2016/02/22 by Sailik Sengupta, Satya Gautam Vadlamudi, Sengupta, Sailik +12 · 1 voice · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information and Cyber Security #Multiagent Systems (cs.MA) #Network Security and Intrusion Detection #Web Application Security Vulnerabilities #cs.AI #cs.CR #cs.GT #cs.MA
paper · pdf · doi:10.48550/arxiv.1602.07024
9 pages, 4 figures
openalex publication_date 2016/02/22 · arxiv published 2016/02/23 · arxiv created 2016/11/17 · arxiv updated 2016/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose modeling of a real-world MTD web application as a repeated Bayesian game. We then formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To incorporate this model into a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.