Optimal Monitoring Problem Research Articles

Monitoring stealthy diffusion

A broad variety of problems, such as targeted marketing and the spread of viruses and malware, have been modeled as maximizing the reach of diffusion through a network. In cyber-security applications, however, a key consideration largely ignored in this literature is stealth. In particular, an attacker who has a specific target in mind succeeds only if the target is reached before the malicious payload is detected and corresponding countermeasures deployed. The dual side of this problem is deployment of a limited number of monitoring units, such as cyber-forensics specialists, to limit the success of such targeted and stealthy diffusion processes. We investigate the problem of optimal monitoring of targeted stealthy diffusion processes. While natural variants of this problem are NP-hard, we show that if stealthy diffusion starts from randomly selected nodes, the defender's objective is submodular and can be approximately optimized. In addition, we present approximation algorithms for the setting where the choice of the starting point is adversarial. We further extend our results to settings where the diffusion starts at multiple-seed nodes simultaneously, and where there is an inherent delay in detecting the infection. Our experimental results show that the proposed algorithms are highly effective and scalable.

A Payoff-Based Learning Approach to Cooperative Environmental Monitoring for PTZ Visual Sensor Networks

This paper addresses cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. In particular, we investigate the optimal monitoring problem whose objective function value is intertwined with the uncertain state of the physical world. In addition, due to the large volume of vision data, it is desired for each sensor to execute processing through local computation and communication. To address these issues, we present a distributed solution to the problem based on game theoretic cooperative control and payoff-based learning. At the first stage, a utility function is designed so that the resulting game constitutes a potential game with potential function equal to the group objective function, where the designed utility is shown to be computable through local image processing and communication. Then, we present a payoff-based learning algorithm so that the sensors are led to the global objective function maximizers without using any prior information on the environmental state. Finally, we run experiments to demonstrate the effectiveness of the present approach.

Fast Algorithms for Optimal Link Selection in Large-Scale Network Monitoring

The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size. We address the problem of optimal large-scale monitoring of computer networks under resource constraints. Specifically, we consider the task of selecting the “best” subset of at most <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> links to monitor, so as to optimally predict the traffic load at the remaining ones. Our notion of optimality is quantified in terms of the statistical error of network traffic predictors. The optimal monitoring problem at hand is akin to certain combinatorial constraints, which render the algorithms seeking the exact solution impractical. We develop a number of fast algorithms that improve upon existing algorithms in terms of computational complexity and accuracy. Our algorithms exploit the geometry of principal component analysis, which also leads us to new types of theoretical bounds on the prediction error. Finally, these algorithms are amenable to randomization, where the best of several parallel independent instances often yields the exact optimal solution. Their performance is illustrated and evaluated on simulated and real-network traces.