DroidDeep: using Deep Belief Network to characterize and detect android malware

Su, Xin; Shi, Weiqi; Qu, Xiaobo; Zheng, Yi; Liu, Xuchong

doi:10.1007/s00500-019-04589-w

Cited by 22 publications

(17 citation statements)

References 31 publications

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“…A total of 32,856 features were extracted, unique features were learned using a deep learning model based on a deep belief network, and benign and malicious Android software were classified using a support vector machine. On a data set consisting of 3,986 benign applications and 3,986 malicious applications, the model's detection accuracy reached 97.4% when the ratio of benign to malicious applications was 1:1, and the average cost is 6 seconds to analyze and detect each Android application [25].…”

Section: A Malware Detection Using Deep Learning Based On Static Anamentioning

confidence: 99%

“…In [17] [27], the deep automatic encoder is used as the deep neural network's pre-training method to reduce the original feature vector's dimension and shorten the training time. In [16] [17] [25], it has processed the features, in [16], it uses the existing or similarity-based feature extraction method to improve the static features, to achieve the effective feature representation in malware detection. In [17], it codes all features and uses the feature code to represent each application.…”

Section: Research Status Analysismentioning

confidence: 99%

“…In [17], it codes all features and uses the feature code to represent each application. In [25], it uses the deep learning model based on the deep belief network to process the feature, using a behavior feature learning algorithm to select behavior features. The detection system proposed by [19] [25] [28] is lightweight and can be deployed flexibly.…”

Section: Research Status Analysismentioning

confidence: 99%

“…In [25], it uses the deep learning model based on the deep belief network to process the feature, using a behavior feature learning algorithm to select behavior features. The detection system proposed by [19] [25] [28] is lightweight and can be deployed flexibly. The system proposed by [19] can be deployed not only on servers but also on mobile devices and even the Internet of things devices.…”

Section: Research Status Analysismentioning

confidence: 99%

“…The system proposed by [19] can be deployed not only on servers but also on mobile devices and even the Internet of things devices. The proposed system spends an average of 6s to analyze and detect each Android application [25], and the scheme is light enough to be deployed on Android devices with limited resources [28]. In [13] [18] [30], it compared and analyzed the proposed method with the methods based on support vector machine, random forest, logical regression, and k-nearest-neighbor algorithm.…”

Section: Research Status Analysismentioning

confidence: 99%

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Review of Android Malware Detection Based on Deep Learning

2020

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At present, smartphones running the Android operating system have occupied the leading market share. However, due to the Android operating system's open-source nature, Android malware has increased dramatically. Malware can steal user privacy and even maliciously charge fees and steal funds. It has posed a severe threat to cyberspace security because traditional detection methods have many limitations. With the widespread application of deep learning in recent years, the method of detecting Android malware using deep learning has gradually attracted widespread attention from scholars at home and abroad. Although scholars have researched Android malware detection using deep learning, there is currently a lack of a detailed and comprehensive introduction to malware detection's latest research results based on deep learning. In order to solve this problem, this study analyzes and summarizes the latest research results by investigating a large number of the latest domestic and international academic papers, summarizing malware detection architecture and detection schemes, and analyzing existing problems and challenges. This review will help researchers better understand the research status and future research directions in this field. INDEX TERMS Android, malware, deep learning, review.

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Section: A Malware Detection Using Deep Learning Based On Static Anamentioning

confidence: 99%

Section: Research Status Analysismentioning

confidence: 99%

Section: Research Status Analysismentioning

confidence: 99%

Section: Research Status Analysismentioning

confidence: 99%

Section: Research Status Analysismentioning

confidence: 99%

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Review of Android Malware Detection Based on Deep Learning

2020

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Hybrid sequence‐based Android malware detection using natural language processing

Zhang

Xue

et al. 2021

Int J Intell Syst

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Android platform has been the target of attackers due to its openness and increasing popularity. Android malware has explosively increased in recent years, which poses serious threats to Android security. Thus proposing efficient Android malware detection methods is curial in defeating malware. Various features extracted from static or dynamic analysis using machine learning have played an important role in malware detection recently. However, existing code obfuscation, code encryption, and dynamic code loading techniques can be employed to hinder systems that single based on static analysis, purely dynamic analysis systems cannot detect all potential code execution paths. To address these issues, we propose CoDroid, a sequence-based hybrid Android malware detection method, which utilizes the sequences of static opcode and dynamic system call. We treat one sequence as a sentence in the natural language processing and construct a CNN-BiLSTM-Attention classifier which consists of Convolutional Neural Networks (CNNs), the Bidirectional Long Short-Term Memory (BiLSTM) with an attention language model. We extensively evaluate CoDroid under a real-world data set and perform comprehensive analysis against other existing related detection methods. The evaluations show the effectiveness and flexibility of CoDroid across a variety of experimental settings.

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A Lightweight Android Malware Detection Framework Based on Knowledge Distillation

Zhi

Liu

et al. 2021

Network and System Security

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DroidDeep: using Deep Belief Network to characterize and detect android malware

Cited by 22 publications

References 31 publications

Review of Android Malware Detection Based on Deep Learning

Review of Android Malware Detection Based on Deep Learning

Hybrid sequence‐based Android malware detection using natural language processing

A Lightweight Android Malware Detection Framework Based on Knowledge Distillation

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