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2020-02-18
Dishington, Cole, Sharma, Dilli P., Kim, Dong Seong, Cho, Jin-Hee, Moore, Terrence J., Nelson, Frederica F..  2019.  Security and Performance Assessment of IP Multiplexing Moving Target Defence in Software Defined Networks. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :288–295.

With the interconnection of services and customers, network attacks are capable of large amounts of damage. Flexible Random Virtual IP Multiplexing (FRVM) is a Moving Target Defence (MTD) technique that protects against reconnaissance and access with address mutation and multiplexing. Security techniques must be trusted, however, FRVM, along with past MTD techniques, have gaps in realistic evaluation and thorough analysis of security and performance. FRVM, and two comparison techniques, were deployed on a virtualised network to demonstrate FRVM's security and performance trade-offs. The key results include the security and performance trade-offs of address multiplexing and address mutation. The security benefit of IP address multiplexing is much greater than its performance overhead, deployed on top of address mutation. Frequent address mutation significantly increases an attackers' network scan durations as well as effectively obfuscating and hiding network configurations.

2019-03-22
Alavizadeh, H., Jang-Jaccard, J., Kim, D. S..  2018.  Evaluation for Combination of Shuffle and Diversity on Moving Target Defense Strategy for Cloud Computing. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :573-578.

Moving Target Defence (MTD) has been recently proposed and is an emerging proactive approach which provides an asynchronous defensive strategies. Unlike traditional security solutions that focused on removing vulnerabilities, MTD makes a system dynamic and unpredictable by continuously changing attack surface to confuse attackers. MTD can be utilized in cloud computing to address the cloud's security-related problems. There are many literature proposing MTD methods in various contexts, but it still lacks approaches to evaluate the effectiveness of proposed MTD method. In this paper, we proposed a combination of Shuffle and Diversity MTD techniques and investigate on the effects of deploying these techniques from two perspectives lying on two groups of security metrics (i) system risk: which is the cloud providers' perspective and (ii) attack cost and return on attack: which are attacker's point of view. Moreover, we utilize a scalable Graphical Security Model (GSM) to enhance the security analysis complexity. Finally, we show that combining MTD techniques can improve both aforementioned two groups of security metrics while individual technique cannot.