Explainable Artificial Intelligence-Enabled Android Malware Detection Model for Cybersecurity

Almutairi, Laila

doi:10.1007/978-981-99-3010-4_51

Cited by 1 publication

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparative review of related works regarding ransomware detection and analysis has highlighted several crucial limitations prevalent across various methodologies. A significant challenge exists in the realm of explainability techniques [29], where many approaches struggle to transparently articulate the rationale behind their decisions. Additionally, methodologies often encounter difficulties in ensuring their generalizability across diverse ransomware types [30] and many studies do not disclose the specific feature selection techniques used.…”

Section: Comparative Analysis Of Existing Studiesmentioning

confidence: 99%

“…Furthermore, the struggle to adapt effectively against unknown ransomware variants poses a critical challenge and impacts the overall efficacy of these detection systems. Another area of concern emerging in the current literature is the differentiation or classification accuracy between ransomware and other malware types [29], [30], which can affect the reliability and precision of the detection process. Moreover, many methodologies rely heavily on the quality and comprehensiveness of their training data [31], [32].…”

Section: Comparative Analysis Of Existing Studiesmentioning

confidence: 99%

See 1 more Smart Citation

RFSA: A Ransomware Feature Selection Algorithm for Multivariate Analysis of Malware Behavior in Cryptocurrency

Nkongolo Wa Nkongolo

2024

IJCDS

View full text Add to dashboard Cite

This research introduces innovative features tailored to capture distinctive characteristics of ransomware activity within the cryptocurrency ecosystem. The study employs a multifaceted analysis to delve into ransomware-related data encompassing transaction metadata, ransom analysis, behavioral patterns, and financial aspects. A feature selection algorithm is explored to discern ransomware transactions in Bitcoin (BTC) and the United States Dollar (USD) using the UGRansome dataset. This comprehensive dataset of ransomware-related transactions facilitates the proposal of novel features designed to capture the unique traits of ransomware activity. The correlation matrix and temporal analysis of these features contribute to a nuanced understanding of the dynamic nature of ransomware threats. The research presents the Ransomware Feature Selection Algorithm (RFSA) based on Gini Impurity and Mutual Information (MI) to effectively select crucial ransomware features. Evaluation metrics such as precision, recall, accuracy, and F1 score highlight the effectiveness of the RFSA. The analysis reveals that approximately 68% of ransomware incidents involve BTC transactions ranging from 1.46 to 2.56, with an average of 2.01 BTC transactions per attack. Moreover, ransomware causes financial damages ranging from 4.38 to 172.36 USD, with an average damage of 88.37 USD. The RFSA identifies 17 ransomware types and their associated malware to shed light on their characteristics. The study investigates the pricing of ransomware and reveals that TowerWeb is associated with the highest fee, amounting to 135.26 BTC, while CryptoLocker has the lowest fee, recorded at 10.51 BTC. Additionally, the impact of ransomware duration on financial gains and network flow is investigated, disclosing a correlation between extended duration and higher financial gains. The research achieves outstanding performance metrics, including an MI score of 95%, accuracy of 93%, recall of 92%, and precision of 89%. These results showcase the superiority of the proposed approach over existing studies, emphasizing the dynamic and adaptable nature of ransomware demands. The findings suggest that there is no fixed amount for specific cyberattacks. This underscores the importance of adapting to the evolving landscape of ransomware threats.

show abstract

Section: Comparative Analysis Of Existing Studiesmentioning

confidence: 99%