Malware Detection Techniques for Mobile Devices

Amro, Belal

doi:10.5121/ijmnct.2017.7601

Cited by 20 publications

(6 citation statements)

References 3 publications

(4 reference statements)

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“…Recently, this work was extended by Svajlenko and Roy [13] to include more clone detection tools. Amro [14] summarized most of the malware detection techniques used by Android and iOS, and he presented a brief description of each technique. Tchakounté et al [15] proposed an Android malware detection tool, LimonDroid, and they tested the tool with tens of malicious and benign apps.…”

Section: Related Workmentioning

confidence: 99%

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Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

Ebad,

Darem

2024

Electronics

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Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula.

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Section: Related Workmentioning

confidence: 99%

“…In conclusion, obfuscators have not attracted the attention of sufficient researchers. Most attempts have focused on a small number of obfuscators [1,11,12,[14][15][16]. Others narrowed their research to specific types of obfuscators, such as plagiarism/cloning [6,13,17], network protocols [10], or JavaScript [1].…”

Section: Related Workmentioning

confidence: 99%

Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

Ebad,

Darem

2024

Electronics

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“…This type of analysis can be divided into signature-based analysis, permission-based analysis, and virtual machine analysis. It is based on the reliance of analyzing the source code [24]. A static analysis of Android malware detection is applied using AndroidManifest.xml, smali files, and a set of static features including permissions, API calls, Dalvik opcode, and other components, which can be obtained by decompiling the APK files, the main objective for analysis [5].…”

Section: Static and Dynamic Analysismentioning

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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“…Grisham et al [19] have used recurrent neural networks to identify the infected attachments and then carry the social network analysis for finding the key hackers disseminating from mobile devices. Amro [20] have analyzed different malware detection techniques that suit mobile operating systems like Android and iOS. Azar et al [21] have presented a method, "analytics," that focuses on extracting static features of any binary file to distinguish malware.…”

Section: Related Workmentioning

confidence: 99%

Protecting Android based applications from malware affected through SMS messages

Bhanu

Sastry

Reddy

2021

IJEECS

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Users use Android-based applications for communicating through emailing, text messaging, and transmission of audio and video objects. The attackers manipulate the email, text, videos, or audio so that users' receipt of the messages causes malware through their handheld devices. A runtime routine is invoked, which causes damage to the local resources of the mobile phone. The manipulation of the messages is done using different signatures, making it difficult to recognize the same using a single approach. Multiple approaches are sometimes required to detect different signature-based incoming messages. Choosing a method that suits the signature of the incoming message is the key. Malware can also enter at the time of installing third-party apps, clicking on the links provided in the messages, installing and invoking the malware in the background. Many issues are involved in dealing with malware detecting, prevention, and curing. A comprehensive architecture is required to deal with every aspect of dealing malware. In this paper, a comprehensive architecture is presented that considers malware's issue, especially concerning malware affected through short message service (SMS) messages operated under the Android operating system. The disection of the SMS messages have been implemented and 99% accuracy has been achieved.

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Malware Detection Techniques for Mobile Devices

Abstract: ABSTRACT

Cited by 20 publications

References 3 publications

Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation

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

Protecting Android based applications from malware affected through SMS messages

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