A Systematic Literature Review of Android Malware Detection Using Static Analysis

Pan, Ya; Ge, Xiuting; Fang, Chunrong; Yi, Fan

doi:10.1109/access.2020.3002842

Cited by 126 publications

(73 citation statements)

References 107 publications

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“…One of the Android malware detected in September 2010 was first discovered on a smartphone. In the future, the number of malicious applications would steadily rise shortly afterwards [34].…”

Section: Android Operating Systemmentioning

confidence: 99%

Efficiency of Malware Detection in Android System: A Survey

Omer

Zeebaree

Sadeeq

et al. 2021

AJRCoS

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Smart phones are becoming essential in our lives, and Android is one of the most popular operating systems. Android OS is wide-ranging in the mobile industry today because of its open-source architecture. It is a wide variety of applications and basic features. App users tend to trust Android OS to secure data, but it has been shown that Android is more vulnerable and unstable. Identification of Android OS malware has become an emerging research subject of concern. This paper aims to analyze the various characteristics involved in malware detection. It also addresses malware detection methods. The current detection mechanism utilizes algorithms such as Bayesian algorithm, Ada grad algorithm, Naïve Bayes algorithm, Hybrid algorithm, and other algorithms for machine learning to train the sets and find the malware.

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Section: Android Operating Systemmentioning

confidence: 99%

Efficiency of Malware Detection in Android System: A Survey

Omer

Zeebaree

Sadeeq

et al. 2021

AJRCoS

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show abstract

“…The first trend dedicated to security of Android is provided in three ways. Static analysis [4] aims at looking into app features such as permissions, source codes and other static characteristics to detect abnormal activities. Static analysis is not able to observe exploits deployed during runtime.…”

Section: Security Of Android Appsmentioning

confidence: 99%

“…The last aspect is a major challenge since it preserves confidentiality, integrity and availability of user data. On Android, most of the approaches static analysis relying static features related to applications [4][5][6][7][8], dynamic analysis relying on features observed during their execution [9][10][11] and hybrid analysis combining the both [12][13][14]. Despite Google Play Protect provided by Google, to filter threats, there are still malicious applications carefully designed by bad people to have an impact on the security and privacy of users [5,15].…”

Section: Introductionmentioning

confidence: 99%

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CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme of Android Apps Based on Sentiment Analysis of Reviews

Tchakounté¹,

Pagore²,

Kamgang³

et al. 2020

Preprint

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To keep its business reliable, Google is concerned to ensure quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7.835.322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication and availability, while the remaining 77% has a polarity under 0.5. Developers should make lot of efforts in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications works without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated to existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.

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“…e authors in [14] investigated Android malware detection and protection technology based on data mining algorithms. e study in [15,16] collected the literature research of the past few years, systematically analysed the static detection technology, and discussed datasets, features, algorithms, empirical experiments, and performance measures. e study in [17] introduced the Android architecture, security mechanism, malware classification, and entire detection process, including sample collection, data preprocessing, feature selection, machine learning model construction, and experimental evaluation.…”

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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A Systematic Literature Review of Android Malware Detection Using Static Analysis

Cited by 126 publications

References 107 publications

Efficiency of Malware Detection in Android System: A Survey

Efficiency of Malware Detection in Android System: A Survey

CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme of Android Apps Based on Sentiment Analysis of Reviews

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

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