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Resource investment for ddos attack resistant SDN: a practical assessment

Science China(Information Sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(ddos) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be affected.Many defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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A Resource-Based Pricing Collaborative Approach for Mitigating ddos Attack in Mobile Edge Computing

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China Communications 2022年第12期19卷 160-175页

作者： Lu Chen Hongbo Tang Wei You Yi Bai Information Engineering University Zhengzhou 450002China

Resource-constrainted and located closer to users,edge servers are more vulnerable to Distributed Denial of Service(ddos)attacks.In order to mitigate the impact of DDoS attacks on benign users,this paper designed a Resource-based Pricing Collaborative approach(RPC)in mobile edge computing.By introducing the influence of resource prices on requester in economics,a collaboration model based on resource pricing was established,and the allocation of user request was regarded as a game strategy to obtain the overall minimum offloading cost of the user in network.The article theoretically proved the existence and rationality of the Nash equilibrium.Finally,simulation results verified the effectiveness and feasibility of the proposed approach in two experimental scenes.Experimental results shows that RPC can effectively improve the network ability to mitigate DDoS attacks,and alleviate the adverse effects of server attacks under delay constraints.

关键词： mobile edge computing DDoS attacks resource-based pricing game theory Nash equilibrium

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GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

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Computers, Materials & Continua 2023年第10期77卷 379-402页

作者： Abdelwahed Berguiga Ahlem Harchay Ayman Massaoudi Mossaad Ben Ayed Hafedh Belmabrouk Department of Computer Science College of Science and Arts in GurayatJouf UniversitySakakahSaudi Arabia Olive Research Center Jouf UniversitySakakahSaudi Arabia Department of Electronic Industrial ENISoSousse UniversitySousseTunisia Department of Physics College of Science at ZulfiMajmaah UniversityMajmaahSaudi Arabia

Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.Indeed,the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important.To provide more comprehensive protection against potential cyber-attacks,this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture.The proposed Intrusion Detection System IDS,namely GMLPIDS,combines the feedforward neural network Multilayer Perceptron(MLP)and the Gaussian Mixture Model(GMM)that can better protect the Smart Agriculture system.GMLP-IDS is evaluated with the CIC-ddos2019 dataset,which contains various Distributed Denial-of-Service(ddos)attacks.The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-ddos2019 dataset characteristics and their corresponding class labels.Then,the CIC-ddos2019 dataset is divided randomly into two parts,i.e.,training and testing.75%of the data is used for training,and 25%is employed for testing.The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating,loss rating,recall,and F1 score.Comparisons are handled on both binary and multi-class classification problems.The results revealed that the proposed GMLP-IDS system achieved more than 99.99%detection accuracy and a loss of 0.02%compared to traditional MLP.Furthermore,evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.

关键词： Drones DDoS attacks Internet of Things deep learning multilayer perceptron gaussian mixture model Industry 5.0 Agricultural 5.0

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