TeFENS: Testbed For Experimenting Next-Generation-Network Security

Ravi, Nagarathna; Selvaraj, Mercy Shalinie

doi:10.1109/5gwf.2018.8516708

Cited by 13 publications

(3 citation statements)

References 16 publications

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“…In this section, the results obtained from the simulation of the presented approach is demonstrated. The study considers a simulation testbed of IoT-fog cloud [30] to evaluate the performance of the proposed system. The fog-node is configured with a component of Cisco Nexus switch device of 5672UP.…”

Section: Resultsmentioning

confidence: 99%

An efficient security framework for intrusion detection and prevention in internet-of-things using machine learning technique

Nagaraj,

Channarayappa

2024

IJECE

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Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.

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Section: Resultsmentioning

confidence: 99%

An efficient security framework for intrusion detection and prevention in internet-of-things using machine learning technique

Nagaraj,

Channarayappa

2024

IJECE

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“…A deep learning model. Ravi et al [104] Security in the context of 5G-IoT. Analyze security aspects.…”

Section: Detection Of Aptmentioning

confidence: 99%

Systematic Literature Review on 5G-IoT Security Aspects

Valadares,

Will,

Sobrinho

et al. 2023

Preprint

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The 5G technology brings many benefits already known: large connection capacity, great transmission velocities, and low latencies with high reliability. One of its core services, the massive Machine Type Communication (mMTC), theoretically allows up to one million devices connected simultaneously in a square kilometer. As the Internet of Things (IoT) devices often have very restricted resources, hampering the adoption of robust security mechanisms, they can be targets for attackers aiming to explore vulnerabilities and gain access to the 5G infrastructure. Given this concern, it is essential to know existent vulnerabilities and possible threats and solutions related to 5G-IoT, the scenarios considering IoT devices connected to 5G infrastructures. For this reason, we carried out a systematic literature review, extracting and analyzing data from 142 selected papers from conferences and journals. As the main results, we present lists with known vulnerabilities, possible threats and solutions, and some recommendations.

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“…The research perspective in a networking aspect for fog infrastructure building stated with the research on SDN. An SDN controller architecture proposed by Nagarathna Ravi et al 27 describes a testbed for attack detection and mitigation in fog infrastructure. The authors have explored the advantages of SDN as a networking component for a fog infrastructure creation.…”

Section: Review Of Literaturementioning

confidence: 99%

Fault tolerant resource allocation in fog environment using game theory‐based reinforcement learning

Divya

2021

Concurrency and Computation

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Realtime decision making is associated with an on-demand, latency aware resource allocation. Fog nodes along with cloud infrastructure, when used effectively can ensure real-time decision making. In this article, we propose an efficient resource allocation and fault tolerance mechanism for the fog layer. Our work takes the advantage of game theory, where Nash equilibrium is the initial allocation strategy, which is then passed on to the reinforcement learner. The allocation is done proactively based on the network status and traffic history. The performance of our system is compared with the existing open shortest path first and neural network algorithms. Besides, fault tolerance mechanism has also been proposed which takes the advantage of the fail-over cluster formation to find the link failure and provide an alternate path in the smart switch, which is the networking component of the fog network. The proposed work gives an improved recovery time and average service time in case of failure with a recovery time of 32 ms. The experimental results are justified in terms of improved service time, lower delay, and optimal energy utilization. K E Y W O R D Sdeep reinforcement learning, failover cluster, fog computing, game theory, resource allocation INTRODUCTIONA surge in the importance of sensors and real-time monitoring has brought about the convergence of two major technologies, the cloud and Internet of Things (IoT), which lead to the emergence of cloud of Things. 1 Processing of large streaming data and providing low latent, power-efficient, high accurate decision making has yet again emerged a new technology-Fog Computing, proposed by CISCO. 2,3 This computing layer helps to alleviate congestion in the cloud by the execution of time-sensitive tasks and data filtering near the edge. 4 Researchers use the terms Edge/fog/mist computing based on the distance of the computing layer from the source of data. 5 Our work takes the advantage of the fog layer to execute the latency aware tasks and effectively distribute the load in the fog layer.One of the most commonly used techniques for load distribution is task or code offloading 6 which helps to distribute the load from the cloud to the lower layer. Latency sensitive tasks are offloaded to the intermediate layer near the edge for better performance. 7,8 This helps in a significant reduction of data being transferred to a remote location, thereby saving bandwidth utility.Software-defined network (SDN) plays a major role as the networking component in a fog network creation. The SDN is an intelligent paradigm of networking, which consists of two planes, the data and the control plane to take care of delivery and networking of the application. 9,10 The Open-Flow protocol ensures the communication between the control plane and the data plane, 11 which instructs the data plane reactively or proactively on packet handling.Traffic engineering plays a key role in path-finding and route establishment in a network. A good algorithm enhances the throughput and minimizes the d...

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TeFENS: Testbed For Experimenting Next-Generation-Network Security

Cited by 13 publications

References 16 publications

An efficient security framework for intrusion detection and prevention in internet-of-things using machine learning technique

An efficient security framework for intrusion detection and prevention in internet-of-things using machine learning technique

Systematic Literature Review on 5G-IoT Security Aspects

Fault tolerant resource allocation in fog environment using game theory‐based reinforcement learning

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