Industrial control systems (ICS) include systems that control industrial processes in critical infrastructure such as electric grids, nuclear power plants, manufacturing plans, water treatment systems, pharmaceutical plants, and building automation systems. ICS represent complex systems that contain an abundance of unique devices all of which may hold different types of software, including applications, firmware and operating systems. Due to their ability to control physical infrastructure, ICS have more and more become targets of cyber-attacks, increasing the risk of serious damage, negative financial impact, disruption to business operations, disruption to communities, and even the loss of life. Ethical hacking represents one way to test the security of ICS. Ethical hacking consists of using a cyber-attacker's perspective and a variety of cybersecurity tools to actively discover vulnerabilities and entry points for potential cyber-attacks. However, ICS ethical hacking represents a difficult task due to the wide variety of devices found on ICS networks. Most ethical hackers do not hold expertise or knowledge about ICS hardware, device computing elements, protocols, vulnerabilities found on these elements, and exploits used to exploit these vulnerabilities. Effective approaches are needed to reduce the complexity of ICS ethical hacking tasks. In this study, we use ontology modeling, a knowledge representation approach in artificial intelligence (AI), to model data that represent ethical hacking tasks of building automation systems. With ontology modeling, information is stored and represented in the form of semantic graphs that express individuals, their properties, and the relations between multiple individuals. Data are drawn from sources such as the National Vulnerability Database, ExploitDB, Common Weakness Enumeration (CWE), the Common Attack Pattern and Enumeration Classification (CAPEC), and others. We show, through semantic queries, how the ontology model can automatically link together entities such as software names and versions of ICS software, vulnerabilities found on those software instances, vulnerabilities found on the protocols used by the software, exploits found on those vulnerabilities, weaknesses that represent those vulnerabilities, and attacks that can exploit those weaknesses. The ontology modeling of ICS ethical hacking and the semantic queries run over the model can reduce the complexity of ICS hacking tasks.
more »
« less
Exploring Ontologies for Mitigation Selection of Industrial Control System Vulnerabilities
Mitigating vulnerabilities in industrial control systems (ICSs) represents a highly complex task. ICSs may contain an abundance of device types, all with unique software and hardware components. Upon discovering vulnerabilities on ICS devices, cyber defenders must determine which mitigations to implement, and which mitigations can apply across multiple vulnerabilities. Cyber defenders need techniques to optimize mitigation selection. This exploratory research paper shows how ontologies, also known as linked-data models, can potentially be used to model ICS devices, vulnerabilities, and mitigations, as well as to identify mitigations that can remediate or mitigate multiple vulnerabilities. Ontologies can be used to reduce the complexity of a cyber defender’s role by allowing for insights to be drawn, especially in the ICS domain. Data are modelled from the Common Platform Enumeration (CPE), the National Vulnerability Database (NVD), standardized list of controls from the National Institute of Standards and Technology (NIST), and ICS Cyber Emergency Response Team (CERT) advisories. Semantic queries provide the techniques for mitigation prioritization. A case study is described for a selected programmable logic controller (PLC), its known vulnerabilities from the NVD, and recommended mitigations from ICS CERT. Overall, this research shows how ontologies can be used to link together existing data sources, to run queries over the linked data, and to allow for new insights to be drawn for mitigation selection.
