Review of Android Malware Detection Based on Deep Learning

Wang, Zhiqiang; Liu, Qian; Chi, Yaping

doi:10.1109/access.2020.3028370

Cited by 52 publications

(27 citation statements)

References 81 publications

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“…Due to API changes, and now able to capture previously unseen activities. The results also demon state that EveDroid is more accurate and robust to malware evolvement compared to existing detection systems [93]. Classification utilizing machine learning was an important class of malware security solutions.…”

Section: Fig 2 Architecture Of Droid Deep [57]mentioning

confidence: 77%

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

Hamdi

Omar

Al-Zebari

et al. 2021

AJRCoS

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Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. 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. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.

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Section: Fig 2 Architecture Of Droid Deep [57]mentioning

confidence: 77%

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

Hamdi

Omar

Al-Zebari

et al. 2021

AJRCoS

View full text Add to dashboard Cite

show abstract

“…e authors in [7][8][9] analysed the Android security mechanism and typical malware detection methods. e authors in [10][11][12] focused on applying deep learning algorithms such as Restricted Boltzmann Machines, Convolutional Neural Network, Deep Belief Network, Recurrent Neural Network, and Deep Autoencoder to malware detection and analysed the advantages and results achieved. e authors in [13] is mainly concerned with the analysis of Android malware variants' detection methods.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Liu

2021

Mobile Information Systems

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With the rapid growth of Android devices and applications, the Android environment faces more security threats. Malicious applications stealing usersʼ privacy information, sending text messages to trigger deductions, exploiting privilege escalation to control the system, etc., cause significant harm to end users. To detect Android malware, researchers have proposed various techniques, among which the machine learning-based methods with static features of apps as input vectors have apparent advantages in code coverage, operational efficiency, and massive sample detection. In this paper, we investigated Android applicationsʼ structure, analysed various sources of static features, reviewed the machine learning methods for detecting Android malware, studied the advantages and limitations of these methods, and discussed the future directions in this field. Our work will help researchers better understand the current research state, the benefits and weaknesses of each approach, and future technology directions.

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“…K. Liu et al [5] presented research on machine learning-based Android malware detection conducted up to the year 2020. In addition, Z. Wang et al [6] published a review of Android malware detection applying deep learning. Rana et al [7] also found an algorithm that best classifies malware through experiments, aiming to find the most effective malware detector among 12 different machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

Lee

Jang

et al. 2021

Mathematics

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Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.

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Review of Android Malware Detection Based on Deep Learning

Cited by 52 publications

References 81 publications

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

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

Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

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