A propagation model with defensive measures for PLC-PC worms in industrial networks

Yao, Yu; Sheng, Chuan; Fu, Qiang; Liu, Hanxin; Wang, Dajin

doi:10.1016/j.apm.2019.01.014

Cited by 22 publications

(5 citation statements)

References 21 publications

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“…Indeed, the authors [20] pointed out that antivirus software updates always lag behind the emergence of new viruses, and it is not possible to remove all viruses from the network. In addition, our work is also different from the traditional computer virus propagation model (e.g., [21]- [26]). On the one hand, these traditional models do not consider the cloud environment.…”

Section: Introductionmentioning

confidence: 98%

Dynamical Propagation Model of Malware for Cloud Computing Security

et al. 2020

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Cloud-fog-edge computing especially cloud computing is providing a variety of services in many areas around the world and plays a vital role in cyber-physical-social systems (CPSS). Particularly, virtualization is one of main enabling technologies of cloud computing and realizes the dynamic deployment of computing tasks through the migration of virtual machines (VMs), so how to secure the virtual environment in the cloud is very crucial. The purpose of this paper is to address the issue of malware propagation among VMs under the infrastructure as a service (IaaS) architecture. Firstly, a dynamical propagation model is proposed to explore the important factors affecting the spread of malware, especially the impact of installing antivirus software in VMs. On this basis, a theoretical analysis for this model is investigated by means of differential dynamics, from which it is able to understand the dissemination behavior of malware under an infected cloud environment. Finally, some numerical simulations are conducted to verify the applicability and effectiveness of the proposed model. INDEX TERMS Cloud computing security, virtual environment, malware, propagation model, dynamical behavior.

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Section: Introductionmentioning

confidence: 98%

Dynamical Propagation Model of Malware for Cloud Computing Security

et al. 2020

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“…A programmable Logic Controller (PLC) is a type of industrial automation technology widely used in harsh operating environments because of its ability to perform reliably under extreme conditions, such as high temperatures, humidity, strong vibrations, and electrical interference [1][2][3]. PLCs are typically programmed using the IEC 61131-3 standard, which defines a set of programming languages, instructions, syntax, and semantics [4,5].…”

Section: Introductionmentioning

confidence: 99%

Low-Latency FPGA-Based PLC Microprocessor for Industrial Automation in Compliance with IEC-61131-3

Cancino-Escobar,

Delgado-Del-Carpio,

Solís-Cisneros

et al. 2024

Arab J Sci Eng

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This study presents the design and implementation of a PLC microprocessor adhering to the IEC-61131-3 standard, executed on a Cyclone-V FPGA using a DE10-NANO development board. Our microprocessor optimizes the central processing unit by streamlining the data path, achieving a remarkable simulated response time of approximately 60 ns, equivalent to three clock cycles at a 50MHz frequency for Boolean operations. To substantiate our approach, we conducted practical experiments utilizing a FESTO conveyor station, employing relays as actuators, and incorporating optical and inductive sensors. The results underscore the feasibility of our proposed approach and serve as practical validation of its efficacy. This work introduces a promising avenue for the development of cost-effective PLCs employing SoC FPGA variants. Additionally, a thorough comparison of execution times with other early reported architectures. Our microprocessor outperforms even well-established PLCs like the S7-312, with substantial reductions in execution times of 94.54% for floating-point operations, 71.42% to 93.33% for word operations, and up to 78.57% for bit operations.

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“…Generating reliable signatures to detect polymorphic worms is one of such means. 9,10 Signature-based detection methods are easy to implement and incorporate into firewalls, Network Intrusion Detection Systems (NIDSs) or antivirus software. At the initial stage of polymorphic worm discovery research, signatures were mostly generated by domain experts or at least with human intervention.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we need to automate the generation of signatures for newly found polymorphic worms to reduce the aforementioned time lag. Traditional automated signature generation approaches rely mainly on algorithm-generated tokens or regular expressions, 1,9,11 capturing the critical sequence segments of known polymorphic worms. Although signature-based detection methods are suited for real-time detection, most of the traditional ones fail to recognize unseen variants of polymorphic worms as they reshape themselves over each propagation.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have been working hard to develop defensive methods against them, for example, signature‐based detection, statistical‐based detection, behavior‐based detection, and for forth. Generating reliable signatures to detect polymorphic worms is one of such means 9,10 . Signature‐based detection methods are easy to implement and incorporate into firewalls, Network Intrusion Detection Systems (NIDSs) or antivirus software.…”

Section: Introductionmentioning

confidence: 99%

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Toward machine intelligence that learns to fingerprint polymorphic worms in IoT

Wang

Yang

Wang

et al. 2022

Int J of Intelligent Sys

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Internet of Things (IoT) is fast growing. Non-personal computer devices under the umbrella of IoT have been increasingly applied in various fields and will soon account for a significant share of total Internet traffic. However, the security and privacy of IoT and its devices have been challenged by malware, particularly polymorphic worms that rapidly selfpropagate once being launched and vary their appearance over each infection to escape from the detection of signature-based intrusion detection systems. It is well recognized that polymorphic worms are one of the most intrusive threats to IoT security.To build an effective, strong defense for IoT networks against polymorphic worms, this study proposes a machine intelligent system, termed Gram-Restricted Boltzmann Machine (Gram-RBM), which automatically generates generic fingerprints/signatures for the polymorphic worm. Two augmented N-gram-based methods are designed and applied in the derivation of polymorphic worm sequences, also known as fingerprints/signatures. These derived sequences are then optimized using the Gaussian-Bernoulli RBM dimension-reduction algorithm. The results, gained

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A propagation model with defensive measures for PLC-PC worms in industrial networks

Cited by 22 publications

References 21 publications

Dynamical Propagation Model of Malware for Cloud Computing Security

Dynamical Propagation Model of Malware for Cloud Computing Security

Low-Latency FPGA-Based PLC Microprocessor for Industrial Automation in Compliance with IEC-61131-3

Toward machine intelligence that learns to fingerprint polymorphic worms in IoT

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