Anomaly diagnosis in industrial control systems for digital forensics

Over several decades, Industrial Control Systems (ICS) have become more interconnected and highly programmable. An increasing number of sophisticated cyber-attacks have targeted ICS with a view to cause tangible damage. Despite the stringent functional safety requirements mandated within ICS environments, critical national infrastructure (CNI) sectors and ICS vendors have been slow to address the growing cyber threat. In contrast with the design of information technology (IT) systems, security of controls systems have not typically been an intrinsic design principle for ICS components, such as Programmable Logic Controllers (PLCs). These factors have motivated substantial research addressing anomaly detection in the context of ICS. However, detecting incidents alone does not assist with the response and recovery activities that are necessary for ICS operators to resume normal service. Understanding the provenance of anomalies has the potential to enable the proactive implementation of security controls, and reduce the risk of future attacks. Digital forensics provides solutions by dissecting and reconstructing evidence from an incident. However, this has typically been positioned from a post-incident perspective, which inhibits rapid triaging, and effective response and recovery, an essential requirement in critical ICS. This thesis focuses on anomaly diagnosis, which involves the analysis of and discrimination between different types of anomalous event, positioned at the intersection between anomaly detection and digital forensics. An anomaly diagnosis framework is proposed that includes mechanisms to aid ICS operators in the context of anomaly triaging and incident response. PLCs have a fundamental focus within this thesis due to their critical role and ubiquitous application in ICS. An examination of generalisable PLC data artefacts produced a taxonomy of artefact data types that focus on the device data generated and stored in PLC memory. Using the artefacts defined in this first stage, an anomaly contextualisation model is presented that differentiates between cyber-attack and system fault anomalies. Subsequently, an attack fingerprinting approach (PLCPrint) generates near real-time compositions of memory fingerprints within 200ms, by correlating the static and dynamic behaviour of PLC registers. This establishes attack type and technique provenance, and maintains the chain-of-evidence for digital forensic investigations. To evaluate the efficacy of the framework, a physical ICS testbed modelled on a water treatment system is implemented. Multiple PLC models are evaluated to demonstrate vendor neutrality of the framework. Furthermore, several generalised attack scenarios are conducted based on techniques identified from real PLC malware. The results indicate that PLC device artefacts are particularly powerful at detecting and contextualising an anomaly. In general, we achieve high F1 scores of at least 0.98 and 0.97 for anomaly detection and contextualisation, respectively, which are highly competitive with existing state-of-the-art literature. The performance of PLCPrint emphasises how PLC memory snapshots can precisely and rapidly provide provenance by classifying cyber-attacks with an accuracy of 0.97 in less than 400ms. The proposed framework offers a much needed novel approach through which ICS components can be rapidly triaged for effective response

FieldFuzz: In Situ Blackbox Fuzzing of Proprietary Industrial Automation Runtimes via the Network

Networked Programmable Logic Controllers (PLCs) are proprietary industrial devices utilized in critical infrastructure that execute control logic applications in complex proprietary runtime environments that provide standardized access to the hardware resources in the PLC. These control applications are programmed in domain specific IEC 61131-3 languages, compiled into a proprietary binary format, and process data provided via industrial protocols. Control applications present an attack surface threatened by manipulated traffic. For example, remote code injection in a control application would directly allow to take over the PLC, threatening physical process damage and the safety of human operators. However, assessing the security of control applications is challenging due to domain-specific challenges and the limited availability of suitable methods. Network-based fuzzing is often the only way to test such devices but is inefficient without guidance from execution tracing. This work presents the FieldFuzz framework that analyzes the security risks posed by the Codesys runtime (used by over 400 devices from 80 industrial PLC vendors). FieldFuzz leverages efficient network-based fuzzing based on three main contributions: i) reverse-engineering enabled remote control of control applications and runtime components, ii) automated command discovery and status code extraction via network traffic and iii) a monitoring setup to allow on-system tracing and coverage computation. We use FieldFuzz to run fuzzing campaigns, which uncover multiple vulnerabilities, leading to three reported CVE IDs. To study the cross-platform applicability of FieldFuzz, we reproduce the findings on a diverse set of Industrial Control System (ICS) devices, showing a significant improvement over the state-of-the-art

