Stackelberg Dynamic Game-Based Resource Allocation in Threat Defense for Internet of Things

Liu, Bingjie; Xu, Haitao; Zhou, Xianwei

doi:10.3390/s18114074

Cited by 11 publications

(14 citation statements)

References 33 publications

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“…The principles of epidemic modelling were applied to IoT networks composed of wireless sensor nodes to describe the uncertain propagation trajectory, where the most commonly are model SI, SIR and SIS [21][22][23]. The game theory was introduced into IoT network to obtain allocated resources for the defenders and the attackers [11,16,[24][25][26]. The adversary was set as the attacker chooses to pollute when knowing the position of the sensor, and some deployment schemes of sensors in the adversarial environment was proposed [11,27,28].…”

Section: The Dynamics Of Scheduling Strategymentioning

confidence: 99%

A Stackelberg Security Game for Adversarial Outbreak Detection in the Internet of Things

Chen

Wang

et al. 2020

Sensors

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With limited computing resources and a lack of physical lines of defense, the Internet of Things (IoT) has become a focus of cyberattacks. In recent years, outbreak propagation attacks against the IoT have occurred frequently, and these attacks are often strategical. In order to detect the outbreak propagation as soon as possible, t embedded Intrusion Detection Systems (IDSs) are widely deployed in the IoT. This paper tackles the problem of outbreak detection in adversarial environment in the IoT. A dynamic scheduling strategy based on specific IDSs monitoring of IoT devices is proposed to avoid strategic attacks. Firstly, we formulate the interaction between the defender and attacker as a Stackelberg game in which the defender first chooses a set of device nodes to activate, and then the attacker selects one seed (one device node) to spread the worms. This yields an extremely complex bilevel optimization problem. Our approach is to build a modified Column Generation framework for computing the optimal strategy effectively. The optimal response of the defender's problem is expressed as mixed-integer linear programming (MILPs). It is proved that the solution of the defender's optimal response is a NP-hard problem. Moreover, the optimal response of defenders is improved by an approximate algorithm-a greedy algorithm. Finally, the proposed scheme is tested on some randomly generated instances. The experimental results show that the scheme is effective for monitoring optimal scheduling.Sensors 2020, 20, 804 2 of 20 with IoT devices, such as the medical systems [7]. To detect the worm outbreak propagation as soon as possible, we can collect potential threat data by deploying specific embedded Intrusion Detection Systems (IDSs) in the IoT [8,9]. Therefore, the rapid detection of the worm outbreak propagation in the IoT with limited resources by IDS has become an urgent problem to be solved. Herein, such IoT devices with embedded IDS are collectively referred to as "sensors". The "scheduling strategy" mentioned in the following chapters of this paper is mainly aimed at methods of "sensor" combination opening. MotivationGenerally, the initiator of outbreak propagation is a malicious party, and our goal is to detect the outbreak propagation as soon as possible. For example, in a water network, the problem is where should we place a limited number of sensors to quickly detect contaminants [10][11][12][13][14], while in smart power grids, what is concerned about is finding the failure node in a short time [15]. It is natural in these scenarios to consider the problem of adversarial outbreak detection (AOD), where the defender can first select a set of nodes to deploy sensors, and subsequently the attacker can choose the source of infection to spread malicious information. Although there have been many studies on the AOD problem [11,15], the scenario setting of these works (e.g., a water network setting) is different from our scenario and assumptions (e.g., cybersecurity settings and parameters). A common assumption in the e...

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Section: The Dynamics Of Scheduling Strategymentioning

confidence: 99%

A Stackelberg Security Game for Adversarial Outbreak Detection in the Internet of Things

Chen

Wang

et al. 2020

Sensors

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“…Nash equilibrium can be solved by the proposed evolutionary algorithm to preserve the communication performance of the IoT network under jamming attack. A Stackelberg dynamic game model is proposed to get the optimal allocated resources in [30], which is effective to allocate resource in threat Defense for IoT. Facing an unknown jamming attack, an equilibrium strategy in waterfilling form was derived in OFDM system, and an algorithm was proposed to determine the parameters of the waterfilling equation.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Resource Allocation Model With Anti-Jamming in IoT Network

Dou

Lin

2019

IEEE Access

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With the booming expansion of the Internet of Things (IoT), flexible and configurable resource allocation in an interference environment is a key issue. In this paper, we investigate resource allocation with anti-jamming for the IoT nodes and propose a novel automatic control allocation (ACA) model to get an adaptive allocation and anti-jamming transmission. More specifically, the spreading-time technology is utilized to satisfy the low-power and band-limited requirements of the IoT node, and by mapping into an orthogonal frequency division multiplexing waveform, the ACA mathematical model is obtained. Then in the model, a new joint stepwise extreme recursion algorithm is used to allocate resource with anti-jamming. The simulation results demonstrate the effectiveness of the proposed model and algorithm.

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“…Retrieved from https://www.ibm.com/support/knowledgecenter/en/ SSPHQG_7.2/concept/ha_concepts_physical_logical.html), which provides us a proper approach to modeling IoT and other real layered network systems, is used in this paper. IoT is a huge network which combines functional devices with the Internet [15]. In IoT, there are functional collaboration and dependency relationships among smart devices such as information-sensing devices, information-processing devices, information-fusion devices, and effectors.…”

Section: Introductionmentioning

confidence: 99%

“…NIP is also a useful model for researching the protection of IoT, e.g., the defensive resource allocation [15] and adversarial outbreak detection [19]. Numerous variations of NIP have been proposed and studied to meet different hypotheses under particular scenarios [16,17,[20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Bi-Layer Shortest-Path Network Interdiction Game for Internet of Things

Yan

Xiao

Zhu

et al. 2020

Sensors

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Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that are modeled as a bi-layer logical–physical network. In this problem, a virtual attack takes place at the logical layer (the layer of Things), while the physical layer (the layer of Internet) provides concrete support for the attack. In the interdiction problem, the attacker attempts to access a target node on the logical layer with minimal communication cost, but the defender can strategically interdict some key edges on the physical layer given a certain budget of interdiction resources. This setting generalizes the classic single-layer shortest-path network interdiction problem, but brings in nonlinear objective functions, which are notoriously challenging to optimize. We reformulate the model and apply Benders decomposition process to solve this problem. A layer-mapping module is introduced to improve the decomposition algorithm and a random-search process is proposed to accelerate the convergence. Extensive numerical experiments demonstrate the computational efficiency of our methods.

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Stackelberg Dynamic Game-Based Resource Allocation in Threat Defense for Internet of Things

Cited by 11 publications

References 33 publications

A Stackelberg Security Game for Adversarial Outbreak Detection in the Internet of Things

A Stackelberg Security Game for Adversarial Outbreak Detection in the Internet of Things

An Adaptive Resource Allocation Model With Anti-Jamming in IoT Network

Bi-Layer Shortest-Path Network Interdiction Game for Internet of Things

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