Biblio

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.

Advanced Persistent Threats (APTs) are stealthy, sophisticated, long-term, multi-stage attacks that threaten the security of sensitive information. Dynamic Information Flow Tracking (DIFT) has been proposed as a promising mechanism to detect and prevent various cyber attacks in computer systems. DIFT tracks suspicious information flows in the system and generates security analysis when anomalous behavior is detected. The number of information flows in a system is typically large and the amount of resources (such as memory, processing power and storage) required for analyzing different flows at different system locations varies. Hence, efficient use of resources is essential to maintain an acceptable level of system performance when using DIFT. On the other hand, the quickest detection of APTs is crucial as APTs are persistent and the damage caused to the system is more when the attacker spends more time in the system. We address the problem of detecting APTs and model the trade-off between resource efficiency and quickest detection of APTs. We propose a game model that captures the interaction of APT and a DIFT-based defender as a two-player, multi-stage, zero-sum, Stackelberg semi-Markov game. Our game considers the performance parameters such as false-negatives generated by DIFT and the time required for executing various operations in the system. We propose a two-time scale Q-learning algorithm that converges to a Stackelberg equilibrium under infinite horizon, limiting average payoff criteria. We validate our model and algorithm on a real-word attack dataset obtained using Refinable Attack INvestigation (RAIN) framework.

Dynamic Information Flow Tracking (DIFT) has been proposed to detect stealthy and persistent cyber attacks that evade existing defenses such as firewalls and signature-based antivirus systems. A DIFT defense taints and tracks suspicious information flows across the network in order to identify possible attacks, at the cost of additional memory overhead for tracking non-adversarial information flows. In this paper, we present the first analytical model that describes the interaction between DIFT and adversarial information flows, including the probability that the adversary evades detection and the performance overhead of the defense. Our analytical model consists of a multi-stage game, in which each stage represents a system process through which the information flow passes. We characterize the optimal strategies for both the defense and adversary, and derive efficient algorithms for computing the strategies. Our results are evaluated on a realworld attack dataset obtained using the Refinable Attack Investigation (RAIN) framework, enabling us to draw conclusions on the optimal adversary and defense strategies, as well as the effect of valid information flows on the interaction between adversary and defense.

Networked control systems consist of distributed sensors and actuators that communicate via a wireless network. The use of an open wireless medium and unattended deployment leaves these systems vulnerable to intelligent adversaries whose goal is to disrupt the system performance. In this paper, we study the wormhole attack on a networked control system, in which an adversary establishes a link between two geographically distant regions of the network by using either high-gain antennas, as in the out-of-band wormhole, or colluding network nodes as in the in-band wormhole. Wormholes allow the adversary to violate the timing constraints of real-time control systems by first creating low-latency links, which attract network traffic, and then delaying or dropping packets. Since the wormhole attack reroutes and replays valid messages, it cannot be detected using cryptographic mechanisms alone. We study the impact of the wormhole attack on the network flows and delays and introduce a passivity-based control-theoretic framework for modeling and mitigating the wormhole attack. We develop this framework for both the in-band and out-of-band wormhole attacks as well as complex, hereto-unreported wormhole attacks consisting of arbitrary combinations of in-and out-of band wormholes. By integrating existing mitigation strategies into our framework, we analyze the throughput, delay, and stability properties of the overall system. Through simulation study, we show that, by selectively dropping control packets, the wormhole attack can cause disturbances in the physical plant of a networked control system, and demonstrate that appropriate selection of detection parameters mitigates the disturbances due to the wormhole while satisfying the delay constraints of the physical system.

Networked control systems consist of distributed sensors and actuators that communicate via a wireless network. The use of an open wireless medium and unattended deployment leaves these systems vulnerable to intelligent adversaries whose goal is to disrupt the system performance. In this paper, we study the wormhole attack on a networked control system, in which an adversary establishes a link between two geographically distant regions of the network by using either high-gain antennas, as in the out-of-band wormhole, or colluding network nodes as in the in-band wormhole. Wormholes allow the adversary to violate the timing constraints of real-time control systems by first creating low-latency links, which attract network traffic, and then delaying or dropping packets. Since the wormhole attack reroutes and replays valid messages, it cannot be detected using cryptographic mechanisms alone. We study the impact of the wormhole attack on the network flows and delays and introduce a passivity-based control-theoretic framework for modeling and mitigating the wormhole attack. We develop this framework for both the in-band and out-of-band wormhole attacks as well as complex, hereto-unreported wormhole attacks consisting of arbitrary combinations of in-and out-of band wormholes. By integrating existing mitigation strategies into our framework, we analyze the throughput, delay, and stability properties of the overall system. Through simulation study, we show that, by selectively dropping control packets, the wormhole attack can cause disturbances in the physical plant of a networked control system, and demonstrate that appropriate selection of detection parameters mitigates the disturbances due to the wormhole while satisfying the delay constraints of the physical system.