VIRTUAL PLC PLATFORM FOR SECURITY AND FORENSICS OF INDUSTRIAL CONTROL SYSTEMS

Industrial Control Systems (ICS) are vital in managing critical infrastructures, including nuclear power plants and electric grids. With the advent of the Industrial Internet of Things (IIoT), these systems have been integrated into broader networks, enhancing efficiency but also becoming targets for cyberattacks. Central to ICS are Programmable Logic Controllers (PLCs), which bridge the physical and cyber worlds and are often exploited by attackers. There\u27s a critical need for tools to analyze cyberattacks on PLCs, uncover vulnerabilities, and improve ICS security. Existing tools are hindered by the proprietary nature of PLC software, limiting scalability and efficiency. To overcome these challenges, I developed a Virtual PLC Platform (VPP) for forensic analyses of ICS attacks and vulnerability identification. The VPP employs the packet replay technique, using network traffic to create a PLC template. This template guides the virtual PLC in network communication, mimicking real PLCs. A Protocol Reverse Engineering Engine (PREE) module assists in reverse-engineering ICS protocols and discovering vulnerabilities. The VPP is automated, supporting PLCs from various vendors, and eliminates manual reverse engineering. This dissertation highlights the architecture and applications of the VPP in forensic analysis, reverse engineering, vulnerability discovery, and threat intelligence gathering, all crucial to bolstering the security and integrity of critical infrastructure

On the Significance of Process Comprehension for Conducting Targeted ICS Attacks

The exploitation of Industrial Control Systems (ICSs) has been described as both easy and impossible, where is the truth? Post-Stuxnet works have included a plethora of ICS focused cyber secu- rity research activities, with topics covering device maturity, network protocols, and overall cyber security culture. We often hear the notion of ICSs being highly vulnerable due to a lack of inbuilt security mechanisms, considered a low hanging fruit to a variety of low skilled threat actors. While there is substantial evidence to support such a notion, when considering targeted attacks on ICS, it is hard to believe an attacker with limited resources, such as a script kiddie or hacktivist, using publicly accessible tools and exploits alone, would have adequate knowledge and resources to achieve targeted operational process manipulation, while simultaneously evade detection. Through use of a testbed environment, this paper provides two practical examples based on a Man-In-The-Middle scenario, demonstrating the types of information an attacker would need obtain, collate, and comprehend, in order to begin targeted process manipulation and detection avoidance. This allows for a clearer view of associated challenges, and illustrate why targeted ICS exploitation might not be possible for every malicious actor

Implementation of Fully Automated Electricity for large Building Using SCADA Tool like LabVIEW

This paper deals with the development and implementation of a framework of SCADA using LabVIEW based Data Acquisition and Management. A SCADA system is described in terms of architecture, process interfaces, functionality, and application development facilities. The system is low on system requirements and easy to implement. This paper describes configuration of Remote Terminal Units to access and transmit real time data over the Intranet, logging all the data in a historian for future references at a centralized location, keeping the human operator updated via Human Machine Interface, and providing measures for any emergency and enabling control via SMS giving true remote control capabilities. In addition, the project can automatically run the suitable generator from his reading of the foundation power; in short it’s the best solution of the foundation electricity problem

Toward Automating Web Protocol Configuration for a Programmable Logic Controller Emulator