more »
« less
- Award ID(s):
- 1922202
- PAR ID:
- 10327901
- Date Published:
- Journal Name:
- 17th International Conference on Cyber Warfare and Security
- Volume:
- 17
- Issue:
- 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Recently, wireless communication technologies, such as Wireless Local Area Networks (WLANs), have gained increasing popularity in industrial control systems (ICSs) due to their low cost and ease of deployment, but communication delays associated with these technologies make it unsuitable for critical real-time and safety applications. To address concerns on network-induced delays of wireless communication technologies and bring their advantages into modern ICSs, wireless network infrastructure based on the Parallel Redundancy Protocol (PRP) has been proposed. Although application-specific simulations and measurements have been conducted to show that wireless network infrastructure based on PRP can be a viable solution for critical applications with stringent delay performance constraints, little has been done to devise an analytical framework facilitating the adoption of wireless PRP infrastructure in miscellaneous ICSs. Leveraging the deterministic network calculus (DNC) theory, we propose to analytically derive worst-case bounds on network- induced delays for critical ICS applications. We show that the problem of worst-case delay bounding for a wireless PRP network can be solved by performing network-calculus-based analysis on its non-feedforward traffic pattern. Closed-form expressions of worst-case delays are derived, which has not been found previously and allows ICS architects/designers to compute worst- case delay bounds for ICS tasks in their respective application domains of interest. Our analytical results not only provide insights into the impacts of network-induced delays on latency- critical tasks but also allow ICS architects/operators to assess whether proper wireless RPR network infrastructure can be adopted into their systems.more » « less
-
null (Ed.)The implementation of Internet of Things (IoT) devices in medical environments, has introduced a growing list of security vulnerabilities and threats. The lack of an extensible big data resource that captures medical device vulnerabilities limits the use of Artificial Intelligence (AI) based cyber defense systems in capturing, detecting, and preventing known and future attacks. We describe a system that generates a repository of Cyber Threat Intelligence (CTI) about various medical devices and their known vulnerabilities from sources such as manufacturer and ICS-CERT vulnerability alerts. We augment the intelligence repository with data sources such as Wikidata and public medical databases. The combined resources are integrated with threat intelligence in our Cybersecurity Knowledge Graph (CKG) from previous research. The augmented graph embeddings are useful in querying relevant information and can help in various AI assisted cybersecurity tasks. Given the integration of multiple resources, we found the augmented CKG produced higher quality graph representations. The augmented CKG produced a 31% increase in the Mean Average Precision (MAP) value, computed over an information retrieval task.more » « less
-
Ethical hacking consists of scanning for targets, evaluating the targets, gaining access, maintaining access, and clearing tracks. The evaluation of targets represents a complex task due to the number of IP addresses, domain names, open ports, vulnerabilities, and exploits that must be examined. Ethical hackers synthesize data from various hacking tools to determine targets that are of high value and that are highly susceptible to cyber-attacks. These tasks represent situation assessment tasks. Previous research considers situation assessment tasks to be tasks that involve viewing an initial set of information about a problem and subsequently piecing together more information to solve the problem. Our research used semantic-web technologies, including ontologies, natural language processing (NLP), and semantic queries, to automate the situation assessment tasks conducted by ethical hackers when evaluating targets. More specifically, our research focused on automatically identifying education organizations that use industrial control system protocols which in turn have highly exploitable vulnerabilities and known exploits. We used semantic-web technologies to reduce an initial dataset of 126,636 potential targets to 155 distinct targets with these characteristics. Our research adds to previous research on situation assessment by showing how semantic-web technologies can be used to reduce the complexity of situation assessment tasks.more » « less
-
Cyber-physical system security is a significant concern in the critical infrastructure. Strong interdependencies between cyber and physical components render cyber-physical systems highly susceptible to integrity attacks such as injecting malicious data and projecting fake sensor measurements. Traditional security models partition cyber-physical systems into just two domains – high and low. This absolute partitioning is not well suited to cyber-physical systems because they comprise multiple overlapping partitions. Information flow properties, which model how inputs to a system affect its outputs across security partitions, are important considerations in cyber-physical systems. Information flows support traceability analysis that helps detect vulnerabilities and anomalous sources, contributing to the implementation of mitigation measures. This chapter describes an automated model with graph-based information flow traversal for identifying information flow paths in the Automatic Dependent Surveillance-Broadcast (ADS-B) system used in civilian aviation, and subsequently partitioning the flows into security domains. The results help identify ADS-B system vulnerabilities to failures and attacks, and determine potential mitigation measures.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.34190/iccws.17.1.32
