Detection and Visualization of Android Malware Behavior

Somarriba, Oscar; Zurutuza, Urko; Uribeetxeberria, Roberto; Delosières, Laurent; Nadjm‐Tehrani, Simin

doi:10.1155/2016/8034967

Cited by 28 publications

(24 citation statements)

References 15 publications

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“…There is no doubt about having many types of malware as confirmed by many PSs [50]. In addition, malware families may shows diverse actions in their lifetime extend from sheer upsetting to highly malicious.…”

Section: Malware Typesmentioning

confidence: 99%

Malware Visualization Techniques

Efe¹,

Hussin²

2020

International Journal of Applied Mathematics Electronics and Computers

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Article history:Malware basically means malicious software that can be an intrusive program code or anything that is designed to perform malicious operations on system and executes malicious actions such as clandestine, listening, monitoring, saving, and deleting without the user's knowledge and consent. Malware review and analysis requires an advanced level of programming knowledge, in-depth file systems knowledge, deep code inspection, and reverse engineering capability. New techniques are needed to reduce indirect costs of malware analysis. This paper aims to provide insights into the malware visualization techniques and its applications, most common malware types and the extracted features that used to identify the malware are demonstrated in this study. In this work, Systematic Literature Review (SLR) conducted to investigate the current state of knowledge about Malware detection techniques, data visualization and malware features. An advanced research has been carried out in most relevant digital libraries for potential published articles. 90 preliminary studies (PS) were determined on the basis of inclusion and exclusion criteria. The analytical study is based mainly on the PSs to achieve the goals. The results clarify the importance of visualization techniques and which are the most common malware as well as the most useful features. Several ways to visualize malware to help malware analysts have been suggested.This is an open access article under the CC BY-SA 4.0 license.(https://creativecommons.org/licenses/by-sa/4.0/)

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Section: Malware Typesmentioning

confidence: 99%

Malware Visualization Techniques

Efe¹,

Hussin²

2020

International Journal of Applied Mathematics Electronics and Computers

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“…In this case, entropic techniques are very useful [24]. (ii) Dynamic analysis, instead, relies on the execution of the applications [25,26]. e main idea is to collect (at runtime) the values that gradually come out from the called instructions.…”

Section: Different Approaches To Analyze Applicationsmentioning

confidence: 99%

The Dangers of Rooting: Data Leakage Detection in Android Applications

Casati

Visconti

2018

Mobile Information Systems

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Mobile devices are widely spread all over the world, and Android is the most popular operative system in use. According to Kaspersky Lab's threat statistic (June 2017), many users are tempted to root their mobile devices to get an unrestricted access to the file system, to install different versions of the operating system, to improve performance, and so on. e result is that unintended data leakage flaws may exist. In this paper, we (i) analyze the security issues of several applications considered relevant in terms of handling user sensitive information, for example, financial, social, and communication applications, showing that 51.6% of the tested applications suffer at least of an issue and (ii) show how an attacker might retrieve a user access token stored inside the device thus exposing users to a possible identity violation. Notice that such a token, and a number of other sensitive information, can be stolen by malicious users through a man-in-the-middle (MITM) attack.

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“…Static detection framework is beneficial in efficiency, granularity, layers, as well as correctness [23]. Sandbox an emulator records and analyzes the app's behavior used to modify the short message (SM) sending function of the emulator, in order to record the SM content and receiver when the function is performed [24].…”

Section: Static Analysismentioning

confidence: 99%

A Survey on Android Malware Detection Techniques

Riasat¹,

Sakeena²,

Wang³

et al. 2017

dtcse

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Abstract. Now-a-days widely applicable smart phone application adoption and rapid growth of contextually sensitive nature of smartphone devices has led to a renaissance in mobile application services and increased concerns over smartphone malware. Most of the smart phone users are connected to internet for various purposes. Even after the advanced technology used in the smartphones still the users are remaining unprotected from malware attacks. To overcome these issues and to keep the smartphones safe a number of malicious applications, malware threat and adwares for mobile phones is expected to increase with the functionality enhancement of smart mobile phone. Android Operating Systems are most commonly used systems in the smartphones. Many applications are available in android play store and it is very difficult to distinguish and to discriminate between clean and malicious applications. We have briefly discussed different methods for detecting android malwares in this review and present a clear picture about these detection tools in the smartphone applications.

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Detection and Visualization of Android Malware Behavior

Cited by 28 publications

References 15 publications

Malware Visualization Techniques

Malware Visualization Techniques

The Dangers of Rooting: Data Leakage Detection in Android Applications

A Survey on Android Malware Detection Techniques

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