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

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

Deep Learning Models for Detecting Malware Attacks

Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Deep learning (DL) is one of the emerging and promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on the Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are presented. We also present feature extraction approaches and a review of recent DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on malware detection including future directions to further advance knowledge and research in this field.Comment: Revised figures 2 and 3, revised title, remove typos page 1

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41