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1. PAtt: Physics-based Attestation of Control Systems

   Ghaeini, H.R.; Chan, M.; Bahmani, R.; F., Garcia; Zhou, J.; Sadeghi, A.R.; Tippenhauer, N.O.; Zonouz, S (September 2019, 22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2019))

   null (Ed.)

   Ensuring the integrity of embedded programmable logic controllers (PLCs) is critical for safe operation of industrial control systems. In particular, a cyber-attack could manipulate control logic running on the PLCs to bring the process of safety-critical application into unsafe states. Unfortunately, PLCs are typically not equipped with hardware support that allows the use of techniques such as remote attestation to verify the integrity of the logic code. In addition, so far remote attestation is not able to verify the integrity of the physical process controlled by the PLC. In this work, we present PAtt, a system that combines remote software attestation with control process validation. PAtt leverages operation permutations—subtle changes in the operation sequences based on integrity measurements—which do not affect the physical process but yield unique traces of sensor readings during execution. By encoding integrity measurements of the PLC’s memory state (software and data) into its control operation, our system allows to remotely verify the integrity of the control logic based on the resulting sensor traces. We implement the proposed system on a real PLC controlling a robot arm, and demonstrate its feasibility. Our implementation enables the detection of attackers that manipulate the PLC logic to change process state and/or report spoofed sensor readings (with an accuracy of 97% against tested attacks). 

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2. Detecting Safety and Security Faults in PLC Systems with Data Provenance

   Al Farooq, Abdullah; Marquard, Jessica; George, Kripa; Moyer, Thomas (November 2019, IEEE International Symposium on Technologies for Homeland Security)

   Programmable Logic Controllers are an integral component for managing many different industrial processes (e.g., smart building management, power generation, water and wastewater management, and traffic control systems), and manufacturing and control industries (e.g., oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, automotive, and aerospace). Despite being used widely in many critical infrastructures, PLCs use protocols which make these control systems vulnerable to many common attacks, including man-in-the-middle attacks, denial of service attacks, and memory corruption attacks (e.g., array, stack, and heap overflows, integer overflows, and pointer corruption). In this paper, we propose PLC-PROV, a system for tracking the inputs and outputs of the control system to detect violations in the safety and security policies of the system. We consider a smart building as an example of a PLC-based system and show how PLC-PROV can be applied to ensure that the inputs and outputs are consistent with the intended safety and security policies. 

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3. A Convolutional Neural Network-LSTM Based Physical Sensor Anomaly Detector for Interdependent SCADA Controllers

   https://doi.org/10.5772/acrt.20250037  

   Das, Rishabh; Werth, Aaron; Morris, Thomas (January 2025, AI, Computer Science and Robotics Technology)

   This study outlines a novel intrusion detection system (IDS) to detect compromised sensor data anomalies in interdependent industrial processes. The IDS used a peer-to-peer communication framework which allowed multiple programmable logic controllers (PLCs) to communicate and share sensor data. Utilizing the shared sensor data, state estimators used a long short-term memory (LSTM) machine learning algorithm to identify anomalous sensor readings connected to neighboring PLCs controlling an interdependent physical process. This study evaluated the performance of the IDS on three industrial operations aligning to a midstream oil terminal. The framework successfully detected several multi-sensor compromises during mid-stream oil terminal operations. A set of performance evaluations also showed no impact on the real-time operations of the PLC and outlined the prediction latencies of the framework. 

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4. Employing attack graphs for intrusion detection

   https://doi.org/10.1145/3368860.3368862  

   Capobianco, Frank; George, Rahul; Huang, Kaiming; Jaeger, Trent; Krishnamurthy, Srikanth; Qian, Zhiyun; Payer, Mathias; Yu, Paul (September 2019, New Security Paradigms Workshop)

   Intrusion detection systems are a commonly deployed defense that examines network traffic, host operations, or both to detect attacks. However, more attacks bypass IDS defenses each year, and with the sophistication of attacks increasing as well, we must examine new perspectives for intrusion detection. Current intrusion detection systems focus on known attacks and/or vulnerabilities, limiting their ability to identify new attacks, and lack the visibility into all system components necessary to confirm attacks accurately, particularly programs. To change the landscape of intrusion detection, we propose that future IDSs track how attacks evolve across system layers by adapting the concept of attack graphs. Attack graphs were proposed to study how multi-stage attacks could be launched by exploiting known vulnerabilities. Instead of constructing attacks reactively, we propose to apply attack graphs proactively to detect sequences of events that fulfill the requirements for vulnerability exploitation. Using this insight, we examine how to generate modular attack graphs automatically that relate adversary accessibility for each component, called its attack surface, to flaws that provide adversaries with permissions that create threats, called attack states, and exploit operations from those threats, called attack actions. We evaluate the proposed approach by applying it to two case studies: (1) attacks on file retrieval, such as TOCTTOU attacks, and (2) attacks propagated among processes, such as attacks on Shellshock vulnerabilities. In these case studies, we demonstrate how to leverage existing tools to compute attack graphs automatically and assess the effectiveness of these tools for building complete attack graphs. While we identify some research areas, we also find several reasons why attack graphs can provide a valuable foundation for improving future intrusion detection systems. 

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5. VIPER: Spotting Syscall-Guard Variables for Data-Only Attacks

   Ye, Hengkai; Liu, Song; Zhang, Zhechang; Hu, Hong (August 2023, Proceedings of the 32nd USENIX Security Symposium (USENIX 2023))

   As control-flow protection techniques are widely deployed, it is difficult for attackers to modify control data, like function pointers, to hijack program control flow. Instead, data-only attacks corrupt security-critical non-control data (critical data), and can bypass all control-flow protections to revive severe attacks. Previous works have explored various methods to help construct or prevent data-only attacks. However, no solution can automatically detect program-specific critical data. In this paper, we identify an important category of critical data, syscall-guard variables, and propose a set of solutions to automatically detect such variables in a scalable manner. Syscall-guard variables determine to invoke security-related system calls (syscalls), and altering them will allow attackers to request extra privileges from the operating system. We propose branch force, which intentionally flips every conditional branch during the execution and checks whether new security-related syscalls are invoked. If so, we conduct data-flow analysis to estimate the feasibility to flip such branches through common memory errors. We build a tool, VIPER, to implement our ideas. VIPER successfully detects 34 previously unknown syscall-guard variables from 13 programs. We build four new data-only attacks on sqlite and v8, which execute arbitrary command or delete arbitrary file. VIPER completes its analysis within five minutes for most programs, showing its practicality for spotting syscall-guard variables. 

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