Detecting GPS information leakage in Android applications

Ma, Siyuan; Tang, Zhushou; Xiao, Qiuyu; Liu, Jiafa; Duong, Tran Triet; Lin, Xiaodong; Zhu, Haojin

doi:10.1109/glocom.2013.6831175

Cited by 5 publications

(1 citation statement)

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“…GPS EXPLOITATION Not many researchers had focused on GPS exploitation in Android mobile OS. Ma et al [32] investigated the location information leakage in Android and proposed a new tool namely, Brox to identify a potential information leakage path in android malicious applications. While Vanjire et al [33] developed an Android Application based on location-based system (LBS) that offered varied location-based services, such as changing of mobile profile from normal to silent mode and vice versa, for certain places where the users had registered.…”

Section: Code Analysismentioning

confidence: 99%

A Systematic Review Analysis for Mobile Botnet Detection using GPS Exploitation

Yusof¹,

Saudi²,

Ridzuan³

2018

MJoSHT

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At present, mobile botnet has become a cyber threat for smartphone users especially on the Android platform. It has the capabilities to exploit the vulnerabilities and steal confidential information in the victim’s smartphone. Zeus, DroidDream and MisoSMS are examples of mobile botnets that have affected thousands of users worldwide. Therefore, this research paper presents a systematic review analysis on the existing techniques for mobile botnet detection techniques. It discusses the strengths and the weaknesses of the existing mobile botnet detection techniques and related works of mobile botnet that exploit GPS. This research paper can be used as a reference and guidance for those with the same interest.

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Section: Code Analysismentioning

confidence: 99%

A Systematic Review Analysis for Mobile Botnet Detection using GPS Exploitation

Yusof¹,

Saudi²,

Ridzuan³

2018

MJoSHT

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Maloid-DS: Labeled Dataset for Android Malware Forensics

Almomani,

Almashat,

El-Shafai

2024

IEEE Access

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Billions of people globally use Android devices a . As such, these devices are highly targeted by security attackers. One of the most threatening attacks is to infect devices with malicious software (malware). Fortunately, there are various ways to counteract these attacks and prevent them. One of these methods is developing a comprehensive malware dataset that researchers can utilize for malware analysis, detection, prediction, and prevention systems. This paper introduces a unique, up-to-date, labeled Android malware dataset (Maloid-DS) comprising a comprehensive set of malware families that reached 345 families with 47,971 malware samples. First, we intensely studied existing datasets utilized by previous research works. These datasets are limited in (a) the number of studied families, (b) the number of samples under each family, (c) the number of new malware samples, (d) the proper categorization of the malware families, (e) the accurate mapping of the sample with its corresponding malware family, (f) providing well structuring of the malware families and subfamilies, and (g) presenting a profound description of each family behavior. All these limitations were seriously tackled by introducing Maloid-DS. The process of creating Maloid is detailed in this paper. Moreover, several case studies are demonstrated in this paper to show the value of Maloid and how different types of analysis systems and AI-based detection and prediction solutions could utilize it. While the full potential of Maloid-DS in real-world scenarios is subject to ongoing research and practical application, it represents a substantial contribution to the cybersecurity community, offering a broad and detailed foundation for protecting Android devices against malware threats.

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