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  1. Proceedings of the 1st International Workshop on Agents and CyberSecurity (ACySE '14)
  2. How to spend it: optimal investment for cyber security
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Identifying and modeling botnet C&C behaviors
SMS mobile botnet detection using a multi-agent system: research in progress
Playing hide-and-seek: an abstract game for cyber security
A memory efficient privacy preserving representation of connection graphs
Agent-based modeling of user circumvention of security
An agent-based framework for dynamical understanding of DNS events (DUDE)
The science of security: introduction from the perspective of secure collaboration
How to spend it: optimal investment for cyber security
Cyber-security challenges of agent technology in intelligent transportation systems
Using agents towards providing security on a context-aware architecture
Effects of network delays on swarming in a multi-agent security system

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How to spend it: optimal investment for cyber security

Content Provider ACM Digital Library
Author Malacaria, Pasquale Smeraldi, Fabrizio
Abstract A basic cyber security problem is how to optimally spend a security budget. We cast this problem in the framework of combinatorial optimization and explore its relationship with the classical knapsack problem. As in the latter, given a budget, we wish to optimally select a set of resources, each having a cost and a benefit. We propose optimisation algorithms that can deal with resources that depend non linearly on each other, and an optimal budget allocation algorithm for the case of several targets covered by target-specific resources. The general case of resources each of which benefits multiple targets leads to the multiple objective knapsack problem. Also in this case, we extend the standard dynamic programming solution to deal with non-linear dependencies between resources.
Starting Page 1
Ending Page 4
Page Count 4
File Format PDF
ISBN 9781450327282
DOI 10.1145/2602945.2602952
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-05-06
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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