6LoWPAN Performance Analysis of IoT Software-Defined-Network-Based Using Mininet-Io

Setiawan, Dharma Yusuf; Hertiana, Sofia Naning; Negara, Ridha Muldina

doi:10.1109/iotais50849.2021.9359714

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…In this study, the test was operated on an Ubuntu 20.04 LTS computer with a specification of Intel® Core™ i5-10400 CPU @ 2.90 GHz, 8 GB of RAM, and 240 GB of SSD. The SDN-Enabled IoT network topology was emulated by the Mininet emulator [23]. Based on Figure 1, the components used in the network architecture in this topology consisted of 7 Open vSwitch (OvS) [24], [25], 1 RYU Controller [26], and 8 Hosts.…”

Section: Research Methods 21 Emulation's Topology and Scenariomentioning

confidence: 99%

Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance

Abizar,

Ihzanor Syahputra,

Habibullah

et al. 2023

IJ-AI

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<p>One of the main challenges in developing the internet of things (IoT) is the existence of availability problems originated from the low-rate distributed denial of service attacks (LRDDoS). The complexity of IoT makes the LRDDoS hard to detect because the attack flow is performed similarly to the regular traffic. Integration of software defined IoT (SDN-Enabled IoT) is considered an alternative solution for overcoming the specified problem through a single detection point using machine learning approaches. The controller has a resource limitation for implementing the classification process. Therefore, this paper extends the usage of Feature Importance to reduce the data complexity during the model generation process and choose an appropriate feature for generating an efficient classification model. The research results show that the Gaussian Naïve Bayes (GNB) produced the most effective outcome. GNB performed better than the other algorithms because the feature reduction only selected the independent feature, which had no relation to the other features.</p>

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Section: Research Methods 21 Emulation's Topology and Scenariomentioning

confidence: 99%

Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance

Abizar,

Ihzanor Syahputra,

Habibullah

et al. 2023

IJ-AI

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“…In this research, the testing phase was carried out using emulation on mininet-iot as an emulator software [12] on the Ubuntu 20.04 Operating System. The hardware specifications of the emulated OS were composed of Intel core i5-3337U, Nvidia GeForce 710M, and 8GB of RAM.…”

Section: Research Methods 21 Emulation Topologymentioning

confidence: 99%

Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient

Azmi

Sumadi

2022

KINETIK

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The evolution of computer network technology is now experiencing substantial changes, particularly with the introduction of a new paradigm, Software Defined Networking (SDN). The SDN architecture has been applied in a variety of networks, including the Internet of Things (IoT), which is known as SD-IoT. IoT is made up of billions of networking devices that are interconnected and linked to the Internet. Since the SD-IoT was considered as a complex entity, several types of attack on vulnerabilities vary greatly and can be exploited by careless individuals. Low-Rate Distributed Denial of Service (LRDDoS) is one of the availability-based attack that may affect the SD-IoT integration paradigm. Therefore, it is necessary to have an Intrusion Detection System (IDS) to overcome the security hole caused by LRDDoS. The main objective of this research was the establishment of an IDS application for resolving LRDDoS attack using the SVM algorithm combined with the Feature Importance method, namely the Logistic Regression Coefficient. The implemented approach was developed to reduce the complexity or resource’s consumption during the classification process as well as increasing the accuracy. It could be concluded that the Linear kernel SVM algorithm acquired the highest results on the test schemes at 100% accuracy, but the training time required for this model was longer, about 23.6 seconds compared to the Radial Basis Function model which only takes about 1.5 seconds.

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“…C. Index Parameters: QOS from TIPHON In the context of networks, quality of service is the capacity to offer customized services for various types of network traffic [35]- [37]. Better, planned network services with allocated, regulated bandwidth delay and increased characteristic loss are the ultimate goals of quality of service [38]- [41]. The ability to ensure the transmission of crucial data is another way to define quality of service; put another way, it is a set of standards that establish how satisfied a customer is with a given service [42]- [44].…”

Section: B Scenario Simulation and Service Testing Schemesmentioning

confidence: 99%

Performance Comparison of Zevenet Multi Service Load Balancing with Least Connection and Round Robin Algorithm

Ma'arifah,

Sarmini

2024

JOIV : Int. J. Inform. Visualization

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Amikom Purwokerto University concentrates on Technology and Digital Business. This requires technology to be utilized optimally. The use of technology, especially internships, will make various jobs easier. KRS online is taking lecture schedules online via the AMIKOM Purwokerto Student website. There are several problems with the web server that arise due to the increasing need for information access, which causes the data traffic load to increase. Increased data traffic causes workload overload, resulting in server downtime. Experimental methods were used in this research to look for the causes of the web server's downtime. Then, implement the technology. The purpose is to evaluate the Zevenet load balancer performances by comparing the round-robin and least-connection algorithms. The decision is which algorithm will be used best to implement the Zevenet Load balancer to achieve a more efficient backend server traffic cluster distribution. The TIPHON standard Quality of Service parameters used in Zevenet Load Balancer performance testing are throughput, delay, jitter, packet loss, and CPU usage. The quality-of-service parameter test results show that the Zevenet Load Balancer with the round-robin algorithm has superior performance and shows less CPU usage. It is concluded that using the round-robin algorithm in implementing the Zevenet load balancer to overcome the problem of data traffic load sharing and minimize server downtime on the Student Amikom Purwokerto web server is more appropriate and more effective.

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6LoWPAN Performance Analysis of IoT Software-Defined-Network-Based Using Mininet-Io

Cited by 9 publications

References 6 publications

Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance

Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance

Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient

Performance Comparison of Zevenet Multi Service Load Balancing with Least Connection and Round Robin Algorithm

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