IoT malware detection using a novel 3-Sigma Auto-Funnel Transformer approach

Abstract

The proliferation of Internet of Things (IoT) devices has ushered in a new era of connected technologies, but it has also brought significant security challenges, particularly in the area of malware detection. This research paper presents a novel approach, the “3 Sigma Auto Funnel Transformer,” that designed to address the specific complexities of malware detection in IoT devices. By leveraging advanced deep learning techniques and a multi-layered architecture, the proposed framework provides an innovative solution to detect and mitigate malware threats in IoT ecosystems. By combining the precision of the ”3 Sigma” approach with the efficiency of an ”Auto Funnel Transformer,” this architecture achieves superior detection accuracy and performance. Through comprehensive evaluations, this paper demonstrates the effectiveness of the proposed system in bolstering the security of IoT devices, thereby contributing to the ongoing efforts to protect these essential components of our interconnected world.