Biblio

EtherNet/IP is a TCP/IP-based industrial protocol commonly used in industrial control systems (ICS). TCP/IP connectivity to the outside world has enabled ICS operators to implement more agile practices, but it also has exposed these cyber-physical systems to cyber attacks. Using a custom Scapy-based fuzzer to test for implementation flaws in the EtherNet/IP software of commercial programmable logic controllers (PLC), we uncover a previously unreported denial-of-service (DoS) vulnerability in the Ethernet/IP implementation of the Rockwell Automation/Allen-Bradley MicroLogix 1100 PLC that, if exploited, can cause the PLC to fault. ICS-CERT recently announces this vulnerability in the security advisory ICSA-17-138-03. This paper describes this vulnerability, the development of an EtherNet/IP fuzzer, and an approach to remotely monitor for faults generated when fuzzing.

Supervisory Control and Data Acquisition (SCADA) systems complexity and interconnectivity increase in recent years have exposed the SCADA networks to numerous potential vulnerabilities. Several studies have shown that anomaly-based Intrusion Detection Systems (IDS) achieves improved performance to identify unknown or zero-day attacks. In this paper, we propose a hybrid model for anomaly-based intrusion detection in SCADA networks using machine learning approach. In the first part, we present a robust hybrid model for anomaly-based intrusion detection in SCADA networks. Finally, we present a feature selection model for anomaly-based intrusion detection in SCADA networks by removing redundant and irrelevant features. Irrelevant features in the dataset can affect modeling power and reduce predictive accuracy. These models were evaluated using an industrial control system dataset developed at the Distributed Analytics and Security Institute Mississippi State University Starkville, MS, USA. The experimental results show that our proposed model has a key effect in reducing the time and computational complexity and achieved improved accuracy and detection rate. The accuracy of our proposed model was measured as 99.5 % for specific-attack-labeled.

New generations of industrial control systems offer higher performance, they are distributed, and it is very likely that they are internet connected in one way or another. These trends raise new challenges in the contexts of reliability and security. We propose a novel approach that tackles the complexity of industrial control systems at design time and run time. At design time our target is to ease the configuration and verification of controller configurations through model-driven engineering techniques together with the contract-based design paradigm. At run time the information from design time is reused in order to support a modular and distributed self-adaptive software system that aims to increase reliability and security. The industrial setting of the presented approach are control devices for hydropower plant units.

This paper outlines a set of 10 cyber security concerns associated with Industrial Control Systems (ICS). The concerns address software and hardware development, implementation, and maintenance practices, supply chain assurance, the need for cyber forensics in ICS, a lack of awareness and training, and finally, a need for test beds which can be used to address the first 9 cited concerns. The concerns documented in this paper were developed based on the authors' combined experience conducting research in this field for the US Department of Homeland Security, the National Science Foundation, and the Department of Defense. The second half of this paper documents a virtual test bed platform which is offered as a tool to address the concerns listed in the first half of the paper. The paper discusses various types of test beds proposed in literature for ICS research, provides an overview of the virtual test bed platform developed by the authors, and lists future works required to extend the existing test beds to serve as a development platform.

Industrial Control System (ICS) consists of large number of electronic devices connected to field devices to execute the physical processes. Communication network of ICS supports wide range of packet based applications. A growing issue with network security and its impact on ICS have highlighted some fundamental risks to critical infrastructure. To address network security issues for ICS a clear understanding of security specific defensive countermeasures is required. Reconnaissance of ICS network by deep packet inspection (DPI) consists analysis of the contents of the captured packets in order to get accurate measures of process that uses specific countermeasure to create an aggregated posture. In this paper we focus on novel approach by presenting a technique with captured network traffic. This technique is capable to identify the protocols and extract different features for classification of traffic based on network protocol, header information and payload to understand the whole architecture of complex system. Here we have segregated possible types of attacks on ICS.

Due to the fact that the cyber security risks exist in industrial control system, risk assessment on Industrial Automation Platform (IAP) is discussed in this paper. The cyber security assessment model for IAP is built based on relevant standards at abroad. Fuzzy analytic hierarchy process and fuzzy comprehensive evaluation method based on entropy theory are utilized to evaluate the communication links' risk of IAP software. As a result, the risk weight of communication links which have impacts on platform and the risk level of this platform are given for further study on protective strategy. The assessment result shows that the methods used can evaluate this platform efficiently and practically.

Industrial Control Systems (ICS) which among others are comprised of Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) are used to control industrial processes. ICS have now been connected to other Information Technology (IT) systems and have as a result become vulnerable to Advanced Persistent Threats (APT). APTs are targeted attacks that use zero-day attacks to attack systems. Current ICS security mechanisms fail to deter APTs from infiltrating ICS. An analysis of possible solutions to deter APTs was done. This paper proposes the use of Artificial Immune Systems to secure ICS from APTs.