Combatting ransomware in ZephyrOS-activated industrial IoT environments

Abstract

The rapid growth of the Industrial Internet of Things (IIoT) has opened up new avenues for cyber threats, with ransomware being a primary area of concern. In response to this, proposed study introduces an innovative approach that combines the strength of the Gradient Boosting Machine (GBM) and the precision of Lasso Regression to effectively identify ransomware threats in IIoT settings. Functioning on the Zephyr operating system, the GBM's ability to handle large-scale datasets and traverse complex data dimensions is complemented by Lasso Regression's skill in curbing overfitting and extracting critical features. This combined ML technique is specifically designed to address the diverse data challenges of IIoT, providing a solid line of defense. Comprehensive tests on updated ransomware tools and the established RanSAP & IoT-23 datasets validated our model's capabilities, achieving an impressive 92 percent detection rate while keeping false positives to a minimum. When compared to existing strategies, projected solution showcased superior performance, highlighting its pivotal role in bolstering IIoT security against ransomware attacks. These results shed light on the next steps for ensuring a safer IIoT landscape, emphasizing the need for advanced, flexible cybersecurity measures in our ever-evolving industrial ecosystem.