches Based on Machine Learning

et al. 2020

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Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. We believe our work complements the previous reviews by surveying a wider range of aspects of the topic. This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware. Then, taking machine learning as the focus, we analyze and summarize the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness. Finally, we assess the future prospects for research into Android malware detection based on machine learning. This review will help academics gain a full picture of Android malware detection based on machine learning. It could then serve as a basis for subsequent researchers to start new work and help to guide research in the field more generally.

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“…The detection and analysis model of malicious code consists of two pieces: feature extraction and classification. The current feature extraction method is usually divided into serval types: static analysis [5], [6], dynamic analysis [7], [8], dynamic and static fused analysis [9]- [11], the graphs-based approach [12]- [14] et.…”

Section: Related Workmentioning

confidence: 99%

Android Malware Analysis and Detection Based on Attention-CNN-LSTM

Luo¹,

Liu²,

Li³

et al. 2019

JCP

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The increasing number of Android malware has made detection and analysis more difficult, aiming to the current malware attacking Android. This paper proposes an Android malware analysis and detection technology based on Attention-CNN-LSTM, which is a types of Multimodel Deep Learning. Selecting open source malware datasets of Drebin for research, extracting texture fingerprint information of Android malware to reflect the similarity of malware binary file blocks, at the same time, in order to improve the detection accuracy, AndroidMainfest.xml is treated as a text document, and its contextual text features are extracted through NLP. Besides, the above two types of features are merged to enhance the expression capability of texture fingerprint information , and Deep Belief Network is used to screen the above features. Above all, the texture fingerprint is processed by one-dimensional serial signal processing, and the end-to-end local correlation features are extracted according to a one-dimensional time-do main convolutional network. At the same time, considering the context relationship of the timing signal for the AndroidMainfest.xml text, combined with the LSTM model with stronger time-series modeling capabilities to analyze and detect the Android malicious code. The experimental results show that the proposed method can detect and analyze malware more effectively.

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Android malware analysis approach based on control flow graphs and machine learning algorithms

Cited by 25 publications

References 20 publications

Web-Based Android Malicious Software Detection and Classification System

Web-Based Android Malicious Software Detection and Classification System

A Review of Android Malware Detection Approaches Based on Machine Learning

Android Malware Analysis and Detection Based on Attention-CNN-LSTM

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