Shortfall-Based Optimal Placement of Security Resources for Mobile IoT Scenarios

Rullo, Antonino; Serra, Edoardo; Bertino, Elisa; Lobo, Jorge

doi:10.1007/978-3-319-66399-9_23

Cited by 13 publications

(6 citation statements)

References 33 publications

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“…In the field of IoT security, Monte Carlo method is applied to several scenarios. In the mobile Internet of Things scenario, Rullo [48] addressed the problem of security resource allocation by using the Monte Carlo method to generate different device configurations in a geographic area in order to evaluate the proposed method with several network topologies. Xu [17] focused on the transmission design for secure relay communications in IoT networks, where the communication is exposed to eavesdroppers with unknown number and locations, and exploited Monte Carlo model simulations to validate the theoretical result.…”

Section: Monte Carlo Methods In Iotmentioning

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: Monte Carlo Methods In Iotmentioning

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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“…In this regard, Rullo et al [45] showed how to determine Pareto-optimal resource allocation strategies on the basis of the network topology and the characteristics of the available security resources. This approach was later adapted for large-scale [46] and mobile [47] networks.…”

Section: Other Workmentioning

confidence: 99%

Kalis2.0—A SECaaS-Based Context-Aware Self-Adaptive Intrusion Detection System for IoT

Rullo,

Midi,

Mudjerikar

et al. 2024

IEEE Internet Things J.

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The wide variety of application domains makes the Internet of Things (IoT) quite unique among other types of computer networks: IoT networks can be made of devices of different types, i.e., characterized by different hardware, functionalities, computing capabilities, and also network topology and communication protocols may drastically change from one IoT application to another. Such a heterogeneity requires adhoc security solutions, as security techniques that are effective in one IoT context may not be so in another context. Furthermore, IoT networks are ever-evolving by their very nature as smart devices can be easily added or removed. These factors call for the design of security tools capable of adapting themselves to the specific IoT instance, but also to the continuous network changes. In this paper we propose a context-aware, Securityas-a-Service based approach for intrusion detection whereby an IDS (i) autonomously collects information about the monitored system, (ii) chooses the best detection strategy accordingly, and (iii) modifies the detection strategy as the network evolves over time. This comprehensive approach to intrusion detection is an attempt to face the heterogeneity which characterizes the IoT in all its aspects, making it possible the design of a security tool able to be self-adaptive and context-aware, that is, effective in different and evolving IoT scenarios with little or no human intervention.

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“…However, it has recently found use in computing and networking for robust optimization as well. For example, Rullo et al [25] and Yu et al [36] both used CVaR to represent and optimize security risks in IoT.…”

Section: Other Related Workmentioning

confidence: 99%

Robust resource provisioning in time-varying edge networks

Xue

Wan

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Edge computing is one of the revolutionary technologies that enable high-performance and low-latency modern applications, such as smart cities, connected vehicles, etc. Yet its adoption has been limited by factors including high cost of edge resources, heterogeneous and uctuating demands, and lack of reliability. In this paper, we study resource provisioning in edge computing, taking into account these dierent factors. First, based on observations from real demand traces, we propose a time-varying stochastic model to capture the time-dependent and uncertain demand and network dynamics in an edge network. We then apply a novel robustness model that accounts for both expected and worst-case performance of a service. Based on these models, we formulate edge provisioning as a multi-stage stochastic optimization problem. The problem is NP-hard even in the deterministic case. Leveraging the multi-stage structure, we apply nested Benders decomposition to solve the problem. We also describe several eciency enhancement techniques, including a novel technique for quickly solving the large number of decomposed subproblems. Finally, we present results from real dataset-based simulations, which demonstrate the advantages of the proposed models, algorithm and techniques.

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Shortfall-Based Optimal Placement of Security Resources for Mobile IoT Scenarios

Cited by 13 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

Kalis2.0—A SECaaS-Based Context-Aware Self-Adaptive Intrusion Detection System for IoT

Robust resource provisioning in time-varying edge networks

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