Open Platform Systems Under Scrutiny: A Cybersecurity Analysis of the Device Tree

Tychalas, Dimitrios; Maniatakos, Michail

doi:10.1109/icecs.2018.8618042

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…Ref. [ 16 ] proposed a variation of a long short-term memory-based system for IIoTtechnology in the manufacturing industry. The attributes were rebuilt throughout the proposed IDS.…”

Section: Related Workmentioning

confidence: 99%

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

Lilhore,

Manoharan,

Simaiya

et al. 2023

Sensors

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Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model’s prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL.

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“…Ref. [ 16 ] proposed a variation of a long short-term memory-based system for IIoTtechnology in the manufacturing industry. The attributes were rebuilt throughout the proposed IDS.…”

Section: Related Workmentioning

confidence: 99%

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

Lilhore,

Manoharan,

Simaiya

et al. 2023

Sensors

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“…The smart factory is powered by a cyber-physical system and sophisticated industrial robots, and it is constantly connected to its surroundings. With the advent of the "Smart Factory", new security issues emerge as this smart factory is linked to various systems Tychalas and Maniatakos, 2018;Hasnan, and Yusoff, 2018;Sanchez, 2019 9 Supply chain management Industry 4.0 provides cybercriminals with new opportunities to penetrate the top of the supply chain, gaining access to the smart factory via its dependent actors. Only by using contemporary cybersecurity countermeasures such as adaptive authentication and behavioural analysis can the flow of supply chain-initiated Industry 4.0 attacks be stopped.…”

Section: Major Contributions Of the Studymentioning

confidence: 99%

Perspectives of cybersecurity for ameliorative Industry 4.0 era: a review-based framework

Haleem

Javaid

Singh

et al. 2022

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Purpose Industry 4.0 refers to the interconnection of cyber-physical systems, which connects the physical and digital worlds by collecting digital data from physical objects/processes, and using this data to drive automation and optimisation. Digital technologies used in this revolution gather and handle massive volumes of high-velocity streams while automating field operations and supply chain activities. Cybersecurity is a complicated process that helps sort out various hacking issues of Industry 4.0. This purpose of this paper is to provide an overview on cybersecurity and its major applications for Industry 4.0. Design/methodology/approach The rise of Industry 4.0 technologies is changing how machines and associated information are obtained to evaluate the data contained within them. This paper undertakes a comprehensive literature-based study. Here, relevant research papers related to cybersecurity for Industry 4.0 are identified and discussed. Cybersecurity results in high-end products, with faster and better goods manufactured at a lesser cost. Findings Artificial intelligence, cloud computing, internet of things, robots and cybersecurity are being introduced to improve the Industry 4.0 environment. In the starting, this paper provides an overview of cybersecurity and its advantages. Then, this study discusses technologies used to enhance the cybersecurity process. Enablers, progressive features and steps for creating a cybersecurity culture for Industry 4.0 are discussed briefly. Also, the research identified the major cybersecurity applications for Industry 4.0 and discussed them. Cybersecurity is vital for better data protection in many businesses and industrial control systems. Manufacturing is getting more digitised as the sector embraces automation to a more significant level than ever before. Originality/value This paper states about Industry 4.0 and the safety of multiple business process systems through cybersecurity. A significant issue for Industry 4.0 devices, platforms and frameworks is undertaken by cybersecurity. Digital transformation in the Industry 4.0 era will increase industrial competitiveness and improve their capacity to make optimum decisions. Thus, this study would give an overview of the role of cybersecurity in the effective implementation of Industry 4.0.

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“…The assignment of ST divides the assignment of ST demo into two steps, where context and rule Find Index are used to determine the index L of the assigned variable X in store, rule Assignment implements the update of the store at index L. The purpose of this division is to make the assignment operation better applicable to complex types, because in some cases the index of the assigned variable can not be directly obtained and multiple queries are required. For instance, when assigning a value to A [3,5,7], where A is a multi-dimensional array, we need to look up one by one dimension to finally determine the index. In addition, refer to the state of X in type and constant during the assignment process.…”

Section: Memory Operationsmentioning

confidence: 99%

“…In addition, there are also obvious differences between different products of the same vendor. For example, the S7-1500 and the S7-1200 of Siemens support different type conversion methods 5 . Where the S7-1500 only provides explicit conversions of types, and the S7-1200 provides both explicit and implicit conversions.duiy…”

Section: Semantic Completeness (Rq1)mentioning

confidence: 99%

“…It is critical to ensure that PLCs are operating correctly, as any functional or security-related defects may lead to serious incidents in the system. This has most famously been demonstrated by the Stuxnet worm [1], while many other less-known safety and security incidents [2], [3], [4] and potential hazards [5], [6] related to PLCs have resulted in significant consequences, with an estimated $350,000 in damage on average [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

K-ST: A Formal Executable Semantics of PLC Structured Text Language

Wang¹,

Wang²,

Poskitt³

et al. 2022

Preprint

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Programmable Logic Controllers (PLCs) are responsible for automating process control in many industrial systems (e.g. in manufacturing and public infrastructure), and thus it is critical to ensure that they operate correctly and safely. The majority of PLCs are programmed in languages such as Structured Text (ST). However, a lack of formal semantics makes it difficult to ascertain the correctness of their translators and compilers, which vary from vendor-to-vendor. In this work, we develop K-ST, a formal executable semantics for ST in the K framework. Defined with respect to the IEC 61131-3 standard and PLC vendor manuals, K-ST is a high-level reference semantics that can be used to evaluate the correctness and consistency of different ST implementations. We validate K-ST by executing 509 ST programs extracted from Github and comparing the results against existing commercial compilers (i.e., CODESYS, CX-Programmer, and GX Works2). We then apply K-ST to validate the implementation of the open source OpenPLC platform, comparing the executions of several test programs to uncover five bugs and nine functional defects in the compiler.

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Open Platform Systems Under Scrutiny: A Cybersecurity Analysis of the Device Tree

Cited by 7 publications

References 7 publications

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

Perspectives of cybersecurity for ameliorative Industry 4.0 era: a review-based framework

K-ST: A Formal Executable Semantics of PLC Structured Text Language

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