Modelling Spreading Process Induced by Agent Mobility in Complex Networks

Chai, Wei Koong

doi:10.1109/tnse.2017.2764523

Cited by 8 publications

(4 citation statements)

References 72 publications

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“…We adopt the susceptible-infectious-recovered (SIR) model [14] to simulate the spread of worms in communities. Although remotely manipulating bots across communities can be prevented in ZTA-6G, bot-herders can still manipulate infected UEs within the same community to launch attacks.…”

Section: Numerical Resultsmentioning

confidence: 99%

Zero Trust Architecture for 6G Security

Chen¹,

Feng²,

Ge³

et al. 2022

Preprint

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The upcoming sixth generation (6G) network is envisioned to be more open and heterogeneous than earlier generations. This challenges conventional security architectures, which typically rely on the construction of a security perimeter at network boundaries. In this article, we propose a softwaredefined zero trust architecture (ZTA) for 6G networks, which is promising for establishing an elastic and scalable security regime. This architecture achieves secure access control through adaptive collaborations among the involved control domains, and can effectively prevent malicious access behaviors such as distributed denial of service (DDoS) attacks, malware spread, and zero-day exploits. We also introduce key design aspects of this architecture and show the simulation results of a case study, which shows the effectiveness and robustness of ZTA for 6G. Furthermore, we discuss open issues to further promote this new architecture.

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Section: Numerical Resultsmentioning

confidence: 99%

Zero Trust Architecture for 6G Security

Chen¹,

Feng²,

Ge³

et al. 2022

Preprint

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“…The framework employs a continuous-time Markov chain analysis to model spreading behavior. It has been studied and extended in various directions (e.g., [63]; [64]; [60]; [65]; [66]). The framework is general and applicable to different problem domains.…”

Section: B Topology-based Sir Modelmentioning

confidence: 99%

“…This results in the infinitesimal generator of the system, Q(t) to have the dimension of 3 N × 3 N . To address this issue, we advocate the use of the N-intertwined epidemic framework ( [63], [64], [60], [65], [66]) for the SIR model which approaches the problem by considering each node individually. Now, applying the Markov theory, we will get N infinitesimal generators, Q n (t) of the three-state continuous Markov chain; one for each node as follows:…”

Section: B Topology-based Sir Modelmentioning

confidence: 99%

Modeling Traffic Congestion Spreading Using a Topology-Based SIR Epidemic Model

Kozhabek,

Chai,

Zheng

2024

IEEE Access

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The continuous urbanisation and increase in vehicle ownership have increasingly exacerbated traffic congestion problems. In this paper, we advocate the use of epidemic theory to model the spreading of traffic congestion in urban cities. Specifically, we use the Susceptible-Infected-Recovered (SIR) model but propose to explicitly consider the road network structure in the model to understand the contagion process of road congestion. This departs from the classical SIR model where homogeneous mixing based on the law of mass action is assumed. For this purpose, we adopt the N-intertwined modeling framework for the SIR model based on continuous-time Markov chain analysis. In our evaluation, we used two real-world traffic datasets collected in California and Los Angeles. We compare our results against both classical and average-degreebased SIR models. Our results show better agreement between the model and actual congestion conditions and shed light on how congestion propagates across a road network. We see the potential application of insights gained from this work on the development of traffic congestion mitigation strategies.INDEX TERMS Congestion spread modeling, epidemics, SIR model, topology,traffic congestion, urban road networks.

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“…Second, the stochastic network dynamics already make this a non-trivial problem even if access to the true infection state X(t) is available. In addition, there are only very few works that have studied the optimal feedback control problems for stochastic networked epidemics [63], [64], [65], [43], [66], [67].…”

Section: Fixed-horizon Stochastic Optimal Control Problemmentioning

confidence: 99%

Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics

et al. 2020

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The sheer volume of IoT networks being deployed today presents a major "attack surface" and poses significant security risks at a scale never encountered before. In other words, a single IoT device/node that gets infected with malware has the potential to spread the malicious activities across the network, eventually ceasing the network functionality or compromising the network. Simply detecting and quarantining the malware in IoT networks does not guarantee preventing malware propagation. On the other hand, use of traditional control theory for malware confinement is not effective, as most of the existing works do not consider real-time malware control strategies that can be implemented using uncertain infection information from the nodes in the network or have the containment problem decoupled from network performance. In response, in this work, we propose a two-pronged approach with malware detection at nodelevel, and confinement of malware at network-level. We deploy a recently proposed lightweight runtime malware detector at the node-level that employs Hardware Performance Counter (HPC) values for malware detection. This node-level malware information is combined with the malware propagation information and then fed during runtime to a stochastic predictive controller to confine the malware propagation without hampering the network performance. Synthesizing the node-level malware information with the model predictive containment strategy leads to achieving an average network throughput of nearly 200% of that of IoT network without any defense, and up to 160% of that of network with commonly employed state-ofthe-art heuristic approaches for malware confinement. Furthermore, to scale with ever-increasing network topology sizes, we introduce a novel multi-attribute graph translation that can predict the network topology and node state information when provided with a snapshot of topology and node-level malware infection. The proposed multi-attribute graph translation has <5.88 Root Mean Square Error (RMSE) compared to the model predictive containment strategy and has shown nearly constant graph translation time and limited resource utilization independent of the network size.

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Modelling Spreading Process Induced by Agent Mobility in Complex Networks

Cited by 8 publications

References 72 publications

Zero Trust Architecture for 6G Security

Zero Trust Architecture for 6G Security

Modeling Traffic Congestion Spreading Using a Topology-Based SIR Epidemic Model

Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics

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