Browsing by Author "Chukwu, I.J."
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Article Citation - Scopus: 1Algorithm for Key Transparency With Transparent Logs(F1000 Research Ltd, 2024) Mollakuqe, E.; Rexhepi, S.; Bunjaku, R.; Dag, H.; Chukwu, I.J.Background: Cryptography plays a crucial role in securing digital communications and data storage. This study evaluates the Transparent Key Management Algorithm utilizing Merkle trees, focusing on its performance and security effectiveness in cryptographic key handling. Methods: The research employs simulated experiments to systematically measure and analyze key operational metrics such as insertion and verification times. Synthetic datasets are used to mimic diverse operational conditions, ensuring rigorous evaluation under varying workloads and security threats. Implementation is carried out using R programming, integrating cryptographic functions and Merkle tree structures for integrity verification. Results: Performance analysis reveals efficient insertion and verification times under normal conditions, essential for operational workflows. Security evaluations demonstrate the algorithm's robustness against tampering, with approximately 95% of keys verified successfully and effective detection of unauthorized modifications. Simulated attack scenarios underscore its resilience in mitigating security threats. Conclusions: The Transparent Key Management Algorithm, enhanced by Merkle trees and cryptographic hashing techniques, proves effective in ensuring data integrity, security, and operational efficiency. Recommendations include continuous monitoring and adaptive algorithms to bolster resilience against evolving cybersecurity challenges, promoting trust and reliability in cryptographic operations. Copyright: © 2024 Mollakuqe E et al.Article Citation - Scopus: 0Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques(St. Petersburg Federal Research Center of the Russian Academy of Sciences, 2025) Imamverdiyev, Y.; Baghirov, E.; Chukwu, I.J.In the internet and smart devices era, malware detection has become crucial for system security. Obfuscated malware poses significant risks to various platforms, including computers, mobile devices, and IoT devices, by evading advanced security solutions. Traditional heuristic-based and signature-based methods often fail against these threats. Therefore, a cost-effective detection system was proposed using memory dump analysis and ensemble learning techniques. Utilizing the CIC-MalMem-2022 dataset, the effectiveness of decision trees, gradient-boosted trees, logistic Regression, random forest, and LightGBM in identifying obfuscated malware was evaluated. The study demonstrated the superiority of ensemble learning techniques in enhancing detection accuracy and robustness. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to elucidate model predictions, improving transparency and trustworthiness. The analysis revealed vital features significantly impacting malware detection, such as process services, active services, file handles, registry keys, and callback functions. These insights are crucial for refining detection strategies and enhancing model performance. The findings contribute to cybersecurity efforts by comprehensively assessing machine learning algorithms for obfuscated malware detection through memory analysis. This paper offers valuable insights for future research and advancements in malware detection, paving the way for more robust and effective cybersecurity solutions in the face of evolving and sophisticated malware threats. © 2025 St. Petersburg Federal Research Center of the Russian Academy of Sciences. All rights reserved.