Interplay Between Malware Epidemics and Honeynet Potency in Industrial Control System Network

Interplay Between Malware Epidemics and Honeynet Potency in Industrial Control System Network

The Industrial Control System (ICS) is widely used in industrial processes, such as power grids, water conservancy, natural gas, petrochemical and so on. More and more cyber attacks are targeting the ICS worldwide. This paper presents a novel honeynet-based epidemic model in ICS network. The honeyne...

Full description

Bibliographic Details
Main Authors:	Qiang Fu, Yu Yao, Chuan Sheng, Wei Yang
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Series:	IEEE Access
Subjects:	ICS network honeynet malware propagation model epidemic dynamics simulation
Online Access:	https://ieeexplore.ieee.org/document/9076604/

Similar Items

Dynamical model and analysis of malware propagation on the dynamically-deployed heterogeneous honeynet
by: Li Hanlun, et al.
Published: (2022-01-01)

HoneyFactory: Container-Based Comprehensive Cyber Deception Honeynet Architecture
by: Tianxiang Yu, et al.
Published: (2024-01-01)

A Mobility-Based Epidemic Model for IoT Malware Spread
by: Bo-Rui Chen, et al.
Published: (2022-01-01)

On the Optimal Control of a Malware Propagation Model
by: Jose Diamantino Hernández Guillén, et al.
Published: (2020-09-01)

Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics
by: Sai Manoj Pudukotai Dinakarrao, et al.
Published: (2020-01-01)

Dynamical behaviors of an epidemic model for malware propagation in wireless sensor networks
by: Ying Zhou, et al.
Published: (2023-05-01)

Zaštita računarskih mreža Ministarstva odbrane i Vojske Srbije primenom virtuelnog honeyneta / Security of computer network of the Ministry of Defence and the Serbian Armed Forces using virtual honeynets
by: Zoran Bobar
Published: (2009-07-01)

Principle and method of deception systems synthesizing for malware and computer attacks detection
by: Antonina Kashtalian, et al.
Published: (2023-12-01)

Propagation of the Malware Used in APTs Based on Dynamic Bayesian Networks
by: Jose D. Hernandez Guillen, et al.
Published: (2021-11-01)

Modeling self-propagating malware with epidemiological models
by: Alesia Chernikova, et al.
Published: (2023-08-01)

SNIRD: Disclosing Rules of Malware Spread in Heterogeneous Wireless Sensor Networks
by: Shigen Shen, et al.
Published: (2019-01-01)

Android Malware Category and Family Identification Using Parallel Machine Learning
by: Ahmed Hashem El Fiky, et al.
Published: (2022-07-01)

Deriving Exact Mathematical Models of Malware Based on Random Propagation
by: Rodrigo Matos Carnier, et al.
Published: (2024-03-01)

Dynamical Propagation Model of Malware for Cloud Computing Security
by: Chenquan Gan, et al.
Published: (2020-01-01)

Reliability Evaluation for Clustered WSNs under Malware Propagation
by: Shigen Shen, et al.
Published: (2016-06-01)

A Malware Distribution Simulator for the Verification of Network Threat Prevention Tools
by: Song-Yi Hwang, et al.
Published: (2021-10-01)

A Malware Propagation Model Considering Conformity Psychology in Social Networks
by: Qingyi Zhu, et al.
Published: (2022-11-01)

Sisyfos: A Modular and Extendable Open Malware Analysis Platform
by: Dimitrios Serpanos, et al.
Published: (2021-03-01)

Malware Clustering Using Family Dependency Graph
by: Binlin Cheng, et al.
Published: (2019-01-01)

On the Effectiveness of Perturbations in Generating Evasive Malware Variants
by: Beomjin Jin, et al.
Published: (2023-01-01)

Evaluation of Survivability of the Automatically Obfuscated Android Malware
by: Himanshu Patel, et al.
Published: (2022-05-01)

Analyzing temporal graphs of malware distribution networks
by: Jose Andre Morales, et al.
Published: (2022-07-01)

Stochastic Modeling of IoT Botnet Spread: A Short Survey on Mobile Malware Spread Modeling
by: Arash Mahboubi, et al.
Published: (2020-01-01)

Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach
by: Roberto Casado-Vara, et al.
Published: (2024-01-01)

MalView: Interactive Visual Analytics for Comprehending Malware Behavior
by: Huyen N. Nguyen, et al.
Published: (2022-01-01)

Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model
by: Jueun Jeon, et al.
Published: (2020-01-01)

The impact of quarantine strategies on malware dynamics in a network with heterogeneous immunity
by: Salma M. Al-Tuwairqi, et al.
Published: (2022-04-01)

A Comprehensive Review on Malware Detection Approaches
by: Omer Aslan, et al.
Published: (2020-01-01)

MalFe—Malware Feature Engineering Generation Platform
by: Avinash Singh, et al.
Published: (2023-10-01)

Dynamic Malware Classification and API Categorisation of Windows Portable Executable Files Using Machine Learning
by: Durre Zehra Syeda, et al.
Published: (2024-01-01)

Optimal control analysis of malware propagation in cloud environments
by: Liang Tian, et al.
Published: (2023-07-01)

Securing Android app markets via modeling and predicting malware spread between markets
by: Meng, Guozhu, et al.
Published: (2021)

Android malware category detection using a novel feature vector-based machine learning model
by: Hashida Haidros Rahima Manzil, et al.
Published: (2023-03-01)

Design of Automation Environment for Analyzing Various IoT Malware
by: Sungwon Lee, et al.
Published: (2021-01-01)

Malware propagation in Wireless Sensor Networks: global models vs Individual-based models
by: Ángel MARTÍN DEL REY, et al.
Published: (2017-11-01)

A STATE OF THE ART SURVEY ON POLYMORPHIC MALWARE ANALYSIS AND DETECTION TECHNIQUES
by: Emmanuel Masabo, et al.
Published: (2018-07-01)

GANG-MAM: GAN based enGine for Modifying Android Malware
by: Renjith G., et al.
Published: (2022-06-01)

Approach to assessing the danger of destructive effects of malware on special-purpose automated systems
by: Alexander V. Melnikov, et al.
Published: (2023-09-01)

A New Malware Classification Framework Based on Deep Learning Algorithms
by: Omer Aslan, et al.
Published: (2021-01-01)

MACoMal: A Multi-Agent Based Collaborative Mechanism for Anti-Malware Assistance
by: Mohamed Belaoued, et al.
Published: (2020-01-01)