论文标题
对关键基础设施中基于AI的入侵检测技术的比较研究
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures
论文作者
论文摘要
志愿者计算使用与Internet连接的设备（笔记本电脑，PC，智能设备等），其所有者自愿将其作为存储和计算电源资源，已成为许多应用程序中资源管理的重要机制。互联网中数据流量的数量和种类的增长导致人们对网络物理系统的鲁棒性，尤其是对于关键基础设施的鲁棒性。因此，用于收集此类感觉数据的有效入侵检测系统的实施至关重要。在本文中，我们介绍了人工智能（AI）驱动的入侵检测系统的比较研究，用于跟踪关键应用的无线连接传感器。具体而言，我们对机器学习，深度学习和强化学习解决方案的使用进行了深入分析，以识别收集到的流量中的侵入性行为。我们通过在模拟中使用KD'99作为实际攻击数据集来评估所提出的机制。结果介绍了三种不同IDS的性能指标，即自适应监督和聚类的混合ID（ASCH-IDS），受限的基于Boltzmann机器的群集IDS（RBC-IDS）和基于Q研究的IDS和基于Q的IDS（QL-IDS），以发现恶意行为。我们还介绍了不同的强化学习技术的性能，例如国家行动 - 奖励 - 状态学习（SARSA）和时间差异学习（TD）。通过模拟，我们表明QL-IDS以100％的检测率执行，而SARSA-IDS和TD-IDS则以99.5％的速度执行。
Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic in the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this paper, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognize intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KD'99 as real attack data-set in our simulations. Results present the performance metrics for three different IDSs namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS) and Q-learning based IDS (QL-IDS) to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that QL-IDS performs with 100% detection rate while SARSA-IDS and TD-IDS perform at the order of 99.5%.
