Detection of Android Ransomware Using Machine Learning Approach

Jose, Anoop; Priyadharsini, C.; Praise, Paradise Mercy; Kathrine, G. Jaspher W.; Andrew, J.

doi:10.1007/978-981-99-2264-2_16

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar work is one by Bagui and Woods [28], where the algorithms considered were Decision Tree, Naïve Bayes, and OneR. Again, Jose et al analyzed in [49] various machine-learning algorithms combining RansomDroid and concept drift in the classification of raw data considering host, network, behavior, and files.…”

Section: Background Of Ransomware Detection For Android Platformsmentioning

confidence: 99%

Lightweight Crypto-Ransomware Detection in Android Based on Reactive Honeyfile Monitoring

Gómez-Hernández,

García-Teodoro

2024

Sensors

View full text Add to dashboard Cite

Given the high relevance and impact of ransomware in companies, organizations, and individuals around the world, coupled with the widespread adoption of mobile and IoT-related devices for both personal and professional use, the development of effective and efficient ransomware mitigation schemes is a necessity nowadays. Although a number of proposals are available in the literature in this line, most of them rely on machine-learning schemes that usually involve high computational cost and resource consumption. Since current personal devices are small and limited in capacities and resources, the mentioned schemes are generally not feasible and usable in practical environments. Based on a honeyfile detection solution previously introduced by the authors for Linux and Window OSs, this paper presents a ransomware detection tool for Android platforms where the use of trap files is combined with a reactive monitoring scheme, with three main characteristics: (i) the trap files are properly deployed around the target file system, (ii) the FileObserver service is used to early alert events that access the traps following certain suspicious sequences, and (iii) the experimental results show high performance of the solution in terms of detection accuracy and efficiency.

show abstract

Section: Background Of Ransomware Detection For Android Platformsmentioning

confidence: 99%