Moving Target Defense Research Articles

Explicit Analysis on Effectiveness and Hiddenness of Moving Target Defense in AC Power Systems

Moving target defense (MTD) is becoming promising in thwarting the false data injection attacks (FDIAs) on power system state estimation (SE). However, due to the nonlinear dynamics of AC power systems, the investigation of the general evaluation metrics of MTD, namely the effectiveness in terms of attack detection and the hiddenness, is still challenging. To this end, in this paper, we attempt to conduct an explicit analysis on the MTD performance in AC power systems. First, we derive explicit approximations of measurement residuals to quantify the two metrics. Then, based on the projection matrix, maximizing the effectiveness is transformed to maximizing the lower bound of the approximated residual, under which the matrix inverse issue is addressed. Moreover, the maximization of hiddenness is achieved by the minimization of the approximated power flow difference caused by reactance perturbation. To balance the trade-off between effectiveness and hiddenness, the design of explicit residual-based MTD (EXR-MTD) is accomplished by aggregating the two sub-problems with an appropriate weight. Finally, extensive simulations are conducted to validate the performance of EXR-MTD. Numerical results indicate that EXR-MTD performs better than existing MTD strategies in terms of hiddenness, while the effectiveness of EXR-MTD is comparable to those of existing MTD strategies.

An SDN-Based Moving Target Defense as a Countermeasure to Prevent Network Scans

An SDN-Based Moving Target Defense as a Countermeasure to Prevent Network Scans

Evaluating the Security and Economic Effects of Moving Target Defense Techniques on the Cloud

Moving Target Defense (MTD) is a proactive security mechanism that changes the attack surface with the aim of confusing attackers. Cloud computing leverages MTD techniques to enhance the cloud security posture against cyber threats. While many MTD techniques have been applied to cloud computing, there has so far been no joint evaluation of the effectiveness of MTD techniques with respect to security and economic metrics. In this paper, we first introduce mathematical definitions for the combination of three MTD techniques: Shuffle, Diversity, and Redundancy. Then, we utilize four security metrics – namely, system risk, attack cost, return on attack, and reliability – to assess the effectiveness of the combined MTD techniques applied to large-scale cloud models. Second, we focus on a specific context based on a cloud model for e-health applications to evaluate the effectiveness of the MTD techniques using security and economic metrics. We introduce (1) a strategy to effectively deploy the Shuffle MTD technique using a virtual machine placement technique, and (2) two strategies to deploy the Diversity MTD technique through operating system diversification. As deploying the Diversity technique incurs costs, we formulate the optimal diversity assignment problem (O-DAP), and solve it as a binary linear programming model to obtain the assignment that maximizes the expected net benefit.

Event-Triggered Control and Proactive Defense for Cyber–Physical Systems

This article studies the attack detection problem of cyber–physical systems (CPSs) with the event-triggered (ET) mechanism. A switching-based moving-target defense (MTD) strategy is developed to detect malicious false-data-injection (FDI) attacks, which is characterized by the average dwell-time (ADT) property. Even if attacks may remain stealthy when the MTD mechanism is adopted, the impact of these stealthy attacks on the system performance is relatively small when the switching signal is unknown to the adversary. In order to reduce the cost of data transmission, an ET mechanism is added into the system. Furthermore, we prove that there exists a lower bound of the execution interval, which indicates that the Zeno phenomenon is excluded. Finally, numerical examples are employed to illustrate the effectiveness of the main results.

Strengthening Network-Based Moving Target Defense with Disposable Identifiers

Strengthening Network-Based Moving Target Defense with Disposable Identifiers

A switching-based Moving Target Defense against sensor attacks in control systems

Moving Target Defense (MTD) prevents adversaries from being able to predict the effect of their attacks by adding uncertainty in the state of a system during runtime. In this paper, we present an MTD algorithm that randomly changes the availability of the sensor data, so that it is difficult for adversaries to tailor stealthy attacks while, at the same time, minimizing the impact of false-data injection attacks. Using tools from the design of state estimators, namely, observers, and switched systems, we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks. We show that the proposed MTD algorithm can be designed to guarantee the stability of the closed-loop system with desired performance. In addition, we formulate an optimization problem for the design of the parameters so as to minimize the impact of the attacks. The results are illustrated in two case studies, one about a generic linear time-invariant system and another about a vehicular platooning problem.

