H2O: Secure Interactions in IoT via Behavioral Fingerprinting

Ferretti, Marco; Nicolazzo, Serena; Nocera, Antonino

doi:10.3390/fi13050117

Cited by 10 publications

(4 citation statements)

References 77 publications

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“…H2O introduces a mechanism to authenticate the identity of objects or humans based on their claims. The study includes a security analysis of the framework, which encompasses an attack model, demonstrating that the proposed solution effectively achieves its objectives even when faced with potential attacks [26]. In their latest research, Yin et al ( 2021) introduce an IoT-oriented privacy-preserving fingerprint authentication system.…”

Section: Reviewmentioning

confidence: 99%

The Role of Biometric in Banking: A Review

Marani,

Soltani,

Bahadori

et al. 2023

EAI Endorsed Trans AI Robotics

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Biometrics plays a pivotal role in enhancing security, ensuring accurate identification, and offering convenient solutions across diverse industries. Its uniqueness, reliability, and potential for future advancements establish it as a crucial and valuable field in today's digital landscape. Fingerprint authentication in ATMs presents primary advantages such as heightened security through distinctive identification and user convenience by eliminating the reliance on PINs or passwords. This research paper focuses on conducting a comprehensive review and comparative analysis of various approaches for fingerprint identification, aiming to contribute to the understanding of effective and efficient methods in the context of ATM authentication.

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Section: Reviewmentioning

confidence: 99%

The Role of Biometric in Banking: A Review

Marani,

Soltani,

Bahadori

et al. 2023

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

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“…IoT devices are now prone to several vulnerabilities. Services and data in the IoT may be susceptible to attack because of their sensitive nature [293].…”

Section: Vulnerabilities In Iotmentioning

confidence: 99%

“…IoT devices are now prone to several vulnerabilities. Services and data in the IoT may be susceptible to attack because of their sensitive nature [281]. Many IoT systems and a highly complex ecosystem in IoT can have increased risks from significant problems in cloud security [282].…”

Section: Vulnerabilities In Iotmentioning

confidence: 99%

Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions

Bharati

Podder

2022

Security and Communication Networks

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The integration of the Internet of Things (IoT) connects a number of intelligent devices with minimum human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems are posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT system protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach’s benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included.

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“…By combining cybersecurity measures, assurance services, and best practices, blockchain networks can minimise the risk of attacks and fraud [8]. However, it is important to note that security in a blockchain network depends on the behaviour of system participants, not just the technology itself [9]. Although traditional industrial networks such as Modbus, Profinet, KNX, CanBus, BacNet, and EtherCat remain widely used in Industry 4.0 and are unlikely to be replaced entirely by blockchain-based networks, integrating blockchain technology with these networks through gateways or adapters can provide secure and scalable data exchange while maintaining the functionality of existing networks.…”

Section: Introductionmentioning

confidence: 99%

Introducing a Fair Tax Method to Harden Industrial Blockchain Applications against Network Attacks: A Game Theory Approach

Ghajar

Reich

2023

Computers

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Industrial Internet of Things (IIoT) systems are enhancing the delivery of services and boosting productivity in a wide array of industries, from manufacturing to healthcare. However, IIoT devices are susceptible to cyber-threats such as the leaking of important information, products becoming compromised, and damage to industrial controls. Recently, blockchain technology has been used to increase the trust between stakeholders collaborating in the supply chain in order to preserve privacy, ensure the provenance of material, provide machine-led maintenance, etc. In all cases, such industrial blockchains establish a novel foundation of trust for business transactions which could potentially streamline and expedite economic processes to a significant extent. This paper presents an examination of “Schloss”, an industrial blockchain system architecture designed for multi-factory environments. It proposes an innovative solution to increase trust in industrial networks by incorporating a fairness concept as a subsystem of an industrial blockchain. The proposed mechanism leverages the concept of taxes imposed on blockchain nodes to enforce ethical conduct and discipline among participants. In this paper, we propose a game theory-based mechanism to address security and trust difficulties in industrial networks. The mechanism, inspired by the ultimatum game, progressively punishes malicious actors to increase the cost of fraud, improve the compensation system, and utilise the reward reporting capabilities of blockchain technology to further discourage fraudulent activities. Furthermore, the blockchain’s incentive structure is utilised to reduce collusion and speed up the process of reaching equilibrium, thereby promoting a secure and trustworthy environment for industrial collaboration. The objective of this paper is to address lack of trust among industrial partners and introduce a solution that brings security and trust to the forefront of industrial blockchain applications.

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H2O: Secure Interactions in IoT via Behavioral Fingerprinting

Cited by 10 publications

References 77 publications

The Role of Biometric in Banking: A Review

The Role of Biometric in Banking: A Review

Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions

Introducing a Fair Tax Method to Harden Industrial Blockchain Applications against Network Attacks: A Game Theory Approach

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