Securing IoT Devices Running PureOS from Ransomware Attacks: Leveraging Hybrid Machine Learning Techniques

Abstract

Internet-enabled (IoT) devices are typically small, low-powered devices used for sensing and computing that enable remote monitoring and control of various environments through the Internet. Despite their usefulness in achieving a more connected cyber-physical world, these devices are vulnerable to ransomware attacks due to their limited resources and connectivity. To combat these threats, machine learning (ML) can be leveraged to identify and prevent ransomware attacks on IoT devices before they can cause significant damage. In this research paper, we explore the use of ML techniques to enhance ransomware defense in IoT devices running on the PureOS operating system. We have developed a ransomware detection framework using machine learning, which combines the XGBoost and ElasticNet algorithms in a hybrid approach. The design and implementation of our framework are based on the evaluation of various existing machine learning techniques. Our approach was tested using a dataset of real-world ransomware attacks on IoT devices and achieved high accuracy (90%) and low false-positive rates, demonstrating its effectiveness in detecting and preventing ransomware attacks on IoT devices running PureOS.

LLaMa Assisted Reverse Engineering of Modern Ransomware: A Comparative Analysis with Early Crypto-Ransomware

“…The development of advanced tools and methodologies that can detect subtle anomalies and patterns indicative of ransomware activity is now a necessity [14,21]. This study serves as a call to action for cybersecurity professionals and researchers to rethink and retool their strategies in the face of this evolving digital menace [15,37,7].…”

“…This necessitates the development of more advanced tools and methodologies capable of analyzing increasingly complex ransomware samples [21]. The integration of machine learning techniques in static analysis has emerged as a promising avenue, offering the potential to automate and enhance the detection of ransomware variants [15,9]. Static analysis remains a critical tool in the arsenal against ransomware [2,8].…”

“…By understanding the evolving tactics of ransomware, cybersecurity professionals are better equipped to identify potential vulnerabilities within their systems and implement safeguards that are specifically tailored to counteract these sophisticated threats [14,21]. This approach is vital in an era where ransomware attacks are not only increasing in frequency but also in sophistication [15,37,21,3,16].…”

“…For instance, in 2022, ransomware was involved in 25% of all breaches, and 66% of organizations reported being affected by ransomware, marking a 78% increase from 2020, whereas the FBI received 3,729 complaints about ransomware attacks in 2021, accounting for financial losses of $49.2 million [11,3,12,13]. This evolving threat landscape, marked by sophisticated ransomware variants that emphasize stealth over brute-force encryption, requires a deep and nuanced understanding of their operational mechanisms [14,15].…”

“…In the context of ransomware, reverse engineering plays a pivotal role not only in understanding the workings of these malicious programs but also in forecasting their evolutionary trajectories [1,14]. The process involves a multifaceted approach where security researchers must be adept in areas such as assembly language and computer architecture [16,15]. Understanding these foundational elements is crucial, as they provide insights into the malware's potential methods of attack and exploitation [7,8].…”