ABSTRACT. The knowledge an attacker gathers about the critical infrastructure network they infiltrate allows them to customize the payload and remain undetected while causing maximum impact. This knowledge is a consequence of internal reconnaissance in the cyber network by lateral movement and is enabled by exploiting discovered vulnerabilities. This stage of the attack is also the longest, thereby giving a defender the biggest opportunity to detect and react to the attacker.

This paper helps a defender minimize the information an attacker might gain once in the network. This can be done by curbing lateral movement, misdirecting the attacker or inhibiting reachability to a critical device. We use a linear threshold models of attack propagation to analyze potential attack loss and use this to find actions that a defender might invest in while staying within their budgetary constraints. We show that while finding the best solution subject to these constraints is computationally intractable, the objective function is supermodular, allowing for a tractable technique with a known approximation bound.

[presentation video].

ABSTRACT. A session-based system provides various services to its end users through user interfaces. A novice user of a service's user interface takes more think time—the time to comprehend the content, and the layout of graphical elements, on the interface—in comparison to expert users.  The think time gradually decreases, as she repeatedly comprehends the same interface, over time. This decrease in think time is the user learning phenomenon. Owing to this learning behavior, the proportion of users—at various learning levels for different services—changes dynamically leading to a difference in the workload. Traditionally though, workload specifications (required for system performance evaluation) never accounted for user learning behavior. They generally assumed a global mean think time, instead. In this work, we propose a novel queueing network (QN) model called CogQN that accounts for user learning. It is a multi-class QN model where each service and its learning level constitute a class of users for the service. The model predicts overall mean response times across different learning modes within 10% error in comparison to empirical data.

[presentation video]. [preprint].

ABSTRACT. This paper presents a Matlab toolkit for the numerical analysis of the two-level processor sharing queue (2LPS). The job sizes are expressed in terms of acyclic phase type distributions which can approximate any distribution arbitrary well while arrivals occur according to a homogeneous Poisson process. The toolkit provides a simple yet efficient way to find the optimal parametrization of the 2LPS queueing disciplines given the job size distributions and the intensity of the workload. In practice, the tool can be used to configure the 2LPS scheduler for TCP flows. The time complexity of the solution depends on the cube of the number of phases of the distribution describing the flow sizes.

[presentation video]. [tool download].

ABSTRACT. Motivated by power-saving mechanisms in data centers, we propose an analytical method for an M/M/1 vacation queue with workload dependent service rates. We are able to obtain the distribution of the workload in the system. Based on our results, we consider a power-saving and performance trade-off problem. Numerical experiments reveal that square root service rate function has lower cost than that of linear and quadratic service functions.

[presentation video]. [preprint].

ABSTRACT. In the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time.

In this work, we translate a deterministic COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic’s dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODE-models. For instance, existence of super-spreaders might lead to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a much higher impact on the spreading dynamics than other characteristic values of the epidemic such as the basic reproduction number R₀.

We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights.

[presentation video]. [preprint].

ABSTRACT. Realistic state-based discrete-event simulation models are often quite complex. The complexity frequently manifests in models that (a) contain a large number of input variables whose values are difficult to determine precisely, and (b) take a relatively long time to solve.

Traditionally, models that have a large number of input variables whose values are not well-known are understood through the use of sensitivity analysis (SA) and uncertainty quantification (UQ). However, it can be prohibitively time consuming to perform SA and UQ.

In this work, we present a novel approach we developed for performing fast and thorough SA and UQ on a metamodel composed of a stacked ensemble of regressors that emulates the behavior of the base model. We demonstrate the approach using a previously published botnet model as a test case, showing that the metamodel approach is fast, accurate, and amenable to SA and UQ.

[presentation video].

ABSTRACT. The dynamic-fault-tree rare event simulator, DFTRES, is a statistical model checker for dynamic fault trees (DFTs), supporting the analysis of highly dependable systems, e.g. with unavailability or unreliability under 10⁻³⁰. To efficiently estimate such low probabilities, we apply the Path-ZVA algorithm to implement Importance Sampling with minimal user input. Calculation speed is further improved by selective automata composition and bisimulation reduction. DFTRES reads DFTs in the Galileo or JANI textual formats. The tool is written in Java 11 with multi-platform support, and it is released under the GPLv3. In this paper we describe the architecture, setup, and input language of DFTRES, and showcase its accurate estimation of dependability metrics of (resilient) repairable DFTs from the FFORT benchmark suite.

[presentation video]. [preprint]. [tool repository].

ABSTRACT. A graph game is a two-player zero-sum game in which the players move a token throughout a graph to produce an infinite path, which determines the winner or payoff of the game. In bidding games, both players have budgets, and in each turn, we hold an “auction” (bidding) to determine which player moves the token. In this talk, we consider several bidding mechanisms and study their effect on the properties of the game. Specifically, bidding games, and in particular bidding games of infinite duration, have an intriguing equivalence with random-turn games in which in each turn, the player who moves is chosen randomly. We show how minor changes in the bidding mechanism lead to unexpected differences in the equivalence with random-turn games.