Moving target defense for adaptive adversaries
Title | Moving target defense for adaptive adversaries |
Publication Type | Conference Paper |
Year of Publication | 2013 |
Authors | Colbaugh, R., Glass, K. |
Conference Name | Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on |
Date Published | June |
Keywords | adaptive adversaries, algorithm performance degradation, Biological system modeling, cyber security, flexible MT defense strategy, Games, hybrid dynamical systems, innocent activities, learning (artificial intelligence), machine learning, malicious activities, ML algorithms, moving target defense, performance evaluation, reverse engineering, reverse-engineer, security, security applications, security of data, security problems, standard static methods, Switches, Training, Unsolicited electronic mail |
Abstract | Machine learning (ML) plays a central role in the solution of many security problems, for example enabling malicious and innocent activities to be rapidly and accurately distinguished and appropriate actions to be taken. Unfortunately, a standard assumption in ML - that the training and test data are identically distributed - is typically violated in security applications, leading to degraded algorithm performance and reduced security. Previous research has attempted to address this challenge by developing ML algorithms which are either robust to differences between training and test data or are able to predict and account for these differences. This paper adopts a different approach, developing a class of moving target (MT) defenses that are difficult for adversaries to reverse-engineer, which in turn decreases the adversaries' ability to generate training/test data differences that benefit them. We leverage the coevolutionary relationship between attackers and defenders to derive a simple, flexible MT defense strategy which is optimal or nearly optimal for a broad range of security problems. Case studies involving two distinct cyber defense applications demonstrate that the proposed MT algorithm outperforms standard static methods, offering effective defense against intelligent, adaptive adversaries. |
URL | http://ieeexplore.ieee.org/document/6578785/ |
DOI | 10.1109/ISI.2013.6578785 |
Citation Key | 6578785 |
- moving target defense
- Unsolicited electronic mail
- Training
- Switches
- standard static methods
- security problems
- security of data
- security applications
- security
- reverse-engineer
- reverse engineering
- performance evaluation
- adaptive adversaries
- ML algorithms
- malicious activities
- machine learning
- learning (artificial intelligence)
- innocent activities
- hybrid dynamical systems
- Games
- flexible MT defense strategy
- cyber security
- Biological system modeling
- algorithm performance degradation