Industrial Control Systems (ICS) remain vulnerable through attack vectors that exist within programmable logic controllers (PLC). PLC emulators used as honeypots can provide insight into these vulnerabilities. Honeypots can sometimes deter attackers from real devices and log activity. A variety of PLC emulators exist, but require manual figuration to change their PLC pro le. This limits their flexibility for deployment. An automated process for configuring PLC emulators can open the door for emulation of many types of PLCs. This study investigates the feasibility of creating such a process. The research creates an automated process for figuring the web protocols of a Koyo DirectLogic PLC. The figuration process is a software program that collects information about the PLC and creates a behavior pro le. A generic web server then references that pro le in order to respond properly to requests. To measure the ability of the process, the resulting emulator is evaluated based on response accuracy and timing accuracy. In addition, the figuration time of the process itself is measured. For the accuracy measurements a workload of 1000 GET requests are sent to the index.html page of the PLC, and then to the emulator. These requests are sent at two rates: Slow and PLC Break. The emulator responses are then compared to those of the PLC baseline. Results show that the process completes in 9.8 seconds, on average. The resulting emulator responds with 97.79% accuracy across all trials. It responds 1.3 times faster than the real PLC at the Slow response rate, and 1.4 times faster at the PLC Break rate. Results indicate that the automated process is able to create an emulator with an accuracy that is comparable to a manually figured emulator. This supports the hypothesis that creating an automated process for figuring a PLC emulator with a high level of accuracy is feasible

Impact of the Shodan Computer Search Engine on Internet-facing Industrial Control System Devices

The Shodan computer search engine crawls the Internet attempting to identify any connected device. Using Shodan, researchers identified thousands of Internet-facing devices associated with industrial controls systems (ICS). This research examines the impact of Shodan on ICS security, evaluating Shodan\u27s ability to identify Internet-connected ICS devices and assess if targeted attacks occur as a result of Shodan identification. In addition, this research evaluates the ability to limit device exposure to Shodan through service banner manipulation. Shodan\u27s impact was evaluated by deploying four high-interaction, unsolicited honeypots over a 55 day period, each configured to represent Allen-Bradley programmable logic controllers (PLC). All four honeypots were successfully indexed and identifiable via the Shodan web interface in less than 19 days. Despite being indexed, there was no increased network activity or targeted ICS attacks. Although results indicate Shodan is an effective reconnaissance tool, results contrast claims of its use to broadly identify and target Internet-facing ICS devices. Additionally, the service banner for two PLCs were modified to evaluate the impact on Shodan indexing capabilities. Findings demonstrated service banner manipulation successfully limited device exposure from Shodan queries

Automatic Configuration of Programmable Logic Controller Emulators

Programmable logic controllers (PLCs), which are used to control much of the world\u27s critical infrastructures, are highly vulnerable and exposed to the Internet. Many efforts have been undertaken to develop decoys, or honeypots, of these devices in order to characterize, attribute, or prevent attacks against Industrial Control Systems (ICS) networks. Unfortunately, since ICS devices typically are proprietary and unique, one emulation solution for a particular vendor\u27s model will not likely work on other devices. Many previous efforts have manually developed ICS honeypots, but it is a very time intensive process. Thus, a scalable solution is needed in order to automatically configure PLC emulators. The ScriptGenE Framework presented in this thesis leverages several techniques used in reverse engineering protocols in order to automatically configure PLC emulators using network traces. The accuracy, flexibility, and efficiency of the ScriptGenE Framework is tested in three fully automated experiments

Constructing Cost-Effective and Targetable ICS Honeypots Suited for Production Networks

Honeypots are a technique that can mitigate the risk of cyber threats. Effective honeypots are authentic and targetable, and their design and implementation must accommodate risk tolerance and financial constraints. The proprietary, and often expensive, hardware and software used by Industrial Control System (ICS) devices creates the challenging problem of building a flexible, economical, and scalable honeypot. This research extends Honeyd into Honeyd+, making it possible to use the proxy feature to create multiple high interaction honeypots with a single Programmable Logic Controller (PLC). Honeyd+ is tested with a network of 75 decoy PLCs, and the interactions with the decoys are compared to a physical PLC to test for authenticity. The performance test evaluates the impact of multiple simultaneous connections to the PLC. The functional test is successful in all cases. The performance test demonstrated that the PLC is a limiting factor, and that introducing Honeyd+ has a marginal impact on performance. Notable findings are that the Raspberry Pi is the preferred hosting platform, and more than five simultaneous connections were not optimal