Biblio

This article is focused on industrial networks and their security. An industrial network typically works with older devices that do not provide security at the level of today's requirements. Even protocols often do not support security at a sufficient level. It is necessary to deal with these security issues due to digitization. It is therefore required to provide other techniques that will help with security. For this reason, it is possible to deploy additional elements that will provide additional security and ensure the monitoring of the network, such as the Intrusion Detection System. These systems recognize identified signatures and anomalies. Methods of detecting security incidents by detecting anomalies in network traffic are described. The proposed methods are focused on detecting DoS attacks in the industrial Modbus protocol and operations performed outside the standard interval in the Distributed Network Protocol 3. The functionality of the performed methods is tested in the IDS system Zeek.

the more (IoT) scales up with promises, the more security issues raise to the surface and must be tackled down. IoT is very vulnerable against DoS attacks. In this paper, we propose a hybrid design of signature-based IDS and anomaly-based IDS. The proposed hybrid design intends to enhance the intrusion detection and prevention systems (IDPS) to detect any DoS attack at early stages by classifying the network packets based on user behavior. Simulation results prove successful detection of DoS attack at earlier stages.

The use of the Internet of Things (IoT) devices has surged in recent years. However, due to the lack of substantial security, IoT devices are vulnerable to cyber-attacks like Denial-of-Service (DoS) attacks. Most of the current security solutions are either computationally expensive or unscalable as they require known attack signatures or full packet inspection. In this paper, we introduce a novel Graph-based Outlier Detection in Internet of Things (GODIT) approach that (i) represents smart home IoT traffic as a real-time graph stream, (ii) efficiently processes graph data, and (iii) detects DoS attack in real-time. The experimental results on real-world data collected from IoT-equipped smart home show that GODIT is more effective than the traditional machine learning approaches, and is able to outperform current graph-stream anomaly detection approaches.