Biblio

As one of the effective methods to enhance traffic safety and improve traffic efficiency, the Internet of vehicles has attracted wide attention from all walks of life. V2X secure communication, as one of the research hotspots of the Internet of vehicles, also has many security and privacy problems. Attackers can use these vulnerabilities to obtain vehicle identity information and location information, and can also attack vehicles through camouflage.Therefore, the identity authentication process in vehicle network communication must be effectively protected. The anonymous identity authentication scheme based on moving target defense proposed in this paper not only ensures the authenticity and integrity of information sources, but also avoids the disclosure of vehicle identity information.

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.

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.

Deep learning (DL) models have been shown to be vulnerable to adversarial attacks. DL model security against adversarial attacks is critical to using DL-trained models in forward deployed systems, e.g. facial recognition, document characterization, or object detection. We provide results and lessons learned applying a moving target defense (MTD) strategy against iterative, gradient-based adversarial attacks. Our strategy involves (1) training a diverse ensemble of DL models, (2) applying randomized affine input transformations to inputs, and (3) randomizing output decisions. We report a primary lesson that this strategy is ineffective against a white-box adversary, which could completely circumvent output randomization using a deterministic surrogate. We reveal how our ensemble models lacked the diversity necessary for effective MTD. We also evaluate our MTD strategy against a black-box adversary employing an ensemble surrogate model. We conclude that an MTD strategy against black-box adversarial attacks crucially depends on lack of transferability between models.

Internet of Things (IoT) revolutionizes cutting-edge technologies by enabling smart sensing, and actuation of the physical world. IoT enables cooperation between numerous heterogeneous smart devices to exchange and aggregate data from the surrounding environment through the internet. Recently, the range of IoT technology could be utilized in the real world by the rapid spread of sensor devices. These capabilities open the door for vital challenges. Security is the major challenge that faces the IoT networks. Traditional solutions cannot tackle smart and powerful attackers. Moving Target Defense (MTD) deploys mechanisms and strategies that increase attackers' uncertainty and frustrate their attempt to eavesdrop the target to be protected. In addition, Steganography is the practice of concealing a message within another message. For security proposes, Steganography is used to hide significant data within any transmitted messages, such as images, videos, and text files. This paper presents Stegano-MTD framework that enables combination between MTD mechanisms with steganography. This combination offers a lightweight solution that can be implemented on the IoT network. Stegano-MTD slices the message into small labeled chunks and sends them randomly through the network's nodes. Steganography is used for hide the key file that used to reconstruct the original data. Simulation results show the effectiveness of the presented solution.

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.

Space systems provide many critical functions to the military, federal agencies, and infrastructure networks. Nation-state adversaries have shown the ability to disrupt critical infrastructure through cyber-attacks targeting systems of networked, embedded computers. Moving target defenses (MTDs) have been proposed as a means for defending various networks and systems against potential cyber-attacks. MTDs differ from many cyber resilience technologies in that they do not necessarily require detection of an attack to mitigate the threat. We devised a MTD algorithm and tested its application to a real-time network. We demonstrated MTD usage with a real-time protocol given constraints not typically found in best-effort networks. Second, we quantified the cyber resilience benefit of MTD given an exfiltration attack by an adversary. For our experiment, we employed MTD which resulted in a reduction of adversarial knowledge by 97%. Even when the adversary can detect when the address changes, there is still a reduction in adversarial knowledge when compared to static addressing schemes. Furthermore, we analyzed the core performance of the algorithm and characterized its unpredictability using nine different statistical metrics. The characterization highlighted the algorithm has good unpredictability characteristics with some opportunity for improvement to produce more randomness.

The digital transformation is injecting energy into economic growth and governance improvement for the China government. Digital governance and e-government services are playing a more and more important role in public management and social governance. Meanwhile, cyber-attacks and threats become the major challenges for e-government application systems. In this paper, we proposed a novel dynamic access entry scheme for web application, which provide a rapidly-changing defender-controlled attack surface based on Moving Target Defense (MTD) technology. The scheme can turn the static keywords of Uniform Resource Locator (URL) into the dynamic and random ones, which significantly increase the cost to adversaries attack. We present the prototype of the proposed scheme and evaluate the feasibility and effectiveness. The experimental results demonstrated the scheme is practical and effective.

