Keyvan Ansari

In the rapidly evolving landscape of cybersecurity threats, ransomware represents a significant challenge. Attackers increasingly employ sophisticated encryption methods, such as entropy reduction through Base64 encoding, and partial or intermittent encryption to evade traditional detection methods. This study explores the dynamic battle between adversaries who continuously refine encryption strategies and defenders developing advanced countermeasures to protect vulnerable data. We investigate the application of online incremental machine learning algorithms designed to predict file encryption activities despite adversaries evolving obfuscation techniques. Our analysis utilizes an extensive dataset of 32.6 GB, comprising 11,928 files across multiple formats, including Microsoft Word documents (doc), PowerPoint presentations (ppt), Excel spreadsheets (xlsx), image formats (jpg, jpeg, png, tif, gif), PDFs (pdf), audio (mp3), and video (mp4) files. These files were encrypted by 75 distinct ransomware families, facilitating a robust empirical evaluation of machine learning classifiers effectiveness against diverse encryption tactics. Results highlight the Hoeffding Tree algorithms superior incremental learning capability, particularly effective in detecting traditional and AES-Base64 encryption methods employed to lower entropy. Conversely, the Random Forest classifier with warm-start functionality excels at identifying intermittent encryption methods, demonstrating the necessity of tailored machine learning solutions to counter sophisticated ransomware strategies.

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. The integration of these technologies enables the creation of a decentralized vehicular network. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Initial experiments show that compared to conventional decentralized federated learning techniques, our proposed approach significantly enhances the performance and security of vehicular networks. The system’s accuracy stands at 91.92%. While this may appear to be low in comparison to state-of-the-art federated learning models, our work is noteworthy because, unlike others, it was achieved in a malicious vehicle setting. Despite the challenging environment, our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks