The Message Queuing Telemetry Transport (MQTT) protocol is a cornerstone of IoT communications. It relies on service brokers to enable reliable data delivery between devices and clients. In modern deployments, MQTT brokers are frequently hosted in virtualized environments to support scalability, flexibility, and resource efficiency. However, virtualization enlarges the attack surface, posing challenges to service reliability and security. This paper investigates the use of Virtual Machine (VM) migration as a Moving Target Defense (MTD) to enhance security in MQTT-based IoT services. While VM migration is an established technique in network and service management for workload balancing and fault tolerance, its impact, when used as a proactive security mechanism in MQTT deployments, remains unexplored. This work shows a comprehensive performance evaluation of VM migration under both normal and active attack scenarios. The results demonstrate that the security benefits of VM migration come with minimal performance degradation, characterized by a modest effect size (Cohen D measure<0.5), thus ensuring service continuity and operational stability. However, it comes with a cost of increased performance oscillation (i.e., higher incidences of peaks in the response time). This paper also introduces an interactive, web-based tool that enables pre-deployment MTD simulation. This work offers insights into integrating security-aware VM migration within IoT service management.

Moving Target Defense (MTD) consists of applying dynamic reconfiguration in the defensive side of the attack-defense cybersecurity game. Virtual Machine (VM) migration could be used as MTD against specific host-based attacks in the cloud computing environment by remapping the distribution of VMs in the existing physical hosts. This way, when the attacker’s VM is moved to a different machine, the attack has to be restarted. However, one significant gap here is how to select a proper VM migration-based MTD schedule to reach the desired levels of system protection. This paper develops a Stochastic Petri Net (SPN) model to address this issue. The model leverages empirical knowledge about the dynamics of the attack defense in a VM migration-enabled setup. First, we present the results of an experimental campaign to acquire knowledge about the system’s behavior. The experiments provide insights for the model design. Then, based on the model, we propose a tool named PyMTDEvaluator , which provides a graphical interface that serves as a wrapper for the simulation environment of the model. Finally, we exercise the tool using Multi-Criteria Decision-Making methods to aid the MTD policy selection. Hopefully, our results and methods will be helpful for system managers and cybersecurity professionals. • Comprehensive experimental results of VM migration as MTD against Memory DoS. • Availability and security Stochastic Petri Net of a system with VM migration as MTD. • An open-source simulation tool providing a graphical interface for the model. • Multi-Criteria Decision-Making to support MTD selection based on user preferences.

The use of Virtual Machine (VM) migration as support for software rejuvenation was introduced more than a decade ago. Since then, several works have validated this approach from experimental and theoretical perspectives. Recently, some works shed light on the possibility of using the same technique as Moving Target Defense (MTD). However, to date, no work evaluated the availability and security levels while applying VM migration for both rejuvenation and MTD (multipurpose VM migration). In this paper, we conduct a comprehensive evaluation using Stochastic Petri Net (SPN) models to tackle this challenge. The evaluation covers the steady-state system availability, expected MTD protection, and related metrics of a system under time-based multipurpose VM migration. Results show that the availability and security improvement due to VM migration deployment surpasses 50% in the best scenarios. However, there is a trade-off between availability and security metrics, meaning that improving one implies compromising the other.

Memory Denial of Service (DoS) attacks are easy-to-launch, hard to detect, and significantly impact their targets. In memory DoS, the attacker targets the memory of his Virtual Machine (VM) and, due to hardware isolation issues, the attack affects the co-resident VMs. Theoretically, we can deploy VM migration as Moving Target Defense (MTD) against memory DoS. However, the current literature lacks empirical evidence supporting this hypothesis. Moreover, there is a need to evaluate how the VM migration timing impacts the potential MTD protection. This practical experience report presents an experiment on VM migration-based MTD against memory DoS. We evaluate the impact of memory DoS attacks in the context of two applications running in co-hosted VMs: machine learning and OLTP. The results highlight that the memory DoS attacks lead to more than 70% reduction in the applications' performance. Nevertheless, timely VM migrations can significantly mitigate the attack effects in both considered applications.