Hands‐on Interdomain network slicing framework and evaluation

5G enables tailoring network services to the needs of different vertical use cases. These Verticals may require deployments across distinct communication service providers' administrative domains. Ensuring secure interaction between domains is fundamental for deploying end‐to‐end services in such environments. This paper examines using Moving Target Defense (MTD) mechanisms to secure VXLAN‐based tunneled connections to realize inter‐domain deployments. The inter‐domain mechanisms are evaluated in different scenarios, mainly targeting throughput and latency, showing the suitability for inter‐domain communications even when under load. The assessment of the MTD mechanism′s effectiveness considers the number of false positives and negatives associated with its operations.

Method of Forming Various Configurations of Telecommunication System Using Moving Target Defense

The purpose of this paper is to improve the effectiveness of the Moving Target Defense (MTD)-based protection method, which reduces the problem of using traditional network protection tools due to the static nature of network services and configurations. Options for solving the problems of choosing an adequate time interval for activating the technology of MTD and its application in networks are given. A new approach is proposed, which consists in creating a set of system configurations and changing it when an attack is detected and determined. The design implementation was tested on a network model using software defined networks (SDN). The advantages of the proposed method are highlighted, among which the most significant are: simple operation scheme, ability to deploy the system without the use of software-defined networks and absence of violations of security policies within the system.

Robust End Hopping for Secure Satellite Communication in Moving Target Defense

Satellite communication contributes tremendously to the Industrial Internet of Things (IIoT) with telecommunication efficiency and data accessibility at global-scale coverage. However, realizing proactive defense for satellite communication remains a challenge. In order to address such an issue, we first explore state-of-the-art proactive defense methods and, following next, a step forward on proposing an end hopping scheme based on fixed hopping timeslot and strict time synchronization strategy by utilizing moving target defense (MTD). In addition, we worked on a Proof of Concept (PoC) to evaluate the scheme’s theoretical protection performance for Distributed Denial of Service (DDoS). Experimental evaluation and analysis of the proposed scheme show that it is efficient and secure, as seen when the attack rate is 100 times/s, the response time of the hopping state is 69.98% shorter than that of the normal state, and when the attack rate is 1000 times/s, the response time of the hopping state is 90.15% shorter.

A Double-Benefit Moving Target Defense Against Cyber–Physical Attacks in Smart Grid

The smart grid (SG), as one of the largest evolutionary critical infrastructures, witnesses the deep integration of electricity facilities and Internet of Things (IoT). But recent events show that the vulnerabilities exposed in IoT devices can be exploited by adversaries to construct the cyberattacks on SG. To address this threat, plenty of countermeasures have been proposed to enhance the SG’s security. However, the additional costs introduced by some countermeasures make the utilities hesitate to implement them practically. To alleviate this concern, in this article, we propose a double-benefit moving target defense (dB-MTD) to protect the SG from cyber–physical attacks (CPAs) and also gain generation-cost benefits. The dB-MTD enables the prevention of stealthy CPAs on the transmission lines by perturbing the reactances with the distributed flexible AC transmission system (D-FACTS). To reduce the infrastructure cost, we minimize the number of required D-FACTS devices for a specific protection goal. Although it needs investment on D-FACTS, we find that the utility can make profits from the generation costs by appropriately setting the reactance perturbations. Therefore, we formulate an optimization problem to compute the optimal reactance perturbations to maximize the generation-cost benefits, without sacrificing the protection performance of dB-MTD. Finally, using the real-world load profiles, we conduct extensive simulations to evaluate the impact of CPA on the system operation and the benefits obtained by the dB-MTD from the aspects of the D-FACTS deployment and the generation-cost profits.

