A Review of Moving Target Defense Mechanisms for Internet of Things Applications

Saputro, Nico; Tonyalı, Samet; Aydeger, Abdullah; Akkaya, Kemal; Rahman, Mohammad Ashiqur; Uluagac, Selcuk

doi:10.1002/9781119593386.ch24

Cited by 16 publications

(12 citation statements)

References 108 publications

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“…Other related works for MTD deal with identifying when the defender should deploy a new implementation [12,13,14] and how the defender can create multiple implementations to switch between [15,16,17,18,19]. Another line of work studies how Moving Target Defense can be applied in real-world settings [20,21]. All these areas of research are orthogonal and complementary to our work.…”

Section: Related Workmentioning

confidence: 99%

Learning Effective Strategies for Moving Target Defense with Switching Costs

Viswanathan¹,

Bose²,

Paruchuri³

2023

Preprint

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Moving Target Defense (MTD) has emerged as a key technique in various security applications as it takes away the attacker's ability to perform reconnaissance for exploiting a system's vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker's motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a system and how each of these vulnerabilities can be exploited by an attacker. In this work, we aim to create algorithms that generate effective Moving Target Defense strategies that do not rely on prior knowledge about the attackers. Our work assumes that the only way the defender receives information about its own reward is via interaction with the attacker in a repeated game setting. Depending on the amount of information that can be obtained from the interactions, we devise two different algorithms using multi-armed bandit formulation to identify efficient strategies. We then evaluate our algorithms using data mined from the National Vulnerability Database to showcase that they match the performance of the state-of-the-art techniques, despite using a lot less amount of information.

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Section: Related Workmentioning

confidence: 99%

Learning Effective Strategies for Moving Target Defense with Switching Costs

Viswanathan¹,

Bose²,

Paruchuri³

2023

Preprint

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“…Cho et al [11] has a sub-subsection of Internetof-Things within a discussion of application domains for MTD; it identified four techniques and acknowledged that MTD seems promising for IoT systems but with some limitations when compared with conventional MTD. A recent book chapter by Saputro et al [12] focuses on the applicability of MTD for IoT applications. It extensively discusses general concepts of MTD, IoT, and Software-Defined Networking (SDN).…”

Section: Related Workmentioning

confidence: 99%

“…The most recent peer-reviewed MTD surveys [9], [11], identify less than five IoT-specific MTD techniques. A recent book chapter [12] focuses on MTD for IoT and effectively identifies around a dozen techniques. MTD for IoT is an acknowledged promising field of study [9], [12].…”

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confidence: 99%

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MTD, Where Art Thou? A Systematic Review of Moving Target Defense Techniques for IoT

Navas

Cuppens

et al. 2021

IEEE Internet Things J.

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Internet of Things (IoT) systems are increasingly deployed in the real world, but their security lags behind the state of the art of non-IoT systems. Moving Target Defense (MTD) is a cyberdefense paradigm, successfully implemented in conventional systems, that could improve IoT security. Objective: Identify and synthesize existing MTD techniques for IoT and validate the feasibility of MTD as a cybersecurity paradigm suitable for IoT systems. Method: We use a systematic literature review method to search and analyze existing MTD for IoT techniques up to July 2020. We evaluated the existing techniques in terms of security foundations and real-world deployability using the evidence they provide. We define and use entropy-related metrics to categorize them. This is the first MTD survey to use Shannon's entropy metric empirically. Results: Thirty-two distinct MTD for IoT techniques exist: 54% are Network-layer-based, 50% present strong evidence about their real-world deployment, and 64% have weak security foundations. Conclusion: MTD for IoT is a feasible cyberdefense approach. A variety of proposals exist, with evidence about their implementation and evaluation. Nevertheless, the MTD for IoT state of the art is still immature: the security foundations of most existing proposals are weak. Novel techniques should prioritize providing convincing security foundations and realworld deployment evidence.

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“…WSNs are applied in many applications, such as industry [3], agriculture [3], military [4], medicine [3,5], and Internet of Things (IoT) [4]. Today, micro-electro-mechanical systems have grown dramatically.…”

Section: Introductionmentioning

confidence: 99%

An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

et al. 2021

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Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

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A Review of Moving Target Defense Mechanisms for Internet of Things Applications

Cited by 16 publications

References 108 publications

Learning Effective Strategies for Moving Target Defense with Switching Costs

Learning Effective Strategies for Moving Target Defense with Switching Costs

MTD, Where Art Thou? A Systematic Review of Moving Target Defense Techniques for IoT

An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

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