论文标题
基于深度学习的方法，用于检测和表征物联网设备中的网络安全事件
A Method Based on Deep Learning for the Detection and Characterization of Cybersecurity Incidents in Internet of Things Devices
论文作者
论文摘要
鉴于物联网网络的增长及其在人类活动的关键方面的存在，与这些网络相关的设备的安全变得至关重要。机器学习方法由于能够处理流量信息以检测可能代表针对基础架构的攻击的异常模式而成为计算机网络中安全解决方案的推动力。在本文中，我们建议利用卷积和经常性的神经网络，这是在图像处理等上下文中成功使用的两个文物，用于开发用于物联网的安全解决方案。我们的结果表明，在使用最先进的数据集进行评估时，在攻击二进制分类（即正常流量与攻击流量）中，可获得99％的准确性，而多类分类（识别不同类型的攻击）精度为96％。这些结果的表现优于文献中可用的建议，展示了开发用于物联网基础设施的安全解决方案的有希望的景观。
Given the increased growing of Internet of Things networks and their presence in critical aspects of human activities, the security of devices connected to these networks becomes critical. Machine Learning approaches are becoming prominent as enablers for security solutions in computer networks due to its capacity to process traffic information in order to detect abnormal patterns which might represent attacks targeting infrastructures. In this paper, we propose to leverage Convolutional and Recurrent Neural Networks, two artifacts that have been successfully used in contexts such as image processing for pattern recognition, for the development of a security solution to be used in the context of Internet of Things. Our results show that this approach, when evaluated with a state-of-the-art data set, achieves around 99% of accuracy in the binary classification of attacks (i.e. normal traffic vs attack traffic) and 96% for multiclass classification (recognition of different types of attacks) accuracy. These results outperform proposals available in literature, showing a promising landscape for developing security solutions for IoT infrastructures.
