Security for Internet of Things: The SerIoT Project

Gelenbe, Erol; Domańska, Joanna; Czachórski, Tadeusz; Drosou, Anastasis; Tzovaras, Dimitrios

doi:10.1109/isncc.2018.8531004

Cited by 27 publications

(28 citation statements)

References 33 publications

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“…S. Gangadhar et al [4] extends the application of Machine Learning to improve traffic tolerance in SDN by extending the functionality of the SDN controller to integrate a resilient While the solutions above have demonstrated the effectiveness of SDN as a basis for a security solution, in this paper we explore how SDN can be used to improve the efficiency of a machine learning approach by distributing the learning process. In particular, the SerIoT project exploits the SDN architecture to increase the security of IoT devices and of the network itself [1], [7]. Detection of anomalies is crucial for the proper operation of SerIoT network security mechanisms thus the proposal described in this paper is a natural fit into the framework of that project.…”

Section: B Machine Learning Based Sdn Securitymentioning

confidence: 99%

“…Therefore, network-based solutions to IoT security are required. Such a network solution is the architecture proposed by the SerIoT project [1], which uses SDN [2] as the network infrastructure to both interconnect the IoT devices and provide the IoT security solution. Our system uses a two-stage classifier to reduce the packet processing effort in an edge classifier by utilising an SDN architecture with an additional centralised anomaly detection layer.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Machine Learning for IoT Anomaly Detection in SDN

Amangele

Reed

Al-Naday

et al. 2019

2019 International Conference on Information Technologies (InfoTech)

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The Internet of Things is a fast emerging technology, however, there have been a significant number of security challenges that have hindered its adoption. This work explores the use of machine learning methods for anomaly detection in network traffic of an IoT network that is connected through a Software Defined Network (SDN). The use of SDN allows a hierarchical approach to machine learning with the aim of reducing the packet level processing of anomaly detection at the edge through applying additional, centralized, machine learning in the SDN controller. For the sake of evaluation, we compare several supervised classification algorithms using a publicly available dataset. The results support a decision-tree based approach and show that the proposed solution promises a considerable reduction in the per-packet processing at the network edge compared to a single stage classifier.

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Section: B Machine Learning Based Sdn Securitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Machine Learning for IoT Anomaly Detection in SDN

Amangele

Reed

Al-Naday

et al. 2019

2019 International Conference on Information Technologies (InfoTech)

View full text Add to dashboard Cite

show abstract

“…An important challenge for supporting diverse multimedia applications in the IoT is the security heterogeneity of wired and wireless sensor and transmission networks, which requires a balance between flexibility and efficiency [47]. A secure and safe Internet of Things (SerIoT) was proposed to improve the information and physical security of different operational IoT application platforms in a holistic and cross layered manner [48]: the SerIoT covers areas such as mobile telephony, networked health systems, the Internet of Things, smart cities, smart transportation systems, supply chains and industrial informatics [49].…”

Section: The Internet Of Things and Cybersecuritymentioning

confidence: 99%

Deep Reinforcement Learning Algorithms in Intelligent Infrastructure

Serrano

2019

Infrastructures

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Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital Expenditure (CAPEX). To address this challenge, this article presents a biological algorithm based on neural networks and deep reinforcement learning that enables infrastructure to be intelligent by making predictions about its different variables. In addition, the proposed method makes decisions based on real time data. Intelligent infrastructure must be able to proactively monitor, protect and repair itself: this includes independent components and assets working the same way any autonomous biological organisms would. Neurons of artificial neural networks are associated with a prediction or decision layer based on a deep reinforcement learning algorithm that takes into consideration all of its previous learning. The proposed method was validated against an intelligent infrastructure dataset with outstanding results: the intelligent infrastructure was able to learn, predict and adapt to its variables, and components could make relevant decisions autonomously, emulating a living biological organism in which data flow exhaustively.

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“…The aims, plans and first results of the project that relate to SerIoT's cognitive and secure SDN routing engine for the Internet of Things are outlined in several recent papers [42,73,78,124,125].…”

Section: Current Developments: Cybersecurity Of the Iot Dependabilitmentioning

confidence: 99%