P2ISE: Preserving Project Integrity in CI/CD Based on Secure Elements

Muñoz, Antonio; Farao, Aristeidis; Correia, Jordy Ryan Casas; Xenakis, Christos

doi:10.3390/info12090357

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…Reducing the latency to meet real-time IoT requirements is another critical area, potentially involving the optimization of blockchain verification processes or the exploration of faster consensus mechanisms. Additionally, inspired by [35], integrating hardware-based security measures such as secure elements and Trusted Execution Environments (TEEs) can significantly bolster the security of IoT devices within the blockchain network. These hardware solutions provide a robust platform for cryptographic operations and secure storage, addressing the physical and logical vulnerabilities in IoT devices.…”

Section: Limitationsmentioning

confidence: 99%

Enhancing Industrial IoT Network Security through Blockchain Integration

Bobde,

Narayanan,

Jati

et al. 2024

Electronics

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In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.

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Section: Limitationsmentioning

confidence: 99%

Enhancing Industrial IoT Network Security through Blockchain Integration

Bobde,

Narayanan,

Jati

et al. 2024

Electronics

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“…Researchers usually focus on security within the life cycle of application services. For example, for the security of software deployment, TPM (Trusted Platform Module) computing technology is used to ensure the integrity of program code [1]. For network attacks and system intrusion, attack process identification and prevention techniques are studied.…”

Section: Typical Location Privacy Protection Technologiesmentioning

confidence: 99%

GLPS: A Geohash-Based Location Privacy Protection Scheme

Liu,

Zhang,

Yao

et al. 2023

Entropy

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With the development of mobile applications, location-based services (LBSs) have been incorporated into people’s daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user’s location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user’s exact location. On the anonymous server side, the scheme takes advantage of Geohash codes’ geospatial gridding capabilities and GL-Tree’s effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user’s location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.

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“…This last section describes the implementation of SUKRY in the Raspberry Pi machine. The deployment steps above are useful for future studies in reproducing the experiment [58]. In the near future, the hardware of SUKRY will be packed properly for commercialization purposes.…”

Section: Sukry Implementationmentioning

confidence: 99%

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

Syamsuddin¹,

Barukab

2022

Electronics

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The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi.

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P2ISE: Preserving Project Integrity in CI/CD Based on Secure Elements

Cited by 14 publications

References 34 publications

Enhancing Industrial IoT Network Security through Blockchain Integration

Enhancing Industrial IoT Network Security through Blockchain Integration

GLPS: A Geohash-Based Location Privacy Protection Scheme

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

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