Team for Research in
Ubiquitous Secure Technology

A Learning-Based Approach to Reactive Security
Benjamin Rubinstein

Citation
Benjamin Rubinstein. "A Learning-Based Approach to Reactive Security". Talk or presentation, 10, November, 2010.

Abstract
Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender's strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best _xed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker's incentives and knowledge.

Electronic downloads

Citation formats  
  • HTML
    Benjamin Rubinstein. <a
    href="http://www.truststc.org/pubs/767.html"
    ><i>A Learning-Based Approach to Reactive
    Security</i></a>, Talk or presentation,  10,
    November, 2010.
  • Plain text
    Benjamin Rubinstein. "A Learning-Based Approach to
    Reactive Security". Talk or presentation,  10,
    November, 2010.
  • BibTeX
    @presentation{Rubinstein10_LearningBasedApproachToReactiveSecurity,
        author = {Benjamin Rubinstein},
        title = {A Learning-Based Approach to Reactive Security},
        day = {10},
        month = {November},
        year = {2010},
        abstract = {Despite the conventional wisdom that proactive
                  security is superior to reactive security, we show
                  that reactive security can be competitive with
                  proactive security as long as the reactive
                  defender learns from past attacks instead of
                  myopically overreacting to the last attack. Our
                  game-theoretic model follows common practice in
                  the security literature by making worst-case
                  assumptions about the attacker: we grant the
                  attacker complete knowledge of the defender's
                  strategy and do not require the attacker to act
                  rationally. In this model, we bound the
                  competitive ratio between a reactive defense
                  algorithm (which is inspired by online learning
                  theory) and the best _xed proactive defense.
                  Additionally, we show that, unlike proactive
                  defenses, this reactive strategy is robust to a
                  lack of information about the attacker's
                  incentives and knowledge.},
        URL = {http://www.truststc.org/pubs/767.html}
    }
    

Posted by Larry Rohrbough on 7 Dec 2010.
Groups: trust
For additional information, see the Publications FAQ or contact webmaster at www truststc org.

Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.