Cyber Deception against DDoS attack using Moving Target Defence Framework in SDN IOT-EDGE Networks

Galadima, Haula; Seeam, Amar; Ramsurrun, Visham

doi:10.1109/nextcomp55567.2022.9932172

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…The goal is to lure, confuse, or delay attackers while simultaneously detecting their presence and gaining insights into their tactics, allowing for early threat detection and enhanced cybersecurity defenses [11]. Furthermore, in [12], the authors addressed the critical issue of DDoS attacks on SDN IoT-Edge Computing, exacerbated by the pandemic-induced remote work trend. They explore the efficacy of an SDN-based Moving Target Defense (MTD) technique within a smart building context.…”

Section: Related Workmentioning

confidence: 99%

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

Kabdjou,

Shinomiya

2024

IEEE Access

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Creating a cyber deception framework for 5G networks, particularly in IoT and cellular applications, is complex due to critical constraints in managing resources, meeting low latency demands, and addressing security concerns. While cloud computing aids in alleviating some limitations, it often falls short in meeting low-latency requirements. Multi-Access Edge Computing (MEC) has emerged as a solution by bringing resources closer to User Equipment (UEs) to reduce latency. Various MEC architectures have leveraged Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC), Network Slicing (NS), decision-making systems, and deception components. However, none have integrated these technologies comprehensively to achieve superior Quality of Service (QoS) and strengthen security. In this paper, we unify SDN, NFV, SFC, NS, decision-making technologies, and deception to efficiently manage MEC server resources and lure attackers. We utilize cyber deception metrics, including request collection rates over time and variations in request numbers concerning different botnet sizes. Moreover, we meticulously address QoS parameters such as latency, computing, storage, and bandwidth resources. Our approach initiates with a mathematical model for MEC server resource allocation, introducing a novel architecture that reduces bandwidth, computing, and storage resource usage. We introduce a cyber deception strategy utilizing uniform distribution and random selection to divert potential attackers. Simulations validate efficient resource management, notably reducing end-to-end latency for requests processed on the edge and in the cloud. This enhancement improves QoS within the MEC system and provides valuable insights for advancing decision-making technologies.

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Section: Related Workmentioning

confidence: 99%

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

Kabdjou,

Shinomiya

2024

IEEE Access

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“…In Reference [44], the authors investigated the efficacy of Moving Target Defense (MTD) based on SDN for countering DDoS attacks in IoT‐Edge networks in a test scenario based on intelligent buildings. Meanwhile, in Reference [45], they developed a chimp‐based AE Network (CbAN) for the SDIoT cloud network.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

Setitra

Fan

Bensalem

2023

Trans Emerging Tel Tech

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The growing popularity of Software Defined Networks (SDN) and the Internet of Things (IoT) has led to the emergence of Software Defined Internet of Things (SDIoT) based on centralized network management by the Control Plane, which can handle the dynamic nature of IoT devices and the high volume of network traffic. However, due to their specific design, SDIoTs are the ideal target for Distributed Denial of Service (DDoS) attacks, becoming one of the most destructive threats. Machine learning (ML) techniques are best suited to solve this problem due to the recent growth and sophistication of DDoS attacks. In this study, we propose an enhanced deep learning approach based on combining AutoEncoder (AE) and Extreme Gradient Boosting (XGBoost). First, we applied the SHapley Additive exPlanations (SHAP) feature selection method to select the appropriate features subset according to their correlation results. Next, the AE is trained on the previous subset to learn a compact representation of the input features. The latent representation generated by the AE is then used as input for the XGBoost model, which is trained to predict the target variable and classify the traffic as usual or attack. In parallel, Grid Search Cross Validation (GSCV) is used to find the optimal hyperparameters for the AE‐XGBoost. The experimental results using two publicly available realistic SDN‐Iot datasets demonstrate that the proposed approach enables precise identification of DDoS attacks in SDIoT networks, achieving a 99.9920% accuracy, an F1 score of 0.999917, and a low false positive rate. Furthermore, the proposed model's performance exceeds that of the models used for comparison.

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SDN as a defence mechanism: a comprehensive survey

Ayodele,

Buttigieg

2023

Int. J. Inf. Secur.

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Investing in cybersecurity is increasingly considered a significant area and aspect a business or organisation should seriously consider. Some of these security solutions are network-based and provide many levels of protection. However, traditional networks are seen to be vendor-specific and are limited, enabling minor to no network flexibility or customisation. Implementing SDN to combat cyberattacks is a workable option for resolving this traditional network constraint. Less attention has been paid to how SDN has been utilised to address security concerns, with most surveys concentrating on the security challenges the SDN paradigm faces. This study aims to provide a comprehensive overview of the state-of-the-art on how SDN has been used to combat attacks between 2017 and 2022 by highlighting the specifics of each literature, its advantages, limitations, and potential areas for further study. This work introduces a taxonomy highlighting SDN’s fundamental traits and contributions as a defence mechanism (SaaDM).

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Cyber Deception against DDoS attack using Moving Target Defence Framework in SDN IOT-EDGE Networks

Cited by 6 publications

References 24 publications

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

SDN as a defence mechanism: a comprehensive survey

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