Advanced persistent threat (APT) attackers usually conduct a large number of network reconnaissance before a formal attack to discover exploitable vulnerabilities in the target network and system. The static configuration in traditional network systems provides a great advantage for adversaries to find network targets and launch attacks. To reduce the effectiveness of adversaries' continuous reconnaissance attacks, this paper develops a moving target defense (MTD) enhanced cyber deception defense system based on software-defined networks (SDN). The system uses virtual network topology to confuse the target network and system information collected by adversaries. Also Besides, it uses IP address randomization to increase the dynamics of network deception to enhance its defense effectiveness. Finally, we implemented the system prototype and evaluated it. In a configuration where the virtual network topology scale is three network segments, and the address conversion cycle is 30 seconds, this system delayed the adversaries' discovery of vulnerable hosts by an average of seven times, reducing the probability of adversaries successfully attacking vulnerable hosts by 83%. At the same time, the increased system overhead is basically within 10%.

In order to suppress the spread of zero-day virus in the network effectively, a zero-day virus suppression strategy was proposed. Based on the mechanism of zero-day virus transmission and the idea of platform dynamic defense, the corresponding methods of virus transmission suppression are put forward. By changing the platform switching frequency, the scale of zero-day virus transmission and its inhibition effect are simulated in a small-world network model. Theory and computer simulation results show that the idea of platform switching can effectively restrain the spread of virus.

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.

With the advancement of network technology, more electronic devices have begun to connect to the Internet. The era of IoE (Internet of Everything) is coming. However, the number of serious incidents of cyberattacks on important facilities has gradually increased at the same time. Security becomes an important issue when setting up plenty of network devices in an environment. Thus, we propose an innovative mechanism of the Moving Target Defense (MTD) to solve the problems happening to other MTD mechanisms in the past. This method applies Dynamic Host Configuration Protocol (DHCP) to dynamically change the IPv4 address of information equipment in the medical environment. In other words, each of the nodes performs IP-Hopping and effectively avoids malicious attacks. Communication between devices relies on DNS lookup. The mechanism avoids problems such as time synchronization and IP conflict. Also, it greatly reduces the costs of large-scale deployment. All of these problems are encountered by other MTD mechanisms in the past. Not only can the mechanism be applied to the medical and information equipment, it can also be applied to various devices connected to the Internet, including Industrial Control System (ICS). The mechanism is implemented in existing technologies and prevents other problems, which makes it easy to build a system.

Distributed denial of service attacks are still one of the greatest threats for computer systems and networks. We propose an intelligent moving target solution against DDOS flooding attacks. Our solution will use a fast-flux approach combined with moving target techniques to increase attack cost and complexity by bringing dynamics and randomization in network address space. It continually increases attack costs and makes it harder and almost infeasible for botnets to launch an attack. Along with performing selective proxy server replication and shuffling clients among this proxy, our solution can successfully separate and isolate attackers from benign clients and mitigate large-scale and complex flooding attacks. Our approach effectively stops both network and application-layer attacks at a minimum cost. However, while we try to make prevalent attack launches difficult and expensive for Bot Masters, this approach is good enough to combat zero-day attacks, too. Using DNS capabilities to change IP addresses frequently along with the proxy servers included in the proposed architecture, it is possible to hide the original server address from the attacker and invalidate the data attackers gathered during the reconnaissance phase of attack and make them repeat this step over and over. Our simulations demonstrate that we can mitigate large-scale attacks with minimum possible cost and overhead.

Due to the constrained resource and computational limitation of many Internet of Things (IoT) devices, conventional security protections, which require high computational overhead are not suitable to be deployed. Thus, vulnerable IoT devices could be easily exploited by attackers to break into networks. In this paper, we employ cyber deception and moving target defense (MTD) techniques to proactively change the network topology with both real and decoy nodes with the support of software-defined networking (SDN) technology and investigate the impact of single-target and multi-target attacks on the effectiveness of the integrated mechanism via a hierarchical graphical security model with security metrics. We also implement a web-based visualization interface to show topology changes with highlighted attack paths. Finally, the qualitative security analysis is performed for a small-scale and SDN-supported IoT network with different combinations of decoy types and levels of attack intelligence. Simulation results show the integrated defense mechanism can introduce longer mean-time-to-security-failure and larger attack impact under the multi-target attack, compared with the single-target attack model. In addition, adaptive shuffling has better performance than fixed interval shuffling in terms of a higher proportion of decoy paths, longer mean-time-to-security-failure and largely reduced defense cost.

