论文标题
物联网的入侵检测系统：边缘计算和机器学习提供的机会和挑战
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning
论文作者
论文摘要
当前网络安全方法的关键组成部分是入侵检测系统（IDS）是不同的技术，并且实现了架构来检测入侵。 IDS可以基于与已知入侵经验（称为基于签名的Intrusion经验的数据库）进行交叉检查的监视事件，或者学习系统的正常行为，并报告是否发生某些异常事件，称为基于异常。这项工作致力于调查ID在物联网（IoT）网络中的应用，在该网络中，边缘计算也用于支持IDS实现。确定在边缘方案中部署ID时会出现的新挑战，并提出了补救措施。我们专注于基于异常的IDS，显示了可以利用的主要技术来检测异常，并且在IDS的背景下，我们提出了机器学习技术及其应用，描述了特定技术可能引起的预期优势和缺点。
Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether some anomalous events occur, named anomaly-based. This work is dedicated to survey the application of IDS to the Internet of Things (IoT) networks, where also the edge computing is used to support the IDS implementation. New challenges that arise when deploying an IDS in an edge scenario are identified and remedies are proposed. We focus on anomaly-based IDSs, showing the main techniques that can be leveraged to detect anomalies and we present machine learning techniques and their application in the context of an IDS, describing the expected advantages and disadvantages that a specific technique could cause.
