Abstract
Intrusion detection has become a major need for the current networked environment due to the high usage levels and the mandatory security that is needed, as sensitive information are being shared in the network. However, there exist several intrinsic issues in the network data that complicates the detection process. Further, real-time detection is also required due to the high velocity of data flow that can be expected in the domain. This paper presents an ensemble-based intrusion detection model to handle data imbalance and noise. Further, the entire approach has been decentralized to enable parallelized detection. The proposed model utilizes a BAgged Stacking Ensemble (BASE) as the detection model. The ensemble architecture initially creates data bags, enabling distributed processing. The bags are processed by multiple heterogeneous base learners. Prediction results from the base learners are passed to a stacked classifier for final predictions. This ensemble model is distributed over the network to enable decentralized processing. Experiments were performed on the NSL-KDD data and the results were compared with recent models. Comparisons with state-of-the-art models indicate the effectiveness of the proposed model.
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Sanjith, S.L., George Dharma Prakash Raj, E. (2020). Decentralized Bagged Stacking Ensemble Mechanism (DBSEM) for Anomaly Detection. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 89. Springer, Singapore. https://doi.org/10.1007/978-981-15-0146-3_71
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DOI: https://doi.org/10.1007/978-981-15-0146-3_71
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