The rapid developments of mobile communication and wearable devices greatly improve our daily life, while the massive entities and emerging services also make Cyber-Physical System (CPS) much more complicated. The maintenance of CPS security tends to be more and more difficult. As a ”gamechanging” new active defense concept, Moving Target Defense (MTD) handle this tricky problem by periodically upsetting and recombining connections between users and servers in the protected system, which is so-called ”shuffle”. By this means, adversaries can hardly obtain enough time to compromise the potential victims, which is the indispensable condition to collect necessary information or conduct further malicious attacks. But every coin has two sides, MTD also introduce unbearable high energy consumption and resource occupation in the meantime, which hinders the large-scale application of MTD for quite a long time. In this paper, we propose a novel deep reinforcement learning-based MOTAG system called DQ-MOTAG. To our knowledge, this is the first work to provide self-adaptive shuffle period adjustment ability for MTD with reinforcement learning-based intelligent control mechanism. We also design an algorithm to generate optimal duration of next period to guide subsequent shuffle. Finally, we conduct a series of experiments to prove the availability and performance of DQ-MOTAG compared to exist methods. The result highlights our solution in terms of defense performance, error block rate and network source consumption.

Static system configuration provides a significant advantage for the adversaries to discover the assets and launch attacks. Configuration-based moving target defense (MTD) reverses the cyber warfare asymmetry by mutating certain configuration parameters to disrupt the attack planning or increase the attack cost significantly. In this research, we present a methodology for the formal verification of MTD techniques. We formally modeled MTD techniques and verified them against constraints. We use Random Host Mutation (RHM) as a case study for MTD formal verification. The RHM transparently mutates the IP addresses of end-hosts and turns into untraceable moving targets. We apply the formal methodology to verify the correctness, safety, mutation, mutation quality, and deadlock-freeness of RHM using the model checking tool. An adversary is also modeled to validate the effectiveness of the MTD technique. Our experimentation validates the scalability and feasibility of the formal verification methodology.

The Internet of Things (IoT) is more and more present in fundamental aspects of our societies and personal life. Billions of objects now have access to the Internet. This networking capability allows for new beneficial services and applications. However, it is also the entry-point for a wide variety of cyber-attacks that target these devices. The security measures present in real IoT systems lag behind those of the standard Internet. Security is sometimes completely absent. Moving Target Defense (MTD) is a 10-year-old cyber-defense paradigm. It proposes to randomize components of a system. Reasonably, an attacker will have a higher cost attacking an MTD-version of a system compared with a static-version of it. Even if MTD has been successfully applied to standard systems, its deployment for IoT is still lacking. In this paper, we propose a generic MTD framework suitable for IoT systems: IANVS (pronounced Janus). Our framework has a modular design. Its components can be adapted according to the specific constraints and requirements of a particular IoT system. We use it to instantiate two concrete MTD strategies. One that targets the UDP port numbers (port-hopping), and another a CoAP resource URI. We implement our proposal on real hardware using Pycom LoPy4 nodes. We expose the nodes to a remote Denial-of-Service attack and evaluate the effectiveness of the IANVS-based port-hopping MTD proposal.

Lightweight virtualization represented by container technology provides a virtual environment for cloud services with more flexibility and efficiency due to the kernel-sharing property. However, the shared kernel also means that the system isolation mechanisms are incomplete. Attackers can scan the shared system configuration files to explore vulnerabilities for launching attacks. Previous works mainly eliminate the problem by fixing operating systems or using access control policies, but these methods require significant modifications and cannot meet the security needs of individual containers accurately. In this paper, we present ConfigRand, a moving target defense framework to prevent the information leakages due to the shared kernel in the container-based cloud. The ConfigRand deploys deceptive system configurations for each container, bounding the scan of attackers aimed at the shared kernel. In design of ConfigRand, we (1) propose a framework applying the moving target defense philosophy to periodically generate, distribute, and deploy the deceptive system configurations in the container-based cloud; (2) establish a model to formalize these configurations and quantify their heterogeneity; (3) present a configuration movement strategy to evaluate and optimize the variation of configurations. The results show that ConfigRand can effectively prevent the information leakages due to the shared kernel and apply to typical container applications with minimal system modification and performance degradation.

Cloud federation is the evolution of modern cloud computing. It provides better resource-sharing, perfect resource-utilization, and load-balancing. However, the heterogeneity of security policies and configurations between cloud service providers makes it hard for users to totally trust them. Further, the severe impact of modern cloud attacks such as cross-side channels on federated environments is a major roadblock against such evolution. Securing users' capsules (Virtual Machines and containers) against cross-side channel attacks is considered as a big challenge to cloud service providers. Moving-target Defense (MtD) by live capsule migration was introduced as an effective mechanism to overcome such challenge. However, researchers noted that even with MtD, migrated capsules can still be tracked via routing information. In this paper, we propose a novel Blockchain-based routing mechanism to enable trace-resistant Moving-target Defence (BMtD) to enable anonymous live cross-cloud migrations of running capsules in federated cloud environments. Exploiting the Vulnerable, Exposed, Attacked, Recovered (VEAR) model, simulation results demonstrated the effectiveness of BMtD in minimizing viral attack dispersion.

