2019 Proceeding- General Chair:
- Zhuo Lu
It is my great pleasure to welcome you to the 2019 ACM Workshop on Moving Target Defense - MTD 2019. Putting together MTD'19 was a team effort. On behalf of the program committee, we first thank the authors for providing the content of the program. We thank the ACM publication staff for their help during the publication of the workshop proceedings. Finally, I would like to thank the reviewers and committee members for their hard work in reviewing the paper submissions to ensure the quality of the workshop.
As you may know, the MTD'19 workshop is the 6-th edition of the ACM moving target defense (MTD) workshop. The idea of moving target defense is to impose the same asymmetric disadvantage on attackers by making systems dynamic and therefore harder to explore and predict. In today's computer and network systems, adversaries do have an asymmetric advantage over the defender of a system because they have sufficient time to observe and learn the behavior of the system, identify possible vulnerabilities, and only attack the system when the attacker gains sufficient knowledge about the system and the defender. MTD is able to consistently change the configurations, settings, and parameters of the system such that the observed system behavior is hard to predict by the attacker. This creates a significant uncertainty to the attacker and brings benefits to the defender. The ultimate goal of MTD is to increase the attackers' workload so as to level the cybersecurity playing field for defenders and attackers - ultimately tilting it in favor of the defender. We received 15 submissions, of which we accepted 8 papers. One keynote speaker is invited to discuss the research challenges and summary of MTD as well as the speaker's vision about future research directions. The other sessions are for full research paper presentation, featuring MTD in software systems, MTD in networking applications, as well as modeling and analysis of MTD.
Proceeding Downloads
On the Resilience of Network-based Moving Target Defense Techniques Against Host Profiling Attacks
Researchers propose Moving Target Defense (MTD) strategies for networking infrastructures as a countermeasure to impede attackers from identifying and exploiting vulnerable network hosts. In this paper, we investigate the weaknesses of Network-based ...
Specification-driven Moving Target Defense Synthesis
Cyber agility enables cyber systems to defend proactively against sophisticated attacks by dynamically changing the system configuration parameters (called mutable parameters) in order to deceive adversaries from reaching their goals, disrupt the attack ...
Bayesian Stackelberg Game for Risk-aware Edge Computation Offloading
Mobile Edge Computing (MEC) is delivering a rich portfolio of computation services to resource-constrained mobile devices, enabling ultra-low latency and location-awareness for the emerging mobile applications. However, the vulnerability of this new ...
A Scalable High Fidelity Decoy Framework against Sophisticated Cyber Attacks
Recent years have witnessed a surging trend of leveraging deception technique to detect and defeat sophisticated cyber attacks such as the advanced persistent threat. Deception typically employs a decoy network to entrap the attackers and divert the ...
Run or Hide? Both! A Method Based on IPv6 Address Switching to Escape While Being Hidden
An increasing number of devices of our everyday life are referred to as connected objects. Most of them need an Internet connection, and are thus provided with a public IP address. With these IP addresses come new security threats as attackers may ...
A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense
Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that ...
A Collaborative Strategy for Mitigating Tracking through Browser Fingerprinting
Browser fingerprinting is a technique that collects information about the browser configuration and the environment in which it is running. This information is so diverse that it can partially or totally identify users online. Over time, several ...
Should I (re)Learn or Should I Go(on)?: Stream Machine Learning for Adaptive Defense against Network Attacks
Continuous, dynamic and short-term learning is an effective learning strategy when operating in dynamic and adversarial environments, where concept drift constantly occurs and attacks rapidly change over time. In an on-line, stream learning model, data ...