论文标题
深图像：一种基于珍贵图像的深度学习方法，用于在物联网环境中的在线恶意软件检测
Deep Image: A precious image based deep learning method for online malware detection in IoT Environment
论文作者
论文摘要
恶意软件的数量和物联网设备的攻击数量每天都在增加，这鼓励安全专业人员不断增强其恶意软件分析工具。网络安全领域的研究人员已广泛探讨了复杂分析和恶意软件检测效率的使用。随着新的恶意软件类型和攻击路线的引入，安全专家在开发有效的恶意软件检测和分析解决方案方面面临着巨大的挑战。在本文中，考虑了恶意软件分析的不同观点，并计算了每个样本特征的风险水平，并基于计算该样本的风险水平。这样，引入了一个标准，该标准与物联网环境中的恶意软件分析的准确性和FPR标准一起使用。在本文中，提出了基于可视化技术的三种恶意软件检测方法，称为聚类方法，概率方法和深度学习方法。然后，除了通常的机器学习标准（即准确性和FPR）外，还使用了基于样品风险的拟议标准进行比较，结果表明，深度学习方法在检测恶意软件方面的表现更好
The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Researchers in the field of cyber security have extensively explored the usage of sophisticated analytics and the efficiency of malware detection. With the introduction of new malware kinds and attack routes, security experts confront considerable challenges in developing efficient malware detection and analysis solutions. In this paper, a different view of malware analysis is considered and the risk level of each sample feature is computed, and based on that the risk level of that sample is calculated. In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment. In this paper, three malware detection methods based on visualization techniques called the clustering approach, the probabilistic approach, and the deep learning approach are proposed. Then, in addition to the usual machine learning criteria namely accuracy and FPR, a proposed criterion based on the risk of samples has also been used for comparison, with the results showing that the deep learning approach performed better in detecting malware
