Abstract
Nowadays computer network traffic is increasing exponentially making the Internet vulnerable to huge number of security threats. Distributed Denial of Service (DDoS) attacks is one such threats that deny services to legitimate users. This necessitates a system for classifying DDoS attacks to protect the computing services provided to legitimate users. Recently, deep learning techniques have been proposed to detect DDoS attacks. The existing deep learning-based classification systems perform static detection of attacks failing to capture unknown attacks happening in the evolving traffic. The unknown attacks could be detected on the fly if a generalizable model is designed for each evolving class of network traffic. This is effectively represented in the proposed Deep Autonomous Learning (DAL) classifier to detect DDoS attacks in the traffic data streams. The proposed DAL network traffic classifier learns the features automatically using incremental learning with distilled cross entropy and classifies the evolving network traffic using softmax function. The proposed classification approach was tested by conducting experiments on benchmark datasets generated from network traffic. It is evident that the DAL network classification approach has the potential to classify the unknown DDoS attacks. Further, it is observed from the comparative analysis that the proposed DAL classifier outperforms the existing approaches.
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Bhuvaneswari Amma, N.G. (2022). Network Traffic Classification Using Deep Autonomous Learning Approach. In: Rao, U.P., Patel, S.J., Raj, P., Visconti, A. (eds) Security, Privacy and Data Analytics. Lecture Notes in Electrical Engineering, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-16-9089-1_15
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DOI: https://doi.org/10.1007/978-981-16-9089-1_15
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