Enhancing DoS Attack Prediction: A Comparative Study of Ensemble Learning and Hybrid Models in Network Security
Keywords:
Network Security, DoSAbstract
In the digital age, Distributed Denial of Service (DDoS) attacks pose one of the most formidable threats to computer networks and systems. These malicious assaults bombard targeted systems with an overwhelming volume of traffic originating from multiple sources, effectively incapacitating the affected services (Kaur & Arora, 2020). The exigencies of cybersecurity demand immediate and reliable detection methods for such attacks to mitigate their impact effectively. However, existing methodologies for detecting DDoS attacks frequently grapple with high incidences of false positives, thereby reducing operational efficacy (Bertino & Islam, 2017). Furthermore, traditional classifiers employed in these systems often fail to grasp the complex and multifarious patterns typical of DDoS attack traffic, contributing to diminished detection accuracy (Hussain et al., 2021). Therefore, there is a pressing need to refine existing methodologies for identifying DDoS assaults, with a focus on hybrid machine learning models and ensemble learning techniques.
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Copyright (c) 2024 S. Veerakannan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
