Optimal cost, collaborative, and distributed response to zero-day worms - A control theoretic approach

0Citations
Citations of this article
12Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Collaborative environments present a happy hunting ground for worms due to inherent trust present amongst the peers. We present a novel control-theoretic approach to respond to zero-day worms in a signature independent fashion in a collaborative environment. A federation of collaborating peers share information about anomalies to estimate the presence of a worm and each one of them independently chooses the most cost-optimal response from a given set of responses. This technique is designed to work when the presence of a worm is uncertain. It is unique in that the response is dynamic and self-regulating based on the current environment conditions. Distributed Sequential Hypothesis Testing is used to estimate the extent of worm infection in the environment. Response is formulated as a Dynamic Programming problem with imperfect state information. We present a solution and evaluate it in the presence of an Internet worm attack for various costs of infections and response. A major contribution of this paper is analytically formalizing the problem of optimal and cost-effective response to worms. The second contribution is an adaptive response design that minimizes the variety of worms that can be successful. This drives the attacker towards kinds of worms that can be detected by other means; which in itself is a success. Counter-intutive results such as leaving oneself open to infections being the cheapest option in certain scenarios become apparent with our response model. © 2008 Springer-Verlag Berlin Heidelberg.

Cite

CITATION STYLE

APA

Cheetancheri, S. G., Agosta, J. M., Levitt, K. N., Wu, F., & Rowe, J. (2008). Optimal cost, collaborative, and distributed response to zero-day worms - A control theoretic approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5230 LNCS, pp. 231–250). https://doi.org/10.1007/978-3-540-87403-4_13

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free