基于深度学习与遗传算法的IoT环境多层次威胁溯源方法

A multi-level threats traceability method for IoT environment based on deep learning and genetic algorithm

摘要:

常规物联网(IoT)环境的多层次威胁溯源主要采用网络数据关系分类实现，忽略了常规数据与威胁数据之间的相似性，导致溯源结果误报数较多。对此，提出基于深度学习与遗传算法的物联网环境多层次威胁溯源方法。建立深度学习神经网络对IoT环境威胁数据进行识别，添加批量标准化操作将常规数据与威胁数据分离，提取多层次威胁数据特征，应用遗传算法得到最优个体，实现威胁数据初始节点的溯源定位。实验结果表明，应用所提方法得出溯源结果误报数较少，溯源结果较为准确，满足IoT环境安全维护的现实需求。

Abstract:

The multi-level threats attribution in conventional Internet of Things(IoT) environments is mainly achieved through the classification of network data relationships, which overlooks the similarity between conventional data and threat data, leading to a large number of false positives in the attribution results. In response to this, a multi-level threats attribution method for IoT environments based on deep learning and genetic algorithms is proposed. A deep learning neural network is established to identify threat data in the IoT environment, and batch normalization operations are added to separate conventional data from threat data, extracting features of multi-level threats data. Genetic algorithms are applied to obtain the optimal individual, achieving the initial node attribution and positioning of threat data. Experimental results show that the attribution results obtained using the proposed method have fewer false positives and are more accurate, meeting the practical needs for the security maintenance of IoT environments.