RAPID-Risk Assessment of Android Permission and Application Programming Interface (API) Call for Android Botnet

Abdullah, Zubaile; Saudi, Madihah Mohd

doi:10.14419/ijet.v7i4.15.21370

Cited by 4 publications

(4 citation statements)

References 22 publications

(21 reference statements)

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“…However, both papers do not include more recent studies and publications (all are pre-2014). Covers the same points as aforementioned papers, but also includes more recent research from 2014-2021 such as [18,19] and more. [20] Presents potential challenges of mobile botnets, but does not include any more recent research (paper from 2012).…”

Section: Paper Main Contribution and Reference Metricsmentioning

confidence: 99%

“…[197] Another point of interest in botnet detection is the detection of smartphone-based botnets. Some papers, such as Abdullah and Saudi [18], propose assessing the potential risk of malicious apps by evaluating the API calls used by given apps. Apps shown to behave more like botnets are categorised as higher-risk and might potentially be blocked.…”

Section: Technique Advantage(s) Disadvantage(s) Detection Rate Papersmentioning

confidence: 99%

“…Apps shown to behave more like botnets are categorised as higher-risk and might potentially be blocked. Reference [18] also attempts to evaluate apps based on risk factors, weighing botnetbehaving apps as higher-risk compared to more benign apps. Reference [19] compares an app's permissions with a list of known harmful permissions and creates a threat level hierarchy on the basis of said permissions.…”

Section: Technique Advantage(s) Disadvantage(s) Detection Rate Papersmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Stege

El-Habr

et al. 2021

Future Internet

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Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

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Section: Paper Main Contribution and Reference Metricsmentioning

confidence: 99%

Section: Technique Advantage(s) Disadvantage(s) Detection Rate Papersmentioning

confidence: 99%

Section: Technique Advantage(s) Disadvantage(s) Detection Rate Papersmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Stege

El-Habr

et al. 2021

Future Internet

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“…The most relevant research was conducted by Abdullah and M. Saudi (Abdullah & Mohd Saudi, 2017) and it was a study on Android botnet detection by using risk assessment method based on permission and API features. They applied the best 20 permission and API calls to compute with the risk level score based on privacy, financial and system to differentiate between malware and benign.…”

Section: Related Workmentioning

confidence: 99%

Android Botnet Detection Using Risk Assessment

Yusof¹,

Saudiand²,

Ridzuan³

2018

Proceedings of the 8th International Conference on Research in Engineering, Science and Technology

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The increasing popularity of Android mobile phones in recent years has attracted the attention of malware developers. Android applications (apps) pose many risks/threats to the user's privacy and system integrity. Currently, permission-based models are used in the Android systems to detect the dangerous apps that possess several weaknesses. In this paper, a new risk assessment method is proposed to evaluate the amount of risk associated with every app in terms of privacy risk, financial risk, and smartphones system risk. It focused on the GPS exploitation for Android botnet detection. The assessment was based on static analysis that used features set permission and API calls. The quantitative calculation model was used as a method to differentiate between the benign and botnet apps. Every app was assessed for risk based on five categories such as Very High, High, Medium, Low and Very Low. Two datasets with 2694 Android botnet samples from Drebin and 774 benign apps from Google Play were used to evaluate the effectiveness of this method. The obtained results demonstrate that the proposed method is good in differentiating the Android botnet and benign apps based on the risk level. This will give a promising impression to the users during apps installation.

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A new method for assigning appropriate labels to create a 28 Standard Android Botnet Dataset (28-SABD)

Moodi

Ghazvini

2018

J Ambient Intell Human Comput

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RAPID-Risk Assessment of Android Permission and Application Programming Interface (API) Call for Android Botnet

Cited by 4 publications

References 22 publications

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Android Botnet Detection Using Risk Assessment

A new method for assigning appropriate labels to create a 28 Standard Android Botnet Dataset (28-SABD)

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