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A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks
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DOI：10.1109/ACCESS.2020.2976908.png)
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摘要
En 中文
As novel technologies continue to reshape the digital era, cyberattacks are also increasingly becoming more commonplace and sophisticated. Distributed denial of service (DDoS) attacks are, perhaps, the most prevalent and exponentially-growing attack, targeting the varied and emerging computational network infrastructures across the globe. This necessitates the design of an efficient and early detection of large-scale sophisticated DDoS attacks. Software defined networks (SDN) point to a promising solution, as a network paradigm which decouples the centralized control intelligence from the forwarding logic. In this work, a deep convolutional neural network (CNN) ensemble framework for efficient DDoS attack detection in SDNs is proposed. The proposed framework is evaluated on a current state-of-the-art Flow-based dataset under established benchmarks. Improved accuracy is demonstrated against existing related detection approaches.
Keyword:
Computer crime
Machine learning
Software
Anomaly detection
Feature extraction
Benchmark testing
Computer architecture
Software defined network (SDN)
anomaly detection
distributed denial of service (DDoS)
deep learning
deep convolutional neural network (CNN)
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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