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Distributed Denial of Service (DDoS) attack is a malicious attempt to disruptthe normal traffic of a targeted server, service or network by overwhelming thetarget or its surrounding infrastructure with a flood of Internet traffic.Emerging technologies such as the Internet of