This study investigates the use of explainable artificial intelligence (XAI) to identify the unique features distinguishing malware families and subspecies.
In this work, we propose an efficient visual analytics framework for measuring the uncertainty from different graph sampling methods and quantifying the ...
Color-coded Attribute Graph: Visual Exploration of Distinctive Traits of IoT-Malware Families ... color code and transform them to a fixed-sized encoded image.
Jun 24, 2024 · To achieve fine-grained malicious behavior identification in the lurking stage of IoT malware, we propose MaGraMal. This approach, leveraging ...
Missing: Visual Traits
Nov 11, 2023 · IoT devices face evolving malware threats; researchers continue to explore methods to protect these devices. This research paper introduces an ...
Apr 3, 2024 · This study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios.
Jan 31, 2023 · In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (ie, images) of the network ...
[PDF] MalNet: A binary-centric network-level profiling of IoT Malware
www.cs.ucr.edu › MalNet_IMC2022
Dec 19, 2021 · Overall, we collect and analyze 1447 malware binaries, which seem to cover seven major malware families as we will see in Table 1. We use two ...
A malware family is a group of associated programs with similar attack techniques, some of which have “code overlap” [15] to a large extent. Grouping them as a ...
Missing: Color- | Show results with:Color-