A  Review on Android Malware: Attacks, Countermeasures and Challenges Ahead

Selvaganapathy, Shymala Gowri; Sadasivam, G. Sudha; Ravi, Vinayakumar

doi:10.13052/jcsm2245-1439.1017

Cited by 17 publications

(14 citation statements)

References 116 publications

(181 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evade malware detection systems, malicious software developers usually imply small modifications on the original source code of the malware app to generate new malicious software variants. In consequence, identifying the new malicious software variants becomes challenging even if they belong to the same family [3].…”

Section: Introductionmentioning

confidence: 99%

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

2022

View full text Add to dashboard Cite

Recently, cybersecurity experts and researchers have given special attention to developing cost-effective deep learning (DL)-based algorithms for Android malware detection (AMD) systems. However, the conventional AMD solutions necessitate extensive computations to achieve high accuracy in detecting Android malware apps. Consequently, there is a significant benefit in utilizing convolution neural networks (CNNs) in vision-based AMD applications to quickly and efficiently learn without prior stages of reverse engineering processes. Thus, this paper introduces an efficient and automated vision-based AMD model composed of 16 well-developed and fine-tuned CNN algorithms. This model precludes the need for a pre-designated features extraction process while generating accurate predictions of malware images with minimum cost and high detection speed. Such performance is achieved with colored or grayscale malware images, whether by using balanced or imbalanced datasets. Firstly, the bytecodes of the "classes.dex" files extracted from the Android benign and malware apps were converted to color and grayscale visual images before forwarding them to the developed CNN algorithms for classification. Then, the detection efficiency of the proposed AMD model was examined and evaluated using the imbalanced benchmark Leopard Android dataset that composes 14733 samples of malware apps and 2486 samples of benign apps. Finally, different experimental scenarios were conducted using balanced and imbalanced Android samples of color and grayscale images generated from the Leopard dataset; to extensively and sufficiently validate the detection and classification performance of the suggested model. A comprehensive assessment classification parameters in the evaluation experiments were applied to prove the high capability of the developed finetuned CNN algorithms in recognizing Android malware attacks with low computational overhead. As a result, the detection accuracy reached 99.40% for balanced samples and 98.05% for imbalanced samples. Furthermore, the proposed AMD model outperforms the existing approaches that utilize conventional vision-based algorithms and tested on the same benchmark Android dataset.

show abstract

Section: Introductionmentioning

confidence: 99%

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

2022

View full text Add to dashboard Cite

show abstract

“…Various surveys were conducted on problem-space evasion attacks, e.g. [22,23,24,25,26,27,28,29,30,31,32,33,34]. However, some of these surveys are general and therefore do not focus on a specific domain of evasion attacks [23,27,28,30,32,33].…”

Section: Introductionmentioning

confidence: 99%

“…Other surveys analyze domains other than Android OS, such as adversarial network examples, Windows PEs, Natural Language Processing, and images [22,24,25,26,29]. Several surveys focused on evasion attacks on the Android OS domain, for example [35,31,34]. While Bhusal and Rastogi [35] presented work on the nature of feature-space and problem-space attacks, they only explored one problem-space evasion attack.…”

Section: Introductionmentioning

confidence: 99%

“…While Bhusal and Rastogi [35] presented work on the nature of feature-space and problem-space attacks, they only explored one problem-space evasion attack. Also, [31,34] surveys suggest a different taxonomy, which focuses on the evasion techniques, while our survey analyzes different aspects that have increased in recent years; For example, the importance of functional evaluation [2] and the orientation of different problem-space attacks. Second, exploring problem-space attacks is vital to test the realistic assumptions of feature-space evasion attacks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Problem-Space Evasion Attacks in the Android OS: a Survey

Berger¹,

Hajaj²,

Dvir³

2022

Preprint

View full text Add to dashboard Cite

Android is the most popular OS worldwide. Therefore, it is a target for various kinds of malware. As a countermeasure, the security community works day and night to develop appropriate Android malware detection systems, with MLbased or DL-based systems considered as some of the most common types.Against these detection systems, intelligent adversaries develop a wide set of evasion attacks, in which an attacker slightly modifies a malware sample to evade its target detection system. In this survey, we address problem-space evasion attacks in the Android OS, where attackers manipulate actual APKs, rather than their extracted feature vector. We aim to explore these kind of attacks, frequently overlooked by the research community due to a lack of knowledge of the Android domain, or due to focusing on general mathematical evasion attacks -i.e., feature-space evasion attacks. We discuss the different aspects of problemspace evasion attacks, using a new taxonomy, which focuses on key ingredients of each problem-space attack, such as the attacker model, the attacker's mode of operation, and the functionality assessment of post-attack applications.

show abstract

“…Consequently, the identification process of Android malware attacks from the same malware family becomes tremendously challenging [ 5 ]. Therefore, efficient Android malware detection algorithms based on smart artificial intelligence (AI) tools need to be developed and implemented to identify and recognize the harmful effect of Android malware threats [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Android malware analysis in a nutshell

2022

View full text Add to dashboard Cite

This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems. However, this paper comprehensively highlights these factors and their impacts through a deep empirical study. The study comprises 22 CNN (Convolutional Neural Network) algorithms, 21 of them are well-known, and one proposed algorithm. Additionally, several types of files are considered before converting them to images, and two benchmark android malware datasets are utilized. Finally, comprehensive evaluation metrics are measured to assess the produced predictive models from the security and complexity perspectives. Consequently, guiding researchers and developers to plan and build efficient malware analysis systems that meet their requirements and resources. The results reveal that some factors might significantly impact the performance of the malware analysis solution. For example, from a security perspective, the accuracy, F1-score, precision, and recall are improved by 131.29%, 236.44%, 192%, and 131.29%, respectively, when changing one factor and fixing all other factors under study. Similar results are observed in the case of complexity assessment, including testing time, CPU usage, storage size, and pre-processing speed, proving the importance of the proposed android malware analysis model.

show abstract

A Review on Android Malware: Attacks, Countermeasures and Challenges Ahead

Cited by 17 publications

References 116 publications

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

Problem-Space Evasion Attacks in the Android OS: a Survey

Android malware analysis in a nutshell

Contact Info

Product

Resources

About