Intrusion Detection System in Software Defined Networks using Machine Learning Approach

Jayasri, P; Atchaya, A; M, Sanfeeya Parveen; Ramprasath, J.

doi:10.22161/ijaers.84.16

Cited by 8 publications

(2 citation statements)

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“…Miter iter (9) By expanding the search, the fight step size becomes progressively smaller. Initially, the crow is a large fight step size that makes the crow have a strong global searching ability.…”

Section: Adaptive Fight Step Adjustment Strategymentioning

confidence: 99%

“…Currently, the SDN-assisted structure not only improves the heterogeneous capability of the IoT network and its dynamic atmosphere but also provides a chance to ease the network management process [8]. It offers an effective and efficient identification method without exhaustion and a platform for resource-limited gadgets that do not burden a security solution [9]. For SDN surveillance, an optimal technique was proposed by incorporating the Intrusion Detection System (IDS) in SDN architecture.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

Motwakel¹,

Alrowais²,

Tarmissi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such cyberattacks. With this motivation, the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection (ECSADL-CAD) model for the Software-Defined Networking (SDN)-enabled IoT environment. The presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT environment. To attain this, the ECSADL-CAD model initially pre-processes the data. In the presented ECSADL-CAD model, the Reinforced Deep Belief Network (RDBN) model is employed for attack detection. At last, the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes. A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD model. The experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

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Section: Adaptive Fight Step Adjustment Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%