High-Security Hardware Module with PUF and Hybrid Cryptography for Data Security

• URL: http://arxiv.org/abs/2409.09928v1
• Date: Mon, 16 Sep 2024 02:06:49 GMT
• Title: High-Security Hardware Module with PUF and Hybrid Cryptography for Data Security
• Authors: Joshua Tito Amael, Oskar Natan, Jazi Eko Istiyanto,
• Abstract summary: This research highlights the rapid development of technology in the industry, particularly Industry 4.0. Despite providing efficiency, these developments also bring negative impacts, such as increased cyber-attacks. This research proposes a solution by developing a hardware security module (HSM) using a field-programmable gate array (FPGA) with physical unclonable function (PUF) authentication and a hybrid encryption data security system.
• Score: 1.8434042562191815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research highlights the rapid development of technology in the industry, particularly Industry 4.0, supported by fundamental technologies such as the Internet of Things (IoT), cloud computing, big data, and data analysis. Despite providing efficiency, these developments also bring negative impacts, such as increased cyber-attacks, especially in manufacturing. One standard attack in the industry is the man-in-the-middle (MITM) attack, which can have severe consequences for the physical data transfer, particularly on the integrity of sensor and actuator data in industrial machines. This research proposes a solution by developing a hardware security module (HSM) using a field-programmable gate array (FPGA) with physical unclonable function (PUF) authentication and a hybrid encryption data security system. Experimental results show that this research improves some criteria in industrial cybersecurity, ensuring critical data security from cyber-attacks in industrial machines.

Related papers

• Interpretable Cyber Threat Detection for Enterprise Industrial Networks: A Computational Design Science Approach [1.935143126104097]
  We use IS computational design science paradigm to develop a two-stage cyber threat detection system for enterprise-level IS. The first stage generates synthetic industrial network data using a modified generative adversarial network. The second stage develops a novel bidirectional gated recurrent unit and a modified attention mechanism for effective threat detection.
  arXiv  Detail & Related papers  (2024-09-04T19:54:28Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• IPAD: Industrial Process Anomaly Detection Dataset [71.39058003212614]
  Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames. We propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage.
  arXiv  Detail & Related papers  (2024-04-23T13:38:01Z)
• Cybersecurity in Critical Infrastructures: A Post-Quantum Cryptography Perspective [0.0]
  Implementing cryptosystems in industrial communication networks faces a trade-off between the security of the communications and the amortization of the industrial infrastructure. New threat to cybersecurity has arisen with the theoretical proposal of quantum computers. Many global agents have become aware that transitioning their secure communications to a quantum secure paradigm is a priority that should be established before the arrival of fault-tolerance.
  arXiv  Detail & Related papers  (2024-01-08T10:02:48Z)
• Software Repositories and Machine Learning Research in Cyber Security [0.0]
  The integration of robust cyber security defenses has become essential across all phases of software development. Attempts have been made to leverage topic modeling and machine learning for the detection of these early-stage vulnerabilities in the software requirements process.
  arXiv  Detail & Related papers  (2023-11-01T17:46:07Z)
• Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems [45.05372822216111]
  Methods from Machine Learning (ML) and Data Mining (DM) have proven to be promising in extracting complex and hidden patterns from the data collected. However, such data-driven projects, usually performed with the Cross-Industry Standard Process for Data Mining (CRISPDM), often fail due to the disproportionate amount of time needed for understanding and preparing the data. This contribution intends present an integrated approach so that data scientists are able to more quickly and reliably gain insights into the CPPS challenges.
  arXiv  Detail & Related papers  (2023-07-21T15:04:00Z)
• A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors [0.0]
  Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. In this article, we try to bridge this gap and propose a secure privacy-preserving protocol for implementing BCI applications. We first transformed brain signals into images and used generative adversarial network to generate synthetic signals to protect data privacy. In addition, we proposed a blockchain-based scheme and developed a prototype, which aims to make storing, querying and sharing personal neurophysiological data and analysis reports secure and privacy-aware.
  arXiv  Detail & Related papers  (2022-11-19T14:57:19Z)
• Trust-Awareness to Secure Swarm Intelligence from Data Injection Attack [5.824096823117585]
  swarm intelligence (SI) is envisaged to play an important role in future industrial Internet of Things (IIoT) that is shaped by Sixth Generation (6G) mobile communications and digital twin (DT) However, its fragility against data injection attack may halt it from practical deployment. In this paper we propose an efficient trust approach to address this security concern for SI.
  arXiv  Detail & Related papers  (2022-10-27T13:37:50Z)
• Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT [57.67687126339891]
  FedGS is a hierarchical cloud-edge-end FL framework for 5G empowered industries. Taking advantage of naturally clustered factory devices, FedGS uses a gradient-based binary permutation algorithm. Experiments show that FedGS improves accuracy by 3.5% and reduces training rounds by 59% on average.
  arXiv  Detail & Related papers  (2022-02-03T10:48:17Z)
• Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review [68.15472610671748]
  Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
  arXiv  Detail & Related papers  (2020-05-23T20:57:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.