用于灵巧家庭攻击检测的可解释和最优配置的人工神经网络
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes
摘要：
近年来，网络安全已成为智能应用程序适应的主要问题。特别地，在使用大量IoT设备的灵巧家庭中，具有安全和可信的机制可以为用户提供安心。准确检测网络攻击至关重要，但如果设计保护系统的对策，准确识别攻击类型则起着巨大的作用。人工神经网络（ANN）为智能应用程序检测任何安全攻击提供了有前途的结果。然而，由于用于该技术的模型的复杂性，普通用户不容易信任基于ANN的安全解决方案。此外，为ANN架构选择正确的超参数在准确检测安全攻击方面起着至关重要的作用，特别是在识别攻击的子类别时。在本文中，我们提出了一个模型，考虑了人工神经网络模型的可解释性和超参数选择的问题，这种方法可以很容易地信任和适应用户的灵巧家庭应用程序。此外，我们的方法考虑了一个子集的数据集的超参数的最佳选择，以减少人工神经网络架构设计过程中的开销。与众不同的是，本文侧重于配置，性能和评估的人工神经网络体系结构识别五类攻击和九个子类攻击。使用最近的物联网数据集，我们的方法显示出入侵检测的高性能，二进制，类别和子类级别的攻击分类准确率分别为99.9%，99.7%和97.7%。
英文摘要：
In recent years cybersecurity has become a major concern in adaptation of smart applications. Specially, in smart homes where a large number of IoT devices are used having a secure and trusted mechanisms can provide peace of mind for users. Accurate detection of cyber attacks is crucial, however precise identification of the type of attacks plays a huge role if devising the countermeasure for protecting the system. Artificial Neural Networks (ANN) have provided promising results for detecting any security attacks for smart applications. However, due to complex nature of the model used for this technique it is not easy for normal users to trust ANN based security solutions. Also, selection of right hyperparameters for ANN architecture plays a crucial role in the accurate detection of security attacks, especially when it come to identifying the subcategories of attacks. In this paper, we propose a model that considers both the issues of explainability of ANN model and the hyperparameter selection for this approach to be easily trusted and adapted by users of smart home applications. Also, our approach considers a subset of the dataset for optimal selection of hyperparamters to reduce the overhead of the process of ANN architecture design. Distinctively this paper focuses on configuration, performance and evaluation of ANN architecture for identification of five categorical attacks and nine subcategorical attacks. Using a very recent IoT dataset our approach showed high performance for intrusion detection with 99.9%, 99.7%, and 97.7% accuracy for Binary, Category, and Subcategory level classification of attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
