A Mechanism for Securing IoT-enabled Applications at the Fog Layer

Abbas, Nadeem; Asim, Muhammad; Tariq, Noshina; Baker, Thar; Abbas, Sohail

doi:10.3390/jsan8010016

Cited by 98 publications

(49 citation statements)

References 42 publications

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“…Chaotic secure hashing in combination with a digital signature was used to authenticate user and then the user packets are analyzed. Rivest-Shamir-Adleman (RSA) algorithm was involved for secure transmission of the user credentials to the verifier entity [34]. The verification was provided by validating security parameters, device identity and password.…”

Section: Related Workmentioning

confidence: 99%

Novel Three-Tier Intrusion Detection and Prevention System in Software Defined Network

Ali

Yousaf

2020

IEEE Access

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Software Defined Network (SDN) is a flexible paradigm that provides support for a variety of data-intensive applications with real-world smart Internet of Things (IoT) devices. This emerging architecture updates with the managing ability and network control. Still, the benefits are challenging to achieve due to the presence of intruder flow into the network. The research topic of intrusion detection and prevention system (IDPS) has grasped the attention to reduce the effect of intruders. Distributed Denial of Service (DDoS) is a targeted attack that develops malicious traffic is flooded into a particular network device. These intruders also involve even with legitimate network devices, the authenticated device will be compromised to inject malicious traffic. In this paper, we investigate the involvement of intruders in three-Tier IDPS with regard to user validation, packet validation and flow validation. Not all the authentication users can be legitimate, since they are compromised, so that the major contribution is to identify all the compromised devices by knee analysis of the packets. Routers are the edge devices employed in first tier which is responsible to validate the IoT user with RFID tag and encrypted signature. Then the authenticated user"s packets are submitted into second tier with switches that validates the packets using type-II fuzzy filtering. Then the key features are extracted from packets and they are classified into normal, suspicious and malicious. The mismatched packets are analyzed in controllers which maintain two queues as suspicious and normal. Then suspicious queue packets are classified and predicted using deep learning method. The proposed work is experimented in OMNeT++ environment and the performances are evaluated in terms of intruder Detection Rate, Failure Rate, Delay, Throughput and Traffic Load.

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Section: Related Workmentioning

confidence: 99%

Novel Three-Tier Intrusion Detection and Prevention System in Software Defined Network

Ali

Yousaf

2020

IEEE Access

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“…The authors in [20] proposed a novel fog security service (FSS). The proposed security mechanism provides identity-based authentication, data integrity, and the non-repudiation of connecting nodes by using a private key generator (PKG).…”

Section: Literature Reviewmentioning

confidence: 99%

Resilient Service Embedding in IoT Networks

et al. 2020

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The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency.

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“…To a certain degree of network delay, FG requires less data transmission volume, has the ability to quickly process real-time data, improves local storage and computing capabilities, and eliminates bottlenecks in data transmission. Software-defined network (SDN), as a new type of network architecture, mainly separates the control plane from the data plane, and finally, realizes flexible control of network traffic [19]. Combining SDN with FG can effectively solve intermittent connections and high packet loss rates in the IoV, as well as optimizing and improving network deployment of the IoV through communication and consistent control between facilities.…”

Section: Iovmentioning

confidence: 99%

A freight integer linear programming model under fog computing and its application in the optimization of vehicle networking deployment

Wang¹,

Qiu²

2020

PLoS ONE

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The increase in data amount makes the traditional Internet of Vehicles (IoV) fail to meet users' needs. Hence, the IoV is explored in series. To study the construction of freight integer linear programming (ILP) model based on fog computing (FG), and to analyze the application of the model in the optimization of the networking deployment (ND) of the IoV. FG and ILP are combined to build a freight computing ILP model. The model is used to analyze the application of ND optimization in the IoV system through simulations. The results show that while analyzing the ND results in different scenarios, the model is more suitable for small-scale scenarios and can optimize the objective function; however, its utilization rate is low in large-scale scenarios. While comparing and analyzing the network cost and running time, compared with traditional cloud computing solutions, the ND solution based on FG requires less cost, shorter running time, and has apparent effectiveness and efficiency. Therefore, it is found that the FG-based model has low cost, short running time, and apparent efficiency, which provides an experimental basis for the application of the later deployment of freight vehicles (FVs) in the Internet of Things (IoT) system for ND optimization. The results will provide important theoretical support for the overall deployment of IoV.

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A Mechanism for Securing IoT-enabled Applications at the Fog Layer

Cited by 98 publications

References 42 publications

Novel Three-Tier Intrusion Detection and Prevention System in Software Defined Network

Novel Three-Tier Intrusion Detection and Prevention System in Software Defined Network

Resilient Service Embedding in IoT Networks

A freight integer linear programming model under fog computing and its application in the optimization of vehicle networking deployment

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