A Survey on Moving Target Defense for Networks: A Practical View

The static nature of many of currently used network systems has multiple practical benefits, including cost optimization and ease of deployment, but it makes them vulnerable to attackers who can observe from the shadows to gain insight before launching a devastating attack against the infrastructure. Moving target defense (MTD) is one of the emerging areas that promises to protect against this kind of attack by continuously shifting system parameters and changing the attack surface of protected systems. The emergence of network functions virtualization (NFV) and software-defined networking (SDN) technology allows for the implementation of very sophisticated MTD techniques. Furthermore, the introduction of such solutions as field-programmable gate array (FPGA) programmable acceleration cards makes it possible to take the MTD concept to the next level. Applying hardware acceleration to existing concepts or developing new, dedicated methods will offer more robust, efficient, and secure solutions. However, to the best of the authors’ knowledge, there are still no major implementations of MTD schemes inside large-scale networks. This survey aims to understand why, by analyzing research made in the field of MTD to show current pitfalls and possible improvements that need to be addressed in future proposals to make MTD a viable solution to address current cybersecurity threats in real-life scenarios.

Voltage Stability Constrained Moving Target Defense Against Net Load Redistribution Attacks

Moving target defense (MTD) using distributed flexible AC transmission system (D-FACTS) devices is a promising defense strategy to detect stealthy false data injection (FDI) attacks against the power system state estimation. However, all existing studies myopically perturb the reactance of D-FACTS lines without considering the system voltage stability. In this paper, we first illustrate voltage instability induced by MTDs in a three-bus system. To address this issue, we further propose a novel MTD framework that explicitly considers system voltage stability by using continuation power flow and voltage stability indices. We mathematically derive the sensitivity matrix of voltage stability index to line impedance, on which an optimization problem for maximizing voltage stability index is formulated. This framework is tested on the IEEE 14-bus and the IEEE 118-bus transmission systems, in which net load redistribution attacks are launched by sophisticated attackers. The simulation results show the effectiveness of the proposed framework in circumventing the voltage instability while maintaining the detection effectiveness of MTD. We conduct case studies with and without the proposed framework under different MTD planning and operational methods. The impacts of the proposed two methods on attack detection effectiveness and system economic metrics are also revealed.

Decentralized Moving Target Defense for Microgrid Protection Against False-Data Injection Attacks

This paper proposes the Decentralized Moving Target Defense via Data Replication (DMTDR) framework, which increases the security of microgrids by adding two layers of uncertainty that limit the success of false-data injection attacks. The DMTDR framework exploits the scalability and low cost of IoT devices to replicate relevant sensory and control signals, which are randomly transmitted through independent communication channels. Then, one of the transmitted replicas is randomly selected to reach its intended destinations. As opposed to most moving target defense (MTD) approaches found in the literature, the proposed DMTDR does not require central coordination but instead it can be applied to any monitoring or control device that utilizes a communication channel. Furthermore, the proposed approach does not deteriorate the system performance in normal operation. The theoretical foundations for the optimal allocation of replicas per signal are developed, and fundamental limits of uncertainties introduced by the framework are calculated. Moreover, taking advantage of the replicated information, a decentralized attack detection strategy is introduced, which is able to detect the presence of malicious data by only using local information without requiring the computation of any estimation model. The proposed framework is demonstrated on a test microgrid, where the data replication and random transmission are implemented on a WiFi network with IoT devices. The results show that the proposed framework considerably improves: i) security by limiting the impact of false-data injection attacks; ii) detectability by enabling the detection of stealthy attacks.

