Android Ransomware Detection Toolkit

Arora, Lakshay; Kumar, Kshitiz

doi:10.1109/aist55798.2022.10064946

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…The experimentation showed that static analysis have approximately a 40-55% detection accuracy regarding 100% of dynamic analyses. Also as a hybrid approach, Arora and Kumar combined static with dynamic detection to introduce a ransomware detection toolkit in [38]. The static features (permissions and APIs) were passed through an artificial neural network, while the dynamic ones (network traffic) were passed through an LGBM classifier to detect ransomware on the network.…”

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

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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.

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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

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“…In [5], Android application and network layer features were employed in ML models, resulting in detection rates of 99 and 81%, respectively. In [6], static and dynamic artifacts were utilized to classify Android applications and address the challenge of ransomware, surpassing the existing solutions. Meanwhile, other studies' proposed models and methods ranging from system permission features to ensemble ML approaches, achieving accuracies between 94% and 99% [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Advanced Android Malware Detection through Deep Learning Optimization

Alhussen

2024

Eng. Technol. Appl. Sci. Res.

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Android stands out as one of the most prevalent mobile operating systems globally, due to its widespread adoption and open-source nature. However, its susceptibility to malware attacks, facilitated by the ability to install third-party applications without centralized control, poses significant security challenges. Despite efforts to integrate security measures, the proliferation of malicious activities and vulnerabilities emphasizes the need for advanced detection techniques. This study implemented and optimized Long Short-Term Memory (LSTM) and Neural Network (NN) models for malware detection on the Android platform. Leveraging meticulous hyperparameter tuning and robust data preprocessing techniques, this study aimed to increase the efficacy of LSTM and NN models in identifying and mitigating various forms of malware. The results demonstrate remarkable performance, with the LSTM model achieving an accuracy of 99.24%, precision of 99.07%, recall of 98.79%, and F1-score of 98.93%, and the NN model attaining an accuracy of 99.18%, precision of 99.02%, recall of 98.84%, and F1-score of 98.93%. By addressing these challenges and achieving such high levels of accuracy and effectiveness, this study contributes significantly to the ongoing endeavor to fortify defenses against cyber threats, thus fostering a safer digital environment for users worldwide.

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Android Ransomware Detection Toolkit

Cited by 2 publications

References 23 publications

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

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

Advanced Android Malware Detection through Deep Learning Optimization

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