This paper presents PyMTDEvaluator, a tool for evaluating the effectiveness of time-based Moving Target Defense (MTD) against availability attacks (e.g., Denial of Service - DoS, resource starvation attacks). PyMTDEvaluator is based on simulation runs of an extended deterministic Stochastic Petri Net (SPN) and offers a user-friendly interface where it is possible to analyze and compare MTD policies with different parameters. The SPN design relies on knowledge obtained from empirical observation. PyMTDEvaluator provides results such as probability of attack success, availability, and system capacity to support MTD design decision making. The tool allows analyzing and comparing several scenarios in the same evaluation, thus enabling the study of the pros and cons of different MTD deployment alternatives. PyMTDEvaluator aims to be part of the toolset for MTD policies design. It is also valuable for sensitivity analysis of MTD-enabled system parameters.

Moving Target Defense (MTD) is a defensive mechanism based on dynamic system reconfiguration to prevent or thwart cyberattacks. In the last years, considerable progress has been made regarding MTD approaches for virtualized environments, and Virtual Machine (VM) migration is the core of most of these approaches. However, VM migration produces system downtime, meaning that each MTD reconfiguration affects system availability. Therefore, a method for a combined evaluation of availability and security is of utmost importance for VM migration-based MTD design. In this paper, we propose a Stochastic Reward Net (SRN) for the probability of attack success and availability evaluation of an MTD based on VM migration scheduling. We study the MTD system under different conditions regarding 1) VM migration scheduling, 2) VM migration failure probability, and 3) attack success rate. Our results highlight the tradeoff between availability and security when applying MTD based on VM migration. The approach and results may provide inputs for designing and evaluating MTD policies based on VM migration.

Cloud computing is a new paradigm that provides services through the Internet. Such paradigm has the influence of the previous available technologies (e.g., cluster, peer-to-peer and grid computing) and has been adopted to reduce costs, to provide flexibility and to make management easier. Companies like Google, Amazon, Microsoft, IBM, HP, Yahoo, Oracle, and EMC have conducted significant investments on cloud infrastructure to provide services with high availability levels. The advantages of cloud computing allowed the construction of digital libraries that represent collections of information. This system demands high reliability and studies regarding analysis of availability are important due to the relevance of conservation and dissemination of the scientific and literature information. This paper proposes an approach to model and evaluate the availability of a digital library. A case study is conducted to show the applicability of the proposed approach. The obtained results are useful for the design of this system since missing data can lead to various errors and incalculable losses.

With the increasing adoption of cloud computing environments, studies about high availability in those systems became more and more significant. Software rejuvenation is an important mechanism to improve system availability. This paper presents a comprehensive availability model to evaluate the utilization of the live migration mechanism to enable VMM rejuvenation with minimum service interruption. Live migrations are performed observing a time-based trigger. We evaluate five different scenarios, with distinct time intervals for triggering the rejuvenation. The results show that the live migration can significantly reduce the system downtime.

Software aging refers to performance degradation faced on long-running execution software. The software infrastructure supporting Cloud Computing systems may suffer from aging effects. Such a phenomenon was already reported for virtual infrastructure managers and virtual machine monitors. In this sense, software rejuvenation is a technique of proactive fault tolerance to deal with effects of software aging. This paper proposes a comprehensive availability model for two different types of rejuvenation scheduling based on live migration mechanism, one with a test before migration, and other without. We evaluate five different scenarios, with distinct time intervals for triggering the rejuvenation. The main goal is to explain the benefits gained from the use of this type of rejuvenation technique, as well as understand the differences between the two approaches analyzed. The results show that the utilization of a schedule with a checking mechanism before rejuvenation can bring a significant improvement on system availability.