The infrastructure of modern society is controlled by software systems that are vulnerable to attacks. Many such attacks, launched by "recreational hackers" have already led to severe disruptions and significant cost. It, therefore, is critical that we find ways to protect such systems and to enable them to continue functioning even after a successful attack. This article describes AWDRAT, a prototype middleware system for providing survivability to both new and legacy applications. AWDRAT stands for architectural differencing, wrappers, diagnosis, recovery, adaptive software, and trust modeling. AWDRAT uses these techniques to gain visibility into the execution of an application system and to compare the application's actual behavior to that which is expected. In the case of a deviation, AWDRAT conducts a diagnosis that determines which computational resources are likely to have been compromised and then adds these assessments to its trust model. The trust model in turn guides the recovery process, particularly by guiding the system in its choice among functionally equivalent methods and resources.AWDRAT has been applied to and evaluated on an example application system, a graphical editor for constructing mission plans. We describe a series of experiments that were performed to test the effectiveness of AWDRAT in recognizing and recovering from simulated attacks, and we present data showing the effectiveness of AWDRAT in detecting a variety of compromises to the application system (approximately 90 percent of all simulated attacks are detected, diagnosed, and corrected). We also summarize some lessons learned from the AWDRAT experiments and suggest approaches for comprehensive application protection methods and techniques.

Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-based approach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD is too large and hence the mechanism must consider approximations. This raises the possibility that agents may be able to exploit the approximation for selfish gain. We adopt sparse-sampling-based MDP algorithms to implement ɛ- efficient policies, and retain truth-revelation as an approximate Bayesian-Nash equilibrium. Our approach is empirically illustrated in the context of the dynamic allocation of WiFi connectivity to users in a coffeehouse.

Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-based approach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD is too large and hence the mechanism must consider approximations. This raises the possibility that agents may be able to exploit the approximation for selfish gain. We adopt sparse-sampling-based MDP algorithms to implement ɛ- efficient policies, and retain truth-revelation as an approximate Bayesian-Nash equilibrium. Our approach is empirically illustrated in the context of the dynamic allocation of WiFi connectivity to users in a coffeehouse.

Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-basedapproach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD is too large and hence the mechanism must consider approximations. This raises the possibility thatagents may be able to exploit the approximation for selfish gain. We adopt sparse-sampling-based MDP algorithms to implement ɛ- efficient policies, and retain truth-revelation as an approximate Bayesian-Nash equilibrium. Our approach is empirically illustrated in the context of the dynamic allocation of WiFi connectivity to users in a coffeehouse.

Online mechanism design (MD) considers the problem of providing incentives to implement desired system-wide outcomes in systems with self-interested agents that arrive and depart dynamically. Agents can choose to misrepresent their arrival and departure times, in addition to information about their value for different outcomes. We consider the problem of maximizing the total longterm value of the system despite the self-interest of agents. The online MD problem induces a Markov Decision Process (MDP), which when solved can be used to implement optimal policies in a truth-revealing Bayesian-Nash equilibrium.

Online mechanism design (MD) considers the problem of providing incentives to implement desired system-wide outcomes in systems with self-interested agents that arrive and depart dynamically. Agents can choose to misrepresent their arrival and departure times, in addition to information about their value for different outcomes. We consider the problem of maximizing the total longterm value of the system despite the self-interest of agents. The online MD problem induces a Markov Decision Process (MDP), which when solved can be used to implement optimal policies in a truth-revealing Bayesian-Nash equilibrium.

Online mechanism design (MD) considers the problem of providing incentivesto implement desired system-wide outcomes in systems withself-interested agents that arrive and depart dynamically. Agentscan choose to misrepresent their arrival and departure times, in addition to information about their value for different outcomes. We consider the problem of maximizing the total longterm valueof the system despite the self-interest of agents. The online MD problem induces a Markov Decision Process (MDP), which when solved can be used to implement optimal policies in a truth-revealing Bayesian-Nash equilibrium.

Population-based Incremental Learning is shown require very sensitive scaling of its learning rate. The learning rate must scale with the system size in a problem-dependent way. This is shown in two problems: the needle-in-a haystack, in which the learning rate must vanish exponentially in the system size, and in a smooth function in which the learning rate must vanish like the square root of the system size. Two methods are proposed for removing this sensitivity. A learning dynamics which obeys detailed balance is shown to give consistent performance over the entire range of learning rates. An analog of mutation is shown to require a learning rate which scales as the inverse system size, but is problem independent.

Population-based Incremental Learning is shown require very sensitive scaling of its learning rate. The learning rate must scale with the system size in a problem-dependent way. This is shown in two problems: the needle-in-a haystack, in which the learning rate must vanish exponentially in the system size, and in a smooth function in which the learning rate must vanish like the square root of the system size. Two methods are proposed for removing this sensitivity. A learning dynamics which obeys detailed balance is shown to give consistent performance over the entire range of learning rates. An analog of mutation is shown to require a learning rate which scales as the inverse system size, but is problem independent.

Population-based Incremental Learning is shown require very sensitive scalingof its learning rate. The learning rate must scale with the system size in a problem-dependent way. This is shown in two problems: the needle-in-a haystack, in which the learning rate must vanish exponentially in the system size, and in a smooth function in which the learning rate must vanish like the square root of the system size. Two methods are proposed for removing this sensitivity. Alearning dynamics which obeys detailed balance is shown to give consistent performance over the entire range of learning rates. An analog of mutation is shown to require a learning rate which scales as the inverse system size, but is problem independent.