Securing IIoT using Defence-in-Depth: Towards an End-to-End Secure Industry 4.0
January 14, 2022 Β· Declared Dead Β· π Journal of manufacturing systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Aintzane Mosteiro-Sanchez, Marc Barcelo, Jasone Astorga, Aitor Urbieta
arXiv ID
2201.05415
Category
cs.CR: Cryptography & Security
Citations
63
Venue
Journal of manufacturing systems
Last Checked
5 months ago
Abstract
Industry 4.0 uses a subset of the IoT, named Industrial IoT (IIoT), to achieve connectivity, interoperability, and decentralization. The deployment of industrial networks rarely considers security by design, but this becomes imperative in smart manufacturing as connectivity increases. The combination of OT and IT infrastructures in Industry 4.0 adds new security threats beyond those of traditional industrial networks. Defence-in-Depth (DiD) strategies tackle the complexity of this problem by providing multiple defense layers, each of these focusing on a particular set of threats. Additionally, the strict requirements of IIoT networks demand lightweight encryption algorithms. Nevertheless, these ciphers must provide E2E (End-to-End) security, as data passes through intermediate entities or middleboxes before reaching their destination. If compromised, middleboxes could expose vulnerable information to potential attackers if it is not encrypted throughout this path. This paper presents an analysis of the most relevant security strategies in Industry 4.0, focusing primarily on DiD. With these in mind, it proposes a combination of DiD, an encryption algorithm called Attribute-Based-Encryption (ABE), and object security (i.e., OSCORE) to get an E2E security approach. This analysis is a critical first step to developing more complex and lightweight security frameworks suitable for Industry 4.0.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted