Due to its simple installation and connectivity, the Internet of Things (IoT) is susceptible to malware attacks. Being able to operate autonomously. As IoT devices have become more prevalent, they have become the most tempting targets for malware. Weak, guessable, or hard-coded passwords, and a lack of security measures contribute to these vulnerabilities along with insecure network connections and outdated update procedures. To understand IoT malware, current methods and analysis ,using static methods, are ineffective. The field of deep learning has made great strides in recent years due to their tremendous data mining, learning, and expression capabilities, cybersecurity has enjoyed tremendous growth in recent years. As a result, malware analysts will not have to spend as much time analyzing malware. In this paper, we propose a novel detection and analysis method that harnesses the power and simplicity of decision trees. The experiments are conducted using a real word dataset, MaleVis which is a publicly available dataset. Based on the results, we show that our proposed approach outperforms existing state-of-the-art solutions in that it achieves 97.23% precision and 95.89% recall in terms of detection and classification. A specificity of 96.58%, F1-score of 96.40%, an accuracy of 96.43.
翻译:由于其简单的安装和连接, 物端互联网( IoT) 很容易受到恶意攻击。 能够自主操作。 随着IoT 设备越来越普遍, 它们已成为恶意软件最诱人的目标。 薄弱、 可猜测或硬编码的密码, 缺乏安全措施, 以及不安全的网络连接和过时的更新程序, 导致这些弱点。 要理解 IoT 恶意软件, 目前的方法和分析, 使用静态方法, 是无效的。 深层次学习领域近年来由于数据挖掘、 学习和表达能力巨大, 网络安全近年来取得了巨大进步。 结果, 恶意软件分析师不必花太多时间分析恶意软件。 在本文中, 我们提出了一个新的检测和分析方法, 利用决策树的力量和简单化程序。 实验使用真实的词数据集进行, 使用静态方法的分析是无效的。 根据结果, 我们提出的方法在近些年中超越了现有状态的解决方案, 从而实现了96. 23% 的精确度和95. 89% 的精确度。