John Musacchio

Sufficient rate constraints for QoS flows in ad-hoc networks

The capacity of an arbitrary ad-hoc network is difficult to estimate due to interference between the links. We use a conflict graph that models this interference relationship to determine if a set of flow rates can be accommodated. Using the cliques (complete subgraphs) of the conflict graph, we derive constraints that are sufficient for a set of flow rates to be feasible, yet are guaranteed to be within a constant bound of the optimal. We also compute an alternate set of sufficient constraints that can be easily derived from the rows of the matrix representation of the conflict graph. These two sets of constraints are particularly useful because their construction and verification may be distributed across the nodes of a network. We also extend the ad-hoc network model to incorporate variations in the interference range, and obstructions in the network.

Genetic adaptive control for an inverted wedge: experiments and comparative analyses

Abstract The inverted wedge is a planar robot with two degrees of freedom and a single control input (i.e., it is “underactuated”). The goal is to develop a digital controller that can balance the wedge in the inverted position by shifting a weight on the top of the wedge. Because it is underactuated and has complicated nonlinear dynamics, the inverted wedge is a good testbed for the development of nonconventional advanced control techniques and comparative analysis between control methods. We begin with the development of a nonlinear state feedback controller and direct and adaptive fuzzy controllers, that we will later use as a baseline comparison to show what type of performance is possible for this testbed. Control routines based on the GA have been found to apply to several practical applications in simulation and off-line optimization. Here, we will show that a GA can be used on-line in real-time to produce a particularly effective adaptive control method and this is the main contribution of this work. Computational and real-time implementation issues will be discussed and the genetic adaptive strategy will be compared with the state feedback and fuzzy control methods.

On the utility of run to run control in semiconductor manufacturing

Run to Run (RTR) control uses data from past process runs to adjust settings for the next run. By making better use of existing in-line metrology and actuation capabilities, RTR control offers the potential of reducing variability in manufacturing with minimal capital cost. In this paper, we survey the types of equipment models that can be used for RTR control, compare existing RTR control algorithms, and discuss issues affecting the potential utility of RTR control.

Adaptive Quality of Service for a Mobile Ad Hoc Network

This paper presents a QoS routing system for MANET supporting multiple traffic classes. The system takes into consideration clustering and channel allocation. Simulation experiments show that our algorithms are convergent. The system also yields a higher total throughput compared to the case in which every interface uses the same channel.

A Network Security Classification Game

We consider a network security classification game in which a strategic defender decides whether an attacker is a strategic spy or a naive spammer based on an observed sequence of attacks on file- or mail-servers. The spammer’s goal is attacking the mail-server, while the spy’s goal is attacking the file-server as much as possible before detection. The defender observes for a length of time that trades-off the potential damage inflicted during the observation period with the ability to reliably classify the attacker. Through empirical analyses, we find that when the defender commits to a fixed observation window, often the spy’s best response is either full-exploitation mode or full-confusion mode. This discontinuity prevents the existence of a pure Nash equilibrium in many cases. However, when the defender can condition the observation time based on the observed sequence, a Nash equilibrium often exists.

Computing the Nash Equilibria of Intruder Classification Games

We investigate the problem of classifying an intruder of two different types (spy or spammer). The classification is based on the number of file server and mail server attacks a network defender observes during a fixed window. The spammer naively attacks (with a known distribution) his main target: the mail server. The spy strategically selects the number of attacks on his main target: the file server. The defender strategically selects his classification policy: a threshold on the number of file server attacks. We first develop parameterized families of payoff functions for both players and analyze the Nash equilibria of the noncooperative nonzero-sum game. We analyze the strategic interactions of the two players and the tradeoffs each one of them faces: The defender chooses a classification threshold that balances the cost of missed detections and false alarms while the spy seeks to hit the file server as much as possible while still evading detection. We give a characterization of the Nash equilibria in mixed strategies, and demonstrate how the Nash equilibria can be computed in polynomial time. We give two examples of the general model, one that involves forensics on the side of the defender and one that does not. Finally, we evaluate how investments in forensics and data logging could improve the Nash equilibrium payoff of the defender.

Optimizing the decision to expel attackers from an information system

The conventional reaction after detecting an attacker in an information system is to expel the attacker immediately. However the attacker is likely to attempt to reenter the system, and if the attacker succeeds in reentering, it might take some time for the defenders intrusion detection system (IDS) to re-detect the attackers presence. In this interaction, both the attacker and defender are learning about each other — their vulnerabilities, intentions, and methods. Moreover, during periods when the attacker has reentered the system undetected, he is likely learning faster than the defender. The more the attacker learns, the greater the chance that he succeeds in his objective — whether it be stealing information, inserting malware, or some other objective. Conversely, the greater the defenders knowledge, the more likely that the defender can prevent the attacker from succeeding. In this setting, we study the defenders optimal strategy for expelling or not expelling an intruder. We find that the policy of always expelling the attacker can be far from optimal. Furthermore, by formulating the problem as a Markov decision process (MDP), we find how the optimal decision depends on the state variables and model parameters that characterize the IDSs detection rate and the attackers persistence.

The price of anarchy in competing differentiated services networks

We investigate competition between network providers that offer service to two types of traffic differing in their sensitivity to delay. We first consider competition amongst network providers who offer differentiated services by providing a priority queue for the delay sensitive traffic. We compare this to a situation in which all the competing network providers have network architectures that treat traffic of both types the same way. Our model of competition is Cournot in that service providers choose a rate to offer traffic of each type, and in-turn the total rate offered to each type of traffic determines the price of each traffic type. We are interested in the price of anarchy in these games of competition, which is defined as the ratio of the maximum achievable social utility versus the social utility attained when service providers selfishly maximize profits and reach a Nash equilibrium. We find that the price of anarchy is no more than 4/3 in our model of competing providers who offer differentiated services. In competition with providers that do not offer preferential service to delay sensitive traffic, we find the price of anarchy can be higher than 4/3, and we derive bounds for a number of important cases.

A botnet detection game

Botnets continue to constitute a major security threat to users of the internet. We examine a novel security game between a bot master and the legitimate users of the compromised network. The more a bot master utilizes his botnet, the more likely it is he will be detected by the legitimate users of the network. Thus he must balance stealth and aggression in his strategic utilization of the botnet. The legitimate users of the network must decide how vigilant they will be in trying to detect the presence of the botnet infection. We establish the existence of a unique, pure, symmetric Nash equilibrium in a game with homogeneous agents. Network effects are numerically explored in relation to the infectivity of the network.

A Game-Theoretic Analysis of Adversarial Classification

Attack detection is usually approached as a classification problem. However, standard classification tools often perform poorly, because an adaptive attacker can shape his attacks in response to the algorithm. This has led to the recent interest in developing methods for adversarial classification, but to the best of our knowledge, there have been a very few prior studies that take into account the attacker’s tradeoff between adapting to the classifier being used against him with his desire to maintain the efficacy of his attack. Including this effect is a key to derive solutions that perform well in practice. In this investigation, we model the interaction as a game between a defender who chooses a classifier to distinguish between attacks and normal behavior based on a set of observed features and an attacker who chooses his attack features (class 1 data). Normal behavior (class 0 data) is random and exogenous. The attacker’s objective balances the benefit from attacks and the cost of being detected while the defender’s objective balances the benefit of a correct attack detection and the cost of false alarm. We provide an efficient algorithm to compute all Nash equilibria and a compact characterization of the possible forms of a Nash equilibrium that reveals intuitive messages on how to perform classification in the presence of an attacker. We also explore qualitatively and quantitatively the impact of the non-attacker and underlying parameters on the equilibrium strategies.