Analysis of Anubis Trojan Attack on Android Banking Application Using Mobile Security Labware

Riadi, Imam; Sunardi, Sunardi; Aprilliansyah, Deco

doi:10.18280/ijsse.130104

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Through a controlled mobile security environment, the malware's behavior was simulated and analyzed, showing how it could access and operate discreetly. This quantitative analysis provided a detailed insight into the malware's capabilities, contributing to knowledge on Android device security and the importance of caution when downloading applications [12].…”

Section: Of 31mentioning

confidence: 99%

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Melendez,

Noriega,

Tiznado

et al. 2024

Preprint

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This research aims to introduce a model, SectraBank, designed to mitigate cyber fraud attacks, specifically targeting SIM Swapping and Fake App schemes in mobile banking users. A quantitative methodology with an experimental design was meticulously employed to examine the rise of cyber fraud within mobile banking. The study involved a sample of 100 professionals with advanced knowledge in Information and Communication Technologies (ICTs). Surveys were utilized to measure awareness of threats and perceptions of security before and after the implementation of the proposed model. SectraBank leverages unique user identifiers such as fingerprint authentication and the International Mobile Equipment Identity (IMEI) number, in addition to geolocation and One Time Passwords (OTP) for validating electronic transactions in mobile banking. The effectiveness of the proposed model was notable, achieving a 96.1% efficacy rate in mitigating Fake App attacks and a 90.2% success rate in countering SIM Swapping attacks. These findings underscore the potential of SectraBank as a robust solution to enhance cybersecurity measures in the realm of mobile banking.

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Section: Of 31mentioning

confidence: 99%

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Melendez,

Noriega,

Tiznado

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Children are at a higher risk of being tricked since many harmful programs appear to be safe applications with positive reviews [9]. Thus, using trusted sources to download the application, frequently updating software, and ensuring that security is enabled can enhance the security of Android devices to avoid malware [10].…”

Section: Introductionmentioning

confidence: 99%

Android Malware Classification Using Gain Ratio and Ensembled Machine Learning

Ansori,

Slamet,

Ghufron

et al. 2024

IJSSE

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Recently, the number of Android users has significantly increased, which has made Android a target for attackers to launch their malicious activities. Malware or malicious code is often embedded in Android apps to gain access to the user's device and retrieve personal data. Researchers have explored various approaches to mitigate the spread of Android malware. Besides, the Android malware dataset has huge dimensions with hundreds of features. Choosing the proper feature selection method is one of the challenges for producing a reliable detection model. This paper proposes an approach to detecting Android malware and classifying it into five categories using gain ratio feature selection and an ensemble machine learning algorithm. Features are reduced based on their importance value through the gain ratio calculation method. Then, features that are considered necessary are included in a classification process that combines many models. Experiment using the CICMalDroid2020 (Canadian Institute for Cybersecurity Malware of Android 2020) dataset shows that the proposed approach can improve detection performance. Gain ratio feature selection improves the detection accuracy in several machine learning classification algorithms, 2.59% in Naï ve Bayes, 0.90% in 𝑘-Nearest Neighbor, and 2.29% in Support Vector Machine. Thus, the ensembled machine learning models of Random Forest, Extra Tree, and k-Nearest Neighbors achieved the highest performance, with an accuracy of 94.57% and a precision score of 94.71%.

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Penetration Testing to Investigate Security Vulnerabilities, Bugs and Potential Threats in Flip Kart, JioMart, and Amazon Mobile Application

Kumar,

Belay,

Hadush

et al. 2023

2023 International Conference on Computer Science and Emerging Technologies (CSET)

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Analysis of Anubis Trojan Attack on Android Banking Application Using Mobile Security Labware

Cited by 4 publications

References 26 publications

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

Android Malware Classification Using Gain Ratio and Ensembled Machine Learning

Penetration Testing to Investigate Security Vulnerabilities, Bugs and Potential Threats in Flip Kart, JioMart, and Amazon Mobile Application

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