Effective Network Planning and Defending Strategies to Minimize Attackers’ Success Probabilities under Malicious and Epidemic Attacks
Date Issued
2011
Date
2011
Author(s)
Pan, Jia-ling
Abstract
Due to the Internet’s scalability and connectivity, enterprises and organizations increasingly rely upon the Internet to provide services and to engage in electronic commerce. On the other hand, attackers intelligently attack enterprises and organizations though continuous vulnerability exploitation and advanced attack strategies to achieve the goals of service interruption and/or theft of confidential information. Recently, many attackers apply the characteristics of fast propagation and infection of epidemic attacks to plan more deliberate strategies by using obtained network topology information. In order to deal with those special attacks, defenders may deploy detection nodes to achieve cooperatively detect unknown epidemic attacks and to generate/distribute signatures. In addition, defenders can activate several defense mechanisms to restrain propagation of epidemic attacks.
In this thesis, we model the attack-defense scenario as a mathematical programming problem where the attackers’ success probability is minimized. We first apply the Monte Carlo method to simulate a variety of attackers and corresponding strategies, and then apply the concept of relaxation-based method in mathematical programming. Through relaxing the budget related constraints and further generating corresponding multipliers, we can use them as directions of resource reallocation. In the above process, alternatively or alternatingly, we may also collect essential information accumulated during the course of simulations combined with the aforementioned multipliers as a more efficient method to enhancement the evaluation, which are then adopted to form a feasible direction in search for effective solutions.
In summary, in our research we take advantages of mathematical programming, which is precise, combine it with the Monte Carlo method, which is capable of handling complicated attackers’ strategies and behaviors under the condition of incomplete information, and adjust the defense strategies and resource allocation policies against malicious and epidemic attacks.
Subjects
Network Attack and Defense
Network Survivability
Optimization
Resource Allocation
Mathematical Programming
Monte Carlo Method
Lagrangian Relaxation
Epidemic Attacks
Worm
Incomplete Information
Type
thesis
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