SDNTruth: Innovative DDoS Detection Scheme for Software-Defined Networks (SDN)

Linhares, Tiago; Patel, Ahmed; Barros, Ana Luiza Bessa de Paula; Fernandez, Marcial Porto

doi:10.21203/rs.3.rs-2223091/v1

Cited by 2 publications

(1 citation statement)

References 25 publications

(29 reference statements)

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“…Ref. [36] uses entropy and statistical Z-test to analyze information and detect anomalies in DDoS attack traffic. This method has good accuracy and low training time.…”

Section: A Information Entropymentioning

confidence: 99%

Overview of DDoS Attack Detection in Software-Defined Networks

Wang,

2024

IEEE Access

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Software Defined network (SDN), as a new network architecture, is the direction of future network development. By decoupling the control plane and data plane of the network, the control and management of network resources can be improved. However, SDN centralized controllers become the target of malicious attacks, prone to the risk of single point of failure, of which DDoS attacks are the main attack behavior. Through extensive literature investigation, this paper systematically reviews the latest progress of DDoS attack detection in SDN environment. Firstly, the SDN architecture and corresponding DDoS attacks are described, and the commonly used data sets and evaluation indicators are summarized. Secondly, the data preprocessing technology is summarized, and the data dimensionality reduction technology is introduced in detail. Then, at the level of detection technology, it focuses on the advantages and disadvantages of detection algorithms based on statistical analysis, machine learning and deep learning. Finally, the challenges of current research are analyzed, and the future development direction is forecasted. In order to provide reference for future research and development, at the same time, it is of great significance to improve the safety of SDN products in the future.

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“…Ref. [36] uses entropy and statistical Z-test to analyze information and detect anomalies in DDoS attack traffic. This method has good accuracy and low training time.…”

Section: A Information Entropymentioning

confidence: 99%

Overview of DDoS Attack Detection in Software-Defined Networks

Wang,

2024

IEEE Access

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Machine learning based data collection protocol for intelligent transport systems: a real-time implementation on Dublin M50, Ireland

Gillani,

Niaz

2023

Complex Intell. Syst.

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The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent/daily commuters and second new/occasional commuters. The proposed protocol’s main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland’s busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols.

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SDNTruth: Innovative DDoS Detection Scheme for Software-Defined Networks (SDN)

Cited by 2 publications

References 25 publications

Overview of DDoS Attack Detection in Software-Defined Networks

Overview of DDoS Attack Detection in Software-Defined Networks

Machine learning based data collection protocol for intelligent transport systems: a real-time implementation on Dublin M50, Ireland

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