Converter-Based Moving Target Defense Against Deception Attacks in DC Microgrids

With the rapid development of information and communications technology in DC microgrids (DCmGs), the deception attacks, which typically include false data injection and replay attacks, have been widely recognized as a significant threat. However, existing literature ignores the possibility of the intelligent attacker, who could launch deception attacks once obtaining necessary information by exploiting zero-day vulnerabilities or bribing insiders, to affect the system in an unforeseeable manner. In this paper, based on the observation that the primary control law of the power converter device in DCmGs is usually programmable, we propose a novel converter-based moving target defense (CMTD) strategy by proactively perturbing the primary control gains to defend against deception attacks. First, we study the impact of perturbing the primary control gains on the voltage stability in DCmGs and provide explicit conditions for the perturbation magnitude and frequency under which the voltage stability can be ensured. Then, we investigate the improved detectability against deception attacks under CMTD and present sufficient conditions under which these attacks can be detected. Finally, we conduct extensive MATLAB SIMULINK/PLECS based simulations and systematic hardware-in-the-loop based experiments to validate the effectiveness of CMTD.

Moving Target Defense for Cyber–Physical Systems Using IoT-Enabled Data Replication

This article proposes a novel moving target defense (MTD) strategy that leverages the versatility of the Internet of Things (IoT) networks to enhance the security of cyber–physical systems (CPSs) by replicating relevant sensory and control signals. The replicated data are randomly selected and transmitted to create two layers of uncertainties that reduce the ability of adversaries to launch successful cyberattacks, without affecting the performance of the system in a normal operation. The theoretical foundations of designing the IoT network and optimal allocation of replicas per signal are developed for linear-time-invariant systems, and fundamental limits of uncertainties introduced by the framework are calculated. The orchestration of the layers and applications integrated in the proposed framework is demonstrated in experimental implementation on a real-time water system over a WiFi network, adopting a data-centric architecture. The implementation results demonstrate that the proposed framework considerably limits the impact of false-data-injection attacks, while decreasing the ability of adversaries to learn details about the physical system operation.

Mathematical analysis and design of PMTD strategies for an SIRO model of OS virus propagation

This paper mainly aims to explore the propagation behaviors of Operating System (OS) virus in Multi-OSs network, as well as propose virus transmission-suppression strategy based on Platform Moving Target Defense (PMTD). To this end, a dynamical Susceptible–Infected–Removed–Others (SIRO) model is established and analyzed theoretically, including the basic reproduction number, equilibrium point and their stability. Systematic analysis finds that the Operating System virus will die out or persist in the network depending on the basic reproduction number, which is mainly associated with the network topology, Operating System migration frequency and Operating System scale. Two network defense strategies based on Platform Moving Target Defense as well as an Operating System migration management algorithm are proposed, and a set of simulation experiments are designed to verify the effectiveness. Experimental results show that the propagation of Operating System virus can be inhibited effectively by adjusting Operating System migration probability. Besides, proposed methods can replenish the drawback of traditional link interrupt strategy, providing various options for the defense vacuum period of vulnerability patch development.

Secure Control for Cyber-Physical Systems Under Malicious Attacks

This article investigates the secure control problem for cyber-physical systems when the malicious data are injected into the cyber realm, which directly connects to the actuators. Based on moving target defense (MTD) and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(A,B)$</tex-math></inline-formula> is modeled as a switching system consisting of several controllable pairs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(A,\mathcal {B}_{l})$</tex-math></inline-formula> to facilitate the construction of the MTD control scheme. The controllable pairs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(A,\mathcal {B}_{l})$</tex-math></inline-formula> can be altered to update system dynamics under certain unpredictable switching probabilities for each subsystem, which can prevent the adversaries from effective attacks. Second, both attack detection and isolation schemes are designed to accurately locate and exclude the compromised actuators from a switching sequence. Third, a reinforcement learning algorithm based on the zero-sum game theory is proposed to design the defense control scheme when there exist no controllable subsystems to switch. To demonstrate the effectiveness of the defense control scheme, a three-tank system under unknown cyber attacks is illustrated.

