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  1. Proceedings of the 2009 workshop on New security paradigms workshop (NSPW '09)
  2. A reinforcement model for collaborative security and Its formal analysis
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Laissez-faire file sharing: access control designed for individuals at the endpoints
What is the shape of your security policy?: security as a classification problem
Generative usability: security and user centered design beyond the appliance
Quis Custodiet ipsos Custodes?: a new paradigm for analyzing security paradigms with appreciation to the Roman poet Juvenal
Musipass: authenticating me softly with "my" song
A reinforcement model for collaborative security and Its formal analysis
Fluid information systems
So long, and no thanks for the externalities: the rational rejection of security advice by users
Server-side detection of malware infection
Quantified security is a weak hypothesis: a critical survey of results and assumptions
The sisterhood of the traveling packets
Securing data through avoidance routing

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A reinforcement model for collaborative security and Its formal analysis

Content Provider ACM Digital Library
Author Saha, Indranil Misra, Janardan
Abstract This paper presents a principled approach to one of the many little studied aspects of computer security which relate to human behavior. Advantages of involving users who usually have strong analytic ability to detect violations and threats but not primarily responsible for security have been well emphasized in the literature. In this work we propose a reinforcement framework for enabling collaborative monitoring of policy violations by the users. We define a payoff model to formalize the reinforcement framework. The model stipulates appropriate payoffs as reward, punishment, and community price according to reporting of genuine or false violations, non-reporting of the detected violations, and proactive reporting of vulnerabilities and threats by the users. We define probabilistic robustness property of the resulting system and constraints for economic feasibility of the payoffs. For estimating the parameters in the payoff model, system and user behaviors are modeled in terms of probabilistic finite state machines (PFSM) and likelihood of the success of the model is specified using Probabilistic Computation Tree Logic (PCTL). PRISM model checker based automated quantitative analysis elicits the process of the estimation of various parameters in the model using PFSMs and PCTL formulas.
Starting Page 101
Ending Page 114
Page Count 14
File Format PDF
ISBN 9781605588452
DOI 10.1145/1719030.1719045
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-09-08
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Collaborative monitoring and reporting Modeling and analysis Reward-punishment rramework Probabilistic Reinforcement model Collaborative security
Content Type Text
Resource Type Article
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