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.
Ontology Modelling of Industrial Control System Ethical Hacking
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 more »
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- International Conference on Cyber Warfare and Security
- Sponsoring Org:
- National Science Foundation
More Like this
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,more »
An Ontology-Based Framework for Formal Verification of Safety and Security Properties of Control LogicsAny safety issues or cyber attacks on an Industrial Control Systems (ICS) may have catastrophic consequences on human lives and the environment. Hence, it is imperative to have resilient tools and mechanisms to protect ICS. To verify the safety and security of the control logic, complete and consistent specifications should be defined to guide the testing process. Second, it is vital to ensure that those requirements are met by the program control algorithm. In this paper, we proposed an approach to formally define the system specifications, safety, and security requirements to build an ontology that is used further to verify the control logic of the PLC software. The use of ontology allowed us to reason about semantic concepts, check the consistency of concepts, and extract specifications by inference. For the proof of concept, we studied part of an industrial chemical process to implement the proposed approach. The experimental results in this work showed that the proposed approach detects inconsistencies in the formally defined requirements and is capable of verifying the correctness and completeness of the control logic. The tools and algorithms designed and developed as part of this work will help technicians and engineers create safer and more secure controlmore »
Architectural Security Weaknesses in Industrial Control Systems (ICS) an Empirical Study Based on Disclosed Software VulnerabilitiesIndustrial control systems (ICS) are systems used in critical infrastructures for supervisory control, data acquisition, and industrial automation. ICS systems have complex, component-based architectures with many different hardware, software, and human factors interacting in real time. Despite the importance of security concerns in industrial control systems, there has not been a comprehensive study that examined common security architectural weaknesses in this domain. Therefore, this paper presents the first in-depth analysis of 988 vulnerability advisory reports for Industrial Control Systems developed by 277 vendors. We performed a detailed analysis of the vulnerability reports to measure which components of ICS have been affected the most by known vulnerabilities, which security tactics were affected most often in ICS and what are the common architectural security weaknesses in these systems. Our key findings were: (1) Human-Machine Interfaces, SCADA configurations, and PLCs were the most affected components, (2) 62.86% of vulnerability disclosures in ICS had an architectural root cause, (3) the most common architectural weaknesses were “Improper Input Validation”, followed by “Im-proper Neutralization of Input During Web Page Generation” and “Improper Authentication”, and (4) most tactic-related vulnerabilities were related to the tactics “Validate Inputs”, “Authenticate Actors” and “Authorize Actors”.
Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-based Deep Structured Semantic ModelBlack hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)-based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-the-art non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20%-41% higher than non-DL approaches and 4%-10% higher than DL-based approaches. We demonstrated themore »