Epidemic Heterogeneity and Hierarchy: A Study of Wireless Hybrid Worm Propagation

Wang, Tianbo; Xia, Chunhe; Li, Xiaojian; Xiang, Yang

doi:10.1109/tmc.2020.3026342

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…High-Order Feature Interaction Extraction. The deep learning models are widely adopted to network intrusion detection to extract the sophisticated hidden features [10][11][12][13][14][15]. In intrusion detection, Wang et al [16] proposed an SDAE-ELM-based integrated deep intrusion detection model to overcome the long training time and low classification accuracy and to achieve timely response to intrusion behavior.…”

Section: Related Workmentioning

confidence: 99%

LCHI: Low‐Order Correlation and High‐Order Interaction Integrated Model Oriented to Network Intrusion Detection

Lei

Xia

Wang

2021

Wireless Communications and Mobile Computing

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Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, existing methods seem to have a strong bias towards single low- or high-order feature interaction. Moreover, they always extract all possible low-order interactions indiscriminately, introducing too much noise. To address the above problems, we propose a low-order correlation and high-order interaction (LCHI) integrated feature extraction model. First, we selectively extract the beneficial low-order correlation between the same-type features by the multivariate correlation analysis (MCA) model and attention mechanism. Second, we extract the complicated high-order feature interaction by the deep neural network (DNN) model. Finally, we emphasize both the low- and high-order feature interactions and incorporate them. Our LCHI model seamlessly combines the linearity of MCA in modeling lower-order feature correlation and the nonlinearity of DNN in modeling higher-order feature interaction. Conceptually, our LCHI is more expressive than the previous models. We carry on a series of experiments on the public wireless and wired network intrusion detection datasets. The experimental results show that LCHI improves 1.06%, 2.46%, 3.74%, 0.25%, 1.17%, and 0.64% on the AWID, NSL-KDD, UNSW-NB15, CICIDS 2017, CICIDS 2018, and DAPT 2020 datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

LCHI: Low‐Order Correlation and High‐Order Interaction Integrated Model Oriented to Network Intrusion Detection

Lei

Xia

Wang

2021

Wireless Communications and Mobile Computing

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“…Particularly interesting are those dedicated to study the propagation of malicious code on wireless sensor networks (WSNs), which constitute the foundation for the development and implementation of IoT networks. The vast majority of these models are of a global nature (see, for example, [10][11][12][13][14] and the references therein), and their dynamics are described by continuous mathematical techniques, such as systems of ordinary differential equations. These are compartmental models (that is, the device population is divided into different classes or compartments: susceptible, infectious, recovered, etc.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

Martín del Rey

2023

Mathematics

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The main goal of this work is to propose a novel compartmental SEA (Susceptible–Exposed–Attacked) model to simulate malware spreading on an IoT (Internet of Things) network. This is a deterministic and individual-based model, whose main novelty compared to others lies in the used of continuous mathematical techniques, such as ordinary differential equations, in the description of local transition rules that define the changes of the states of the devices. These states are given by probability vectors representing the probabilities of being susceptible, exposed and attacked at each step of time. The qualitative study of the model is presented, and several simulations are performed.

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Hybrid control for malware propagation in rechargeable WUSN and WASN: From knowledge-driven to data-driven

Yang

Liu

Liang

et al. 2023

Chaos, Solitons & Fractals

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Epidemic Heterogeneity and Hierarchy: A Study of Wireless Hybrid Worm Propagation

Cited by 7 publications

References 39 publications

LCHI: Low‐Order Correlation and High‐Order Interaction Integrated Model Oriented to Network Intrusion Detection

LCHI: Low‐Order Correlation and High‐Order Interaction Integrated Model Oriented to Network Intrusion Detection

Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

Hybrid control for malware propagation in rechargeable WUSN and WASN: From knowledge-driven to data-driven

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