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.

/pdf/a-review-of-android-malware-detection-approaches-based-on-1637q9qc65.pdf

A Review of Android Malware Detection Approaches Based on Machine Learning

Malware is a serious threat that has been used to target mobile devices since its inception. Two types of mobile malware attacks are standalone: fraudulent mobile apps and injected malicious apps. Defending against the cyber threats of mobile malware requires a strong understanding of the permissions declared in applications and application programmeinterface (API) calls. In this paper, we propose an effective classification model that combines permission requests and API calls. As Android apps use a large number of APIs, we propose three different grouping strategies for choosing the most valuable API calls to maximize the likelihood of identifying Android malware apps: the ambiguous group, risky group, and disruptive group. The results demonstrate that compared with benign apps, malicious applications invoke a different set of API calls and that mobile malware often requests dangerous permissions to access sensitive data more often than benign apps. Empirical results obtained with a real malware dataset containing 27,891 Android apps suggest that our proposed method is effective at detecting mobile malware apps and achieves an F-measure of 94.3%. Our model can significantly assist in the process of malware forensic investigation and mobile application analysis.

Intelligent mobile malware detection using permission requests and API calls

Android malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it poses great threats to users such as the leakage of personal information and advanced fraud To address these threats, various techniques are proposed by researchers and practitioners Static analysis is one of these techniques, which is widely applied to Android malware detection and can detect malware quickly and prohibit malware before installation To provide a clarified overview of the latest work in Android malware detection using static analysis, we perform a systematic literature review by identifying 98 studies from January 2014 to March 2020 Based on the features of applications, we first divide static analysis in Android malware detection into four categories, which include Android characteristic-based method, opcode-based method, program graph-based method, and symbolic execution-based method Then we assess the malware detection capability of static analysis, and we compare the performance of different models in Android malware detection by analyzing the results of empirical evidence Finally, it is concluded that static analysis is effective to detect Android malware Moreover, there is a preliminary result that neural network model outperforms the non-neural network model in Android malware detection However, static analysis still faces many challenges Thus, it is necessary to derive some novel techniques for improving Android malware detection based on the current research community Moreover, it is essential to establish a unified platform that is used to evaluate the performance of a series of techniques in Android malware detection fairly

/pdf/a-systematic-literature-review-of-android-malware-detection-4u3v9t37cu.pdf

A Systematic Literature Review of Android Malware Detection Using Static Analysis

Ransomware is a very effective form of malware that is recently spreading out on an impressive number of workstations and smartphones. This malware blocks the access to the infected machine or to the files located in the infected machine. The attackers will restore the machine and files only after the payment of a certain amount of money, usually given in the form of bitcoins. Commercial solutions are still ineffective to recognize the last variants of ransomware, and the problem has been poorly investigated in literature. In this paper we discuss a methodology based on formal methods for detecting ransomware malware on Android devices. We have implemented our method in a tool named Talos. We evaluate the method, and the obtained results show that Talos is very effective in recognizing ransomware (accuracy of 0.99) even when it is obfuscated (accuracy still remains at 0.99).

Talos: no more ransomware victims with formal methods

Malicious application detection in android — A systematic literature review

CENDroid—A cluster-ensemble classifier for detecting malicious Android applications

The growth of Android mobile platform has led to the increase in the number of malicious applications. Malware creators are ahead of the malware detectors. In this paper, we present eight techniques of hiding a malicious Android application inside images (PNG/JPEG) by using methods such as Concatenation, Obfuscation, Cryptography, and Steganography separately and in conjunction. The image containing the malicious application is stored in the resources of another Android application. After hiding the malicious application using these techniques, we evaluated the vulnerability of ten popular and freely downloadable commercial Android anti-malwares towards them. The results were alarming as only one of them was able to detect two hiding techniques in which the malicious Android application (or its obfuscated version) was hidden by concatenating it at the end of an image and rest all the other anti-malwares were blind towards the eight hiding techniques. If the malicious Android application (or its obfuscated version) is not hidden inside an image but resides as it is in the resources of another Android application, seven out of ten anti-malwares flagged such applications as malicious. Such an evaluation provides a metric for measurement of the available defense against evolving Android malware and also aids in improving the state of the art of the Android malware detection systems.

Evading android anti-malware by hiding malicious application inside images

The amount of Android malware is increasing faster every year along with the growing popularity of Android platform. Hence, detection and analysis of Android malware have become a critical topic in the area of computer security. This paper proposes a novel method of detecting Android malware that uses the semantics of the code in the form of code graphs extracted from Android apps. These code graphs are then used for classifying Android apps as benign or malicious by using the Jaccard index of the code graphs as a similarity metric. We have also evaluated code graph of real-world Android apps by using the k-NN classifier with Jaccard distance as the distance metric for classification. The result of our experiment shows that code graph of Android apps can be used effectively to detect Android malware with the k-NN classifier, giving a high accuracy of 98%.

Android Malware Detection Using Code Graphs

Malware creators have been very innovative when it comes to creating versions of existing malware to evade detection by anti-malware tools. Obfuscation has been the all-time favorite weapon...

Analyzing Android Code Graphs against Code Obfuscation and App Hiding Techniques

Malware creators have been very opportunistic in creating novel malware by leveraging the vulnerabilities which can be either at system level or environmental level. Covid-19 pandemic was the next opportunity for them. Novel malwares were seen exploiting the new normal lifestyle during the Covid-19 pandemic. In this paper, we explore the malwares that were observed specially during the Covid-19 pandemic and then present an analysis of malware detection techniques with a focus on these Covid-19-themed malwares. This study aims to set a baseline for cyber security researchers exploring the malwares that surged during the Covid-19 pandemic and the malware detection techniques.

An Analysis of Malware Detection and Control through Covid-19 Pandemic

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