论文标题
通过粒子群和深度学习增强网络取证：粒子深框架
Enhancing network forensics with particle swarm and deep learning: The particle deep framework
论文作者
论文摘要
由于它们提供的自动化及其对生产率的影响，物联网智能事物的普及正在上升。但是，已经证明，物联网设备容易受到建立良好和新物联网特定的攻击向量的攻击。在本文中，我们提出了粒子深框架，这是一种用于物联网网络的新网络法医框架，该框架利用粒子群优化来调整深MLP模型的超参数并改善其性能。使用Bot-Iot数据集对PDF进行了训练和验证，Bot-iot数据集是一种现代网络交通数据集，将正常的物联网和非iot流量结合在一起，以及众所周知的与BotNet相关的攻击。通过实验，我们表明，深度MLP模型的性能得到了极大的改善，其准确性为99.9％，虚假警报率接近0％。
The popularity of IoT smart things is rising, due to the automation they provide and its effects on productivity. However, it has been proven that IoT devices are vulnerable to both well established and new IoT-specific attack vectors. In this paper, we propose the Particle Deep Framework, a new network forensic framework for IoT networks that utilised Particle Swarm Optimisation to tune the hyperparameters of a deep MLP model and improve its performance. The PDF is trained and validated using Bot-IoT dataset, a contemporary network-traffic dataset that combines normal IoT and non-IoT traffic, with well known botnet-related attacks. Through experimentation, we show that the performance of a deep MLP model is vastly improved, achieving an accuracy of 99.9% and false alarm rate of close to 0%.
