A Fuzzy Logic-Based Intelligent Multiattribute Routing Scheme for Two-Layered SDVNs

Zhao, Liang; Yin, Z.; Yu, Keping; Tang, Xizi; Xu, Lexi; Guo, Zhenzhou; Nehra, Pulkit

doi:10.1109/tnsm.2022.3202741

Cited by 93 publications

(18 citation statements)

References 35 publications

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“…The most used method of attack detection in SDN networks is still based on threshold [9]. An SDN-based technique called FlexProtect was presented by Chen et al [10] to detect DDoS assaults by assessing the rate of open TCP connections.…”

Section: Related Workmentioning

confidence: 99%

Joint Selfattention-SVM DDoS Attack Detection and Defense Mechanism Based on Self-Attention Mechanism and SVM Classification for SDN Networks

MAN,

YANG,

FENG

2024

IEICE Trans. Fundamentals

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Wanying MAN †a) , Nonmember, Guiqin YANG † †b) , and Shurui FENG † † c) ，Nonmembers SUMMARY Software Defined Networking (SDN), a new network architecture, allows for centralized network management by separating the control plane from the forwarding plane. Because forwarding and control is separated, distributed denial of service (DDoS) assaults provide a greater threat to SDN networks. To address the problem, this paper uses a joint high-precision attack detection combining selfattentive mechanism and support vector machine: a trigger mechanism deployed at both control and data layers is proposed to trigger the initial detection of DDoS attacks; the data in the network under attack is screened in detail using a combination of self-attentive mechanism and support vector machine; the control plane is proposed to initiate attack defense using the OpenFlow protocol features to issue flow tables for accurate classification results. The experimental results show that the trigger mechanism can react to the attack in time with less than 20% load, and the accurate detection mechanism is better than the existing inspection and testing methods, with a precision rate of 98.95% and a false alarm rate of only 1.04%. At the same time, the defense strategy can achieve timely recovery of network characteristics.

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Section: Related Workmentioning

confidence: 99%

Joint Selfattention-SVM DDoS Attack Detection and Defense Mechanism Based on Self-Attention Mechanism and SVM Classification for SDN Networks

MAN,

YANG,

FENG

2024

IEICE Trans. Fundamentals

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“…Additional efforts use vehicular networks to estimate where vehicles will be in the future. In [35], the authors used routing computation and the multi-attribute automotive independent entity routing decision-making procedures to improve packet arrival rate, decrease end-to-end time, and address the uncertainty associated with multiple characteristics. Compared to its contemporaries, the proposed work increases the proportion of packets delivered and decreases the average end-to-end time in metropolitan areas.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 3. Contributions of previous surveys on clustering and routing protocols in WSNs[17,19,27,[35][36][37].…”

mentioning

confidence: 99%

Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heterogeneous Networks of Smart City

et al. 2022

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For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network (WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed–time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.

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“…Deep neural networks can access the underlying essential features or rules [19]. Thus, it seems that deep neural network technology has a technical support function for political education theory courses [20].…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Educational Evaluation System Using Deep Learning

Yan

Lu²

2023

IEEE Access

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Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario.

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A Fuzzy Logic-Based Intelligent Multiattribute Routing Scheme for Two-Layered SDVNs

Cited by 93 publications

References 35 publications

Joint Selfattention-SVM DDoS Attack Detection and Defense Mechanism Based on Self-Attention Mechanism and SVM Classification for SDN Networks

Joint Selfattention-SVM DDoS Attack Detection and Defense Mechanism Based on Self-Attention Mechanism and SVM Classification for SDN Networks

Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heterogeneous Networks of Smart City

Design of an Intelligent Educational Evaluation System Using Deep Learning

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