Software-Defined Security-by-Contract for Blockchain-Enabled MUD-Aware Industrial IoT Edge Networks

Krishnan, Prabhakar; Jain, Kurunandan; Achuthan, Krishnashree; Buyya, Rajkumar

doi:10.1109/tii.2021.3084341

Cited by 25 publications

(8 citation statements)

References 19 publications

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“…The identification of connected objects poses a great challenge given a large number of heterogeneous protocols [12], the networks used and few consensual standards. Recent approaches to object identification based on behavioral analysis of computing devices have emerged [13]. The basic idea is to scrutinize the traffic crossing the network, using either active or passive measurement techniques, and to extract unique patterns that are sufficiently discriminating in order to individually identify the objects present within our network.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for internet of things classification using network traffic parameters

Elhaloui¹,

Filali²,

Benlahmar³

et al. 2023

IJECE

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With the growth of the internet of things (IoT) smart objects, managing these objects becomes a very important challenge, to know the total number of interconnected objects on a heterogeneous network, and if they are functioning correctly; the use of IoT objects can have advantages in terms of comfort, efficiency, and cost. In this context, the identification of IoT objects is the first step to help owners manage them and ensure the security of their IoT environments such as smart homes, smart buildings, or smart cities. In this paper, to meet the need for IoT object identification, we have deployed an intelligent environment to collect all network traffic traces based on a diverse list of IoT in real-time conditions. In the exploratory phase of this traffic, we have developed learning models capable of identifying and classifying connected IoT objects in our environment. We have applied the six supervised machine learning algorithms: support vector machine, decision tree (DT), random forest (RF), k-nearest neighbors, naive Bayes, and stochastic gradient descent classifier. Finally, the experimental results indicate that the DT and RF models proved to be the most effective and demonstrate an accuracy of 97.72% on the analysis of network traffic data and more particularly information contained in network protocols. Most IoT objects are identified and classified with an accuracy of 99.21%.

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Section: Introductionmentioning

confidence: 99%

Machine learning for internet of things classification using network traffic parameters

Elhaloui¹,

Filali²,

Benlahmar³

et al. 2023

IJECE

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show abstract

“…SDN innovatively changes the existing network structure by dividing it into a data plane and a control plane [2], making it possible to optimize network resource allocation and improve network quality. However, the open and untrustworthy network environment of SDN leaves it vulnerable to attackers who may use forged user identities or malicious terminals to attack the network [3,4].…”

Section: Introductionmentioning

confidence: 99%

FACSC: Fine-Grained Access Control Based on Smart Contract for Terminals in Software-Defined Network

Jiang

et al. 2023

Security and Communication Networks

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Physical terminals provide network services to upper-layer applications, but their limited memory and processing power make it challenging to perform security updates and patches, leaving them vulnerable to known security threats. Attackers can exploit these weaknesses to control the terminals and attack the network. To restrict unauthorized access to the network and its resources, appropriate access control mechanisms are necessary. In this paper, we propose a fine-grained access control method based on smart contracts (FACSC) for terminals in software-defined networking (SDN). FACSC utilizes the attribute-based access control (ABAC) model to achieve fine-grained control over terminal access networks. To ensure the security and reliability of access control policies and terminal-related attribute information, we utilize smart contract technology to implement the ABAC model. Furthermore, we leverage the programming protocol-independent packet processor (P4) to filter and forward packets in the data plane based on the packet option field, enabling rapid terminal access. Experimental results show that our proposed method achieves fine-grained secure authentication of terminals in SDN networks with a low authentication processing overhead.

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“…As distributed networks are gaining popularity, efforts are being made by researchers to adopt Blockchain Technology for securing SDN-enabled networks. Blockchain is a Distributed Ledger Technology (DLT) with characteristics such as decentralization, immutability, non-reversibility, and peer-to-peer operation and has been widely adopted in various fields Krishnan et al (2022); Wang et al (2022); Roy et al (2021); Misra et al (2020a). Blockchain has been effectively used in the field of IoT to preserve the privacy of IoT devices and data-sharing schemes Ferrag et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Preserving flow table integrity in OpenFlow networks through smart contract

Bargayary,

Medhi

2023

Cluster Comput

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SDN revolutionises network management by providing a centralised controller that enables flexible and effortless configuration of networks. However, this flexibility also leads to a vulnerability that enables the adversary to trick the security system into allowing the installation of unauthorised flow rules in the switches. Blockchain provides us with a way to protect against malicious tampering with flow rules by storing them in the distributed ledger. In this work, we propose FTISCON, a mechanism to preserve the integrity of the Open-Flow flow table that utilizes blockchain technology. We employ the Ethereum Private Blockchain to implement the proof-of-concept and conduct a comparative analysis of the proposed scheme and existing related schemes, evaluating their performance in terms of delay, computation time, transaction cost, and detection rate. The proposed work is found to perform better in each of these. The study results suggest that the proposed approach offers a practical and efficient remedy to prevent flow modification attacks within SDN networks.

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Software-Defined Security-by-Contract for Blockchain-Enabled MUD-Aware Industrial IoT Edge Networks

Cited by 25 publications

References 19 publications

Machine learning for internet of things classification using network traffic parameters

Machine learning for internet of things classification using network traffic parameters

FACSC: Fine-Grained Access Control Based on Smart Contract for Terminals in Software-Defined Network

Preserving flow table integrity in OpenFlow networks through smart contract

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