Moving Target Defense (MTD) has been emerged as a promising countermeasure to defend systems against cyberattacks asymmetrically while working well with legacy security and defense mechanisms. MTD provides proactive security services by dynamically altering attack surfaces and increasing attack cost or complexity to prevent further escalation of the attack. However, one of the non-trivial hurdles in deploying MTD techniques is how to handle potential performance degradation (e.g., interruptions of service availability) and maintain acceptable quality-of-service (QoS) in an MTD-enabled system. In this paper, we derive the service performance metrics (e.g., an extent of failed jobs) to measure how much performance degradation is introduced due to MTD operations, and propose QoS-aware service strategies (i.e., drop and wait) to manage ongoing jobs with the minimum performance degradation even under MTD operations running. We evaluate the service performance of software-defined networking (SDN)-based web services (i.e., Apache web servers). Our experimental results prove that the MTD-enabled system can minimize performance degradation by using the proposed job management strategies. The proposed strategies aim to optimize a specific service configuration (e.g., types of jobs and request rates) and effectively minimize the adverse impact of deploying MTD in the system with acceptable QoS while retaining the security effect of IP shuffling-based MTD.

Moving target defense technology is one of the revolutionary techniques that is “changing the rules of the game” in the field of network technology, according to recent propositions from the US Science and Technology Commission. Building upon a recently-developed approach called Self Cleansing Intrusion Tolerance (SCIT), this paper proposes a moving target defense system that is based on server switching and cleaning. A protected object is maneuvered to improve its safety by exploiting software diversity and thereby introducing randomness and unpredictability into the system. Experimental results show that the improved system increases the difficulty of attack and significantly reduces the likelihood of a system being invaded, thus serving to enhance system security.

Digital connectivity is fundamental to the health care system to deliver safe and effective care. However, insecure connectivity could be a major threat to patient safety and privacy (e.g., in August 2017, FDA recalled 465,000 pacemakers because of discovering security flaws). Although connecting a patient's pacemaker to the Internet has many advantages for monitoring the patient, this connectivity opens a new door for cyber-attackers to steal the patient data or even control the pacemaker or damage it. Therefore, patients are forced to choose between connectivity and security. This paper presents a framework for secure and private communications between wearable medical devices and patient monitoring systems. The primary objective of this research is twofold, first to identify and analyze the communication vulnerabilities, second, to develop a framework for combating unauthorized access to data through the compromising of computer security. Specifically, hiding targets from cyber-attackers could prevent our system from future cyber-attacks. This is the most effective way to stop cyber-attacks in their first step.

Moving target defense (MTD) suggests a game-changing way of enhancing web security by increasing uncertainty and complexity for attackers. A good number of web MTD techniques have been investigated to counter various types of web attacks. However, in most MTD techniques, only fixed attributes of the attack surface are shifted, leaving the rest exploitable by the attackers. Currently, there are few mechanisms to support the whole attack surface movement and solve the partial coverage problem, where only a fraction of the possible attributes shift in the whole attack surface. To address this issue, this paper proposes a Web Gene Tree (WGT) based MTD mechanism. The key point is to extract all potential exploitable key attributes related to vulnerabilities as web genes, and mutate them using various MTD techniques to withstand various attacks. Experimental results indicate that, by randomly shifting web genes and diversely inserting deceptive ones, the proposed WGT mechanism outperforms other existing schemes and can significantly improve the security of web applications.

In this work, we propose a novel approach for decentralized identifier distribution and synchronization in networks. The protocol generates network entity identifiers composed of timestamps and cryptographically secure random values with a significant reduction of collision probability. The distribution is inspired by Unique Universal Identifiers and Timestamp-based Concurrency Control algorithms originating from database applications. We defined fundamental requirements for the distribution, including: uniqueness, accuracy of distribution, optimal timing behavior, scalability, small impact on network load for different operation modes and overall compliance to common network security objectives. An implementation of the proposed approach is evaluated and the results are presented. Originally designed for a domain of proactive defense strategies known as Moving Target Defense, the general architecture of the protocol enables arbitrary applications where identifier distributions in networks have to be decentralized, rapid and secure.

Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host's network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network's vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts' network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.