An intelligent proactive defense against the client‐side DNS cache poisoning attack via self‐checking deep reinforcement learning

A new class of poisoning attacks has recently emerged targeting the client-side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current DNS defense architecture does not include DNS clients. Although relative encryption solutions can mitigate this attack, they require the cooperation of multiple parties, and the deployment speed is slow. Therefore, we propose an intelligent-driven proactive defense strategy. First, we model the offensive and defensive process as a stochastic game based on moving target defense. Second, we adopt and optimize Proximal Policy Optimization (PPO), a deep reinforcement learning method, to solve problems caused by uncertain attack strategies and unknown state transition probability. Third, we design a self-checking component in PPO to solve the uncertainty of action space caused by game state constraints based on our previous work. Thus the convergence speed and stability of PPO are improved. Finally, to the best of our knowledge, we are the first to game with intelligent attackers besides three conventional ones. Our strategy does not require any modifications to the DNS architecture. Through an extensive experimental campaign, the prototype system is proved to be effective against multiple attack modes. Its success rate is 98.5% approximately, and network round-trip time is about 55 ms. Even for random attackers, our method can achieve the theoretical maximum defensive success rate.

IoDM: A Study on a IoT-Based Organizational Deception Modeling with Adaptive General-Sum Game Competition

Moving target defense (MTD) and decoy strategies, measures of active defense, were introduced to secure both the proactive security and reactive adaptability of internet-of-things (IoT) networks that have been explosively applied to various industries without any strong security measures and to mitigate the side effects of threats. However, the existing MTD and decoy strategies are limited to avoiding the attacker’s reconnaissance and initial intrusion attempts through simple structural mutations or inducing the attackers to a static trap based on the deceptive path and lack approaches to adaptively optimize IoT in consideration of the unique characteristic information by the domain of IoT. Game theory-based and decoy strategies are other options; however, they do not consider the dynamicity and uncertainty of the decision-making stages by the organizational agent related to the IoT domains. Therefore, in this paper, we present a type of organizational deception modeling, namely IoT-based organizational deception modeling (IoDM), which considers both the dynamic topologies and organizational business fingerprints customized in the IoT domain and operational purpose. For this model, we considered the practical scalability of the existing IoT-enabled MTD and decoy concepts and formulated the partially incomplete deceptive decision-making modeling for the cyber-attack and defense competition for IoT in real-time based on the general-sum game. According to our experimental results, the efficiency of the deceptive defense of the IoT defender could be improved by 70% on average while deriving the optimal defense cost compared to the increased defense performance. The findings of this study will improve the deception performances of MTD and decoy strategies by IoT scenarios related to various operational domains such as smart home networks, industrial networks, and medical networks. To the best of our knowledge, this study has employed social-engineering IoT knowledge and general-sum game theory for the first time.

EI-MTD: Moving Target Defense for Edge Intelligence against Adversarial Attacks

Edge intelligence has played an important role in constructing smart cities, but the vulnerability of edge nodes to adversarial attacks becomes an urgent problem. A so-called adversarial example can fool a deep learning model on an edge node for misclassification. Due to the transferability property of adversarial examples, an adversary can easily fool a black-box model by a local substitute model. Edge nodes in general have limited resources, which cannot afford a complicated defense mechanism like that on a cloud data center. To address the challenge, we propose a dynamic defense mechanism, namely EI-MTD. The mechanism first obtains robust member models of small size through differential knowledge distillation from a complicated teacher model on a cloud data center. Then, a dynamic scheduling policy, which builds on a Bayesian Stackelberg game, is applied to the choice of a target model for service. This dynamic defense mechanism can prohibit the adversary from selecting an optimal substitute model for black-box attacks. We also conduct extensive experiments to evaluate the proposed mechanism, and results show that EI-MTD could protect edge intelligence effectively against adversarial attacks in black-box settings.