An Integrated Approach to Optimize the Intrusion Detection System in SDN based on Feature Selection and Deep Learning Techniques
DOI:
https://doi.org/10.26438/ijcse/v13i5.916Keywords:
Software Defined Networking (SDN),, Software Defined Networking (SDN), Outlier, Feature EngineeringAbstract
Software-defined networking (SDN) is a revolutionary innovation that has become known as an internet architecture. It allows for modular, adaptable, and effective network management alternatives by decoupling the management plane about the data plane. The centralized management and open interfaces that are present in software-defined networking (SDN) provide a number of new security issues, the most notable of which is the increased danger of network breaches. It is difficult for traditional security measures to adjust to the dynamic and programmable nature of SDN, which is why more intelligent solutions are required. The ability of deep learning (DL)-based intrusion detection systems (IDS) to acquire knowledge from data and distinguish complex attack trends in real time has led to their rising popularity. This research looks at software-defined networking (SDN) environments with intrusion detection systems (IDS) built on deep learning algorithms. Identify outliers, feature selection and architectures approaches are discussed. We have constructed and compared two models based on feature selection for comprehensive Intrusion Detection System (IDS) solution. One of these models uses CNN-LSTM architecture, using 48 features achieved the maximum accuracy, which was 99.5%. On the other hand, the CNN-LSTM model using 30 features achieved 98.9% accuracy.
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