Comparative Analysis of Community Detection Methods for Link Failure Recovery in Software Defined Networks

Daha, Muhammad Yunis; Zahid, Mohd Soperi Mohd; Alashhab, Abdussalam Ahmed; Hassan, Shahab Ul

doi:10.1109/icicyta53712.2021.9689089

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…However, the process of calculating the edge betweenness in GN has the complexity of Ο(𝑒𝑛), so that the overall complexity of the GN method is Ο(𝑒 2 𝑛) . Furthermore, the GN method is also inefficient for extensive data [14].…”

Section: Related Workmentioning

confidence: 99%

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

2022

IJIES

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The Girvan-Newman (GN) method is one of the most popular methods for detecting communities. However, the method is applied to graphs with only positive weights, while graphs with positive and negative weights are in the real world. This paper proposes improving the GN method to work on graphs with positive and negative weights. The method is called Girvan-Newman Parallel Positive Negative (GN-PPN). Other than the benefit related to the use of negative weights, GN-PPN has a parallelization process that accelerates processing time. This method also uses edge betweenness and pair dependencies as calculations. The GN-PPN method is evaluated using modularity score, Normalized Mutual Information (NMI), and processing time. Our experiments show that the modularity score of the GN-PPN is similar to GN, modified GN (mGN), and Parallel GN (PGN). The accuracy of the GN-PPN method in detecting communities evaluated by NMI shows that the GN-PPN is better than similar methods such as Infomap, which ranges from 0.651 to 0.937. The processing time of the GN-PPN method shows a significant acceleration, which is a decrease of around 45% to 95% compared to other methods such as GN, mGN, and PGN.

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Section: Related Workmentioning

confidence: 99%

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

2022

IJIES

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“…Although SDN can provide extensive functionality for IoT networks, network utilization efficiency has been improved. However, at the same time, SDN still faces many security challenges, such as DoS/DDoS attacks [5,6], link failure [7,8], switch data leakage [9], and other common attacks in traditional networks [10]. Distributed Denial of Services (DDoS) attacks is a security threat that has plagued the Internet for more than 20 years.…”

Section: Introductionmentioning

confidence: 99%

Low-rate DDoS attack Detection using Deep Learning for SDN-enabled IoT Networks

Alashhab¹,

Zahid²,

Muneer³

et al. 2022

IJACSA

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Software Defined Networks (SDN) can logically route traffic and utilize underutilized network resources, which has enabled the deployment of SDN-enabled Internet of Things (IoT) architecture in many industrial systems. SDN also removes bottlenecks and helps process IoT data efficiently without overloading the network. An SDN-based IoT in an evolving environment is vulnerable to various types of distributed denial of service (DDoS) attacks. Many research papers focus on highrate DDoS attacks, while few address low-rate DDoS attacks in SDN-based IoT networks. There's a need to enhance the accuracy of LDDoS attack detection in SDN-based IoT networks and OpenFlow communication channel. In this paper, we propose LDDoS attack detection approach based on deep learning (DL) model that consists of an activation function of the Long-Short Term Memory (LSTM) to detect different types of LDDoS attacks in IoT networks by analyzing the characteristic values of different types of LDDoS attacks and natural traffic, improve the accuracy of LDDoS attack detection, and reduce the malicious traffic flow. The experiment result shows that the model achieved an accuracy of 98.88%. In addition, the model has been tested and validated using benchmark Edge IIoTset dataset which consist of cyber security attacks.

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Online Machine Learning Approach to Detect and Mitigate Low-Rate DDoS Attacks in SDN-Based Networks

Alashhab,

Mohd Zahid,

Alashhab

et al. 2023

2023 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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Comparative Analysis of Community Detection Methods for Link Failure Recovery in Software Defined Networks

Cited by 3 publications

References 17 publications

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

Low-rate DDoS attack Detection using Deep Learning for SDN-enabled IoT Networks

Online Machine Learning Approach to Detect and Mitigate Low-Rate DDoS Attacks in SDN-Based Networks

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