A Lightweight Authentication and Key Exchange Protocol for IoT

Bin-Rabiah, Abdulrahman; Ramakrishnan, K. K.; Liri, Elizabeth; Kar, Koushik

doi:10.14722/diss.2018.23004

Cited by 20 publications

(18 citation statements)

References 11 publications

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“…Lightweight key exchange protocols that use pre-shared secret symmetric keys are proposed in [8]- [9]. These protocols assume that one or more symmetric keys are readily available to the client and the server before the handshake protocol begins.…”

Section: Related Workmentioning

confidence: 99%

SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

Baykara¹,

Şafak²,

Kalkan³

2021

Advances in Machine Learning

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This paper proposes a new lightweight handshake protocol implemented on top of the Constrained Application Protocol (CoAP) that can be used in device discovery and ensuring the IoT network security by autonomously managing devices of any computational complexity using whitelisting and blacklisting. A Physical Unclonable Function (PUF) is utilized for the session key generation in the proposed handshake protocol. The CoAP server performs real-time device discovery using the proposed handshake protocol, and anomaly detection using machinelearning algorithms to ensure the security of the IoT network. To the best of our knowledge, the presented PUF-based handshake protocol is the first to performs blacklisting and whitelisting. Whitelisted IoT devices not displaying anomalous behavior can join and remain in the IoT network. IoT devices that display anomalous behavior are autonomously blacklisted by the CoAP server and are either disallowed from joining the IoT network or are removed from the IoT network. Simulation results show that amongst the five machine learning algorithms studied, the stacking classifier displays the highest overall anomaly detection accuracy of 99.98%. Based on the results of the network simulation performed, the CoAP server is capable of blacklisting malicious IoT devices within the network with perfect accuracy.

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Section: Related Workmentioning

confidence: 99%

SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

Baykara¹,

Şafak²,

Kalkan³

2021

Advances in Machine Learning

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“…SHA256 based on Hash-based Message Authentication Code (HMAC). It calculates the message authentication code which speci es the hash function combination with secret cryptographic key for authentication [17].…”

Section: Applied Authentication Routinementioning

confidence: 99%

Advanced Protection Scheme For Information Monitoring in Internet of Things Environment

Gaber¹,

Khalaf²,

Mahmoud³

et al. 2021

Preprint

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In the recent years, Internet of Things (IoT) technologies are growing to make great progress for the nuclear energy applications. The protection of their sensitive information has become an important challenge in implementing secure application services. These services should meet the major security attributes including confidentiality, availability, and integrity. This paper introduces a security scheme for the transmitted sensitive information and secure monitoring of the critical radiation levels at the nuclear facilities. It evaluated through integrating the cryptography and steganography techniques with cloud computing services. The cryptography techniques based on Advanced Encryption Standard (AES) and Rivest, Shamir-Adleman (RSA) algorithms. The scheme uses the extracted cryptography keys from authenticated biometric attributes. The proposed scheme provides a low computational time suitable for fast responding in the emergencies. It allows securing access for the encrypted sensitive measurements, files, and images with high data integrity and confidentiality. Furthermore, it hides the confidential sensitive information with great capacity and imperceptibility through the transmitted carrier image. The security performance analysis ensures the robustness of introducing scheme against various attacks through authentication, encryption, and information hiding techniques. It resists the serious attacks, including the man in the middle, noise, and Distributed Denial of Service (DDOS) attacks.

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“…There are different lightweight approaches to deal with authentication and corrupted message issues in the IoT. For example, embedding digital certificates, signed by the manufacturer, into devices can solve authentication and key exchange problems in some IoT applications [55]. However, there are many devices that cannot cope with this approach.…”

Section: ) Lightweight Message Authentication Code (Mac)mentioning

confidence: 99%

IoT-HarPSecA: A Framework and Roadmap for Secure Design and Development of Devices and Applications in the IoT Space

et al. 2020

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The past couple of years have marked continued growth in the applications and services of the Internet of Things (IoT). This has attracted the attention of new operators as well as institutional, corporate, and private investors in every sector of the economy, and as a result, new businesses are springing up rapidly. These include many start-up companies that are producing various kinds of useful IoT devices and Smart Applications (smart apps). While this can be seen as a boost for innovation in the IoT, some of these companies produce IoT devices and smart apps with security vulnerabilities. In this paper, we propose the IoT Hardware Platform Security Advisor (IoT-HarPSecA), a security framework intended to provide support to such IoT producers. IoT-HarPSecA offers three functionality features, namely security requirement elicitation, security best practice guidelines for secure development, and above all, a feature that recommends specific LightWeight Cryptographic Algorithms (LWCAs) for both software and hardware implementations. Accordingly, IoT-HarPSecA is composed of three main components, namely Security Requirements Elicitation (SRE) component, Security Best Practice Guidelines (SBPG) component, and LightWeight Cryptographic Algorithms Recommendation (LWCAR) component, each of them servicing one of the aforementioned features. We implement a command-line tool in C++ to serve as an interface between users and the proposed framework. IoT-HarPSecA can be employed during the early stages of IoT systems design, and it can also be used to facilitate the implementation of security in existing IoT systems. This paper presents a detailed description, design, and implementation of the SRE, SBPG, and LWCAR components of the proposed framework. Using real-world practical scenarios, we show how IoT-HarPSecA can be used to elicit security requirements and recommend appropriate LWCAs based on user inputs. While a full performance evaluation of the SRE and SBPG components is beyond the scope of this paper, we present a detailed performance evaluation of the LWCAR component, which shows that IoT-HarPSecA can serve as a roadmap for secure IoT development.

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A Lightweight Authentication and Key Exchange Protocol for IoT

Cited by 20 publications

References 11 publications

SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

Advanced Protection Scheme For Information Monitoring in Internet of Things Environment

IoT-HarPSecA: A Framework and Roadmap for Secure Design and Development of Devices and Applications in the IoT Space

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