Visible to the public Biblio

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Bahirat, Kanchan, Shah, Umang, Cardenas, Alvaro A., Prabhakaran, Balakrishnan.  2018.  ALERT: Adding a Secure Layer in Decision Support for Advanced Driver Assistance System (ADAS). Proceedings of the 26th ACM International Conference on Multimedia. :1984-1992.

With the ever-increasing popularity of LiDAR (Light Image Detection and Ranging) sensors, a wide range of applications such as vehicle automation and robot navigation are developed utilizing the 3D LiDAR data. Many of these applications involve remote guidance - either for safety or for the task performance - of these vehicles and robots. Research studies have exposed vulnerabilities of using LiDAR data by considering different security attack scenarios. Considering the security risks associated with the improper behavior of these applications, it has become crucial to authenticate the 3D LiDAR data that highly influence the decision making in such applications. In this paper, we propose a framework, ALERT (Authentication, Localization, and Estimation of Risks and Threats), as a secure layer in the decision support system used in the navigation control of vehicles and robots. To start with, ALERT tamper-proofs 3D LiDAR data by employing an innovative mechanism for creating and extracting a dynamic watermark. Next, when tampering is detected (because of the inability to verify the dynamic watermark), ALERT then carries out cross-modal authentication for localizing the tampered region. Finally, ALERT estimates the level of risk and threat based on the temporal and spatial nature of the attacks on LiDAR data. This estimation of risk and threats can then be incorporated into the decision support system used by ADAS (Advanced Driver Assistance System). We carried out several experiments to evaluate the efficacy of the proposed ALERT for ADAS and the experimental results demonstrate the effectiveness of the proposed approach.

Markman, Chen, Wool, Avishai, Cardenas, Alvaro A..  2017.  A New Burst-DFA Model for SCADA Anomaly Detection. Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy. :1–12.

In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Past work showed that in many cases, it is possible to model the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server by a cyclic Deterministic Finite Automaton (DFA), and to use the model to detect anomalies in the traffic. However, a recent analysis of network traffic in a water facility in the U.S, showed that cyclic-DFA models have limitations. In our research, we examine the same data corpus; our study shows that the communication on all of the channels in the network is done in bursts of packets, and that the bursts have semantic meaning---the order within a burst depends on the messages. Using these observations, we suggest a new burst-DFA model that fits the data much better than previous work. Our model treats the traffic on each channel as a series of bursts, and matches each burst to the DFA, taking the burst's beginning and end into account. Our burst-DFA model successfully explains between 95% and 99% of the packets in the data-corpus, and goes a long way toward the construction of a practical anomaly detection system.

Murillo, Andrés Felipe, Cómbita, Luis Francisco, Gonzalez, Andrea Calderón, Rueda, Sandra, Cardenas, Alvaro A., Quijano, Nicanor.  2018.  A Virtual Environment for Industrial Control Systems: A Nonlinear Use-Case in Attack Detection, Identification, and Response. Proceedings of the 4th Annual Industrial Control System Security Workshop. :25–32.

The integration of modern information technologies with industrial control systems has created an enormous interest in the security of industrial control, however, given the cost, variety, and industry practices, it is hard for researchers to test and deploy security solutions in real-world systems. Industrial control testbeds can be used as tools to test security solutions before they are deployed, and in this paper we extend our previous work to develop open-source virtual industrial control testbeds where computing and networking components are emulated and virtualized, and the physical system is simulated through differential equations. In particular, we implement a nonlinear control system emulating a three-water tank with the associated sensors, PLCs, and actuators that communicate through an emulated network. In addition, we design unknown input observers (UIO) to not only detect that an attack is occurring, but also to identify the source of the malicious false data injections and mitigate its impact. Our system is available through Github to the academic community.

Urbina, David I., Giraldo, Jairo A., Cardenas, Alvaro A., Tippenhauer, Nils Ole, Valente, Junia, Faisal, Mustafa, Ruths, Justin, Candell, Richard, Sandberg, Henrik.  2016.  Limiting the Impact of Stealthy Attacks on Industrial Control Systems. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :1092–1105.

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.

Valente, Junia, Cardenas, Alvaro A..  2017.  Security & Privacy in Smart Toys. Proceedings of the 2017 Workshop on Internet of Things Security and Privacy. :19–24.

We analyze the security practices of three smart toys that communicate with children through voice commands. We show the general communication architecture, and some general security and privacy practices by each of the devices. Then we focus on the analysis of one particular toy, and show how attackers can decrypt communications to and from a target device, and perhaps more worryingly, the attackers can also inject audio into the toy so the children listens to any arbitrary audio file the attacker sends to the toy. This last attack raises new safety concerns that manufacturers of smart toys should prevent.