MiniCPS: A toolkit for security research on CPS Networks

In recent years, tremendous effort has been spent to modernizing communication infrastructure in Cyber-Physical Systems (CPS) such as Industrial Control Systems (ICS) and related Supervisory Control and Data Acquisition (SCADA) systems. While a great amount of research has been conducted on network security of office and home networks, recently the security of CPS and related systems has gained a lot of attention. Unfortunately, real-world CPS are often not open to security researchers, and as a result very few reference systems and topologies are available. In this work, we present MiniCPS, a CPS simulation toolbox intended to alleviate this problem. The goal of MiniCPS is to create an extensible, reproducible research environment targeted to communications and physical-layer interactions in CPS. MiniCPS builds on Mininet to provide lightweight real-time network emulation, and extends Mininet with tools to simulate typical CPS components such as programmable logic controllers, which use industrial protocols (Ethernet/IP, Modbus/TCP). In addition, MiniCPS defines a simple API to enable physical-layer interaction simulation. In this work, we demonstrate applications of MiniCPS in two example scenarios, and show how MiniCPS can be used to develop attacks and defenses that are directly applicable to real systems.Comment: 8 pages, 6 figures, 1 code listin

No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems

In recent years, a number of process-based anomaly detection schemes for Industrial Control Systems were proposed. In this work, we provide the first systematic analysis of such schemes, and introduce a taxonomy of properties that are verified by those detection systems. We then present a novel general framework to generate adversarial spoofing signals that violate physical properties of the system, and use the framework to analyze four anomaly detectors published at top security conferences. We find that three of those detectors are susceptible to a number of adversarial manipulations (e.g., spoofing with precomputed patterns), which we call Synthetic Sensor Spoofing and one is resilient against our attacks. We investigate the root of its resilience and demonstrate that it comes from the properties that we introduced. Our attacks reduce the Recall (True Positive Rate) of the attacked schemes making them not able to correctly detect anomalies. Thus, the vulnerabilities we discovered in the anomaly detectors show that (despite an original good detection performance), those detectors are not able to reliably learn physical properties of the system. Even attacks that prior work was expected to be resilient against (based on verified properties) were found to be successful. We argue that our findings demonstrate the need for both more complete attacks in datasets, and more critical analysis of process-based anomaly detectors. We plan to release our implementation as open-source, together with an extension of two public datasets with a set of Synthetic Sensor Spoofing attacks as generated by our framework

Assessing Model-free Anomaly Detection in Industrial Control Systems Against Generic Concealment Attacks

In recent years, a number of model-free process-based anomaly detection schemes for Industrial Control Systems (ICS) were proposed. Model-free anomaly detectors are trained directly from process data and do not require process knowledge. They are validated based on a set of public data with limited attacks present. As result, the resilience of those schemes against general concealment attacks is unclear. In addition, no structured discussion on the properties verified by the detectors exists. In this work, we provide the first systematic analysis of such anomaly detection schemes, focusing on six model-free process-based anomaly detectors. We hypothesize that the detectors verify a combination of temporal, spatial, and statistical consistencies. To test this, we systematically analyse their resilience against generic concealment attacks. Our generic concealment attacks are designed to violate a specific consistency verified by the detector, and require no knowledge of the attacked physical process or the detector. In addition, we compare against prior work attacks that were designed to attack neural network-based detectors. Our results demonstrate that the evaluated model-free detectors are in general susceptible to generic concealment attacks. For each evaluated detector, at least one of our generic concealment attacks performs better than prior work attacks. In particular, the results allow us to show which specific consistencies are verified by each detector. We also find that prior work attacks that target neural-network architectures transfer surprisingly well against other architectures

Smooth Transition of Vehicles' Maximum Speed for Lane Detection based on Computer Vision

This paper presents a prototype electric scooter designed to detect the driving lane via computer vision and automatically set the vehicular configuration. The electric scooter can drive on the pedestrian, bicycle, or car lanes. The government enforces maximum speeds on each lane for the electric scooter. Our prototype scooter would apply those regulations securely, with the help of a computer vision component. However, the safety of such a system is still part of the concern and research is going on the security and safety aspects of such vehicular systems. The maximum speed changes while the driver is riding the vehicle at the fastest possible speed could cause a safety hazard. To prevent that, we proposed to use the logarithmic speed reduction or acceleration. The results show that such an algorithm will smooth the transition between the maximum of the vehicle

Security Analysis of Vendor Implementations of the OPC UA Protocol for Industrial Control Systems

The OPC UA protocol is an upcoming de-facto standard for building Industry 4.0 processes in Europe, and one of the few industrial protocols that promises security features to prevent attackers from manipulating and damaging critical infrastructures. Despite the importance of the protocol, challenges in the adoption of OPC UA's security features by product vendors, libraries implementing the standard, and end-users were not investigated so far. In this work, we systematically investigate 48 publicly available artifacts consisting of products and libraries for OPC UA and show that 38 out of the 48 artifacts have one (or more) security issues. We show that 7 OPC UA artifacts do not support the security features of the protocol at all. In addition, 31 artifacts that partially feature OPC UA security rely on incomplete libraries and come with misleading instructions. Consequently, relying on those products and libraries will result in vulnerable implementations of OPC UA security features. To verify our analysis, we design, implement, and demonstrate attacks in which the attacker can steal credentials exchanged between victims, eavesdrop on process information, manipulate the physical process through sensor values and actuator commands, and prevent the detection of anomalies

Nearby Threats: Reversing, Analyzing, and Attacking Google’s 'Nearby Connections' on Android

Google’s Nearby Connections API enables any An-droid (and Android Things) application to provide proximity-based services to its users, regardless of their network connectivity.The API uses Bluetooth BR/EDR, Bluetooth LE and Wi-Fi to let“nearby” clients (discoverers) and servers (advertisers) connectand exchange different types of payloads. The implementation ofthe API is proprietary, closed-source and obfuscated. The updatesof the API are automatically installed by Google across differentversions of Android, without user interaction. Little is knownpublicly about the security guarantees offered by the API, eventhough it presents a significant attack surface.In this work we present the first security analysis of theGoogle’s Nearby Connections API, based on reverse-engineeringof its Android implementation. We discover and implement sev-eral attacks grouped into two families: connection manipulation(CMA) and range extension attacks (REA). CMA-attacks allow anattacker to insert himself as a man-in-the-middle and manipulateconnections (even unrelated to nearby), and to tamper withthe victim’s interface and network configuration. REA-attacksallow an attacker to tunnel any nearby connection to remotelocations, even between two honest devices. Our attacks areenabled by REArby, a toolkit we developed while reversingthe API implementation. REArby includes a dynamic binaryinstrumenter, a packet dissector, and the implementations ofcustom Nearby Connections client and server. We plan to open-source REArby after a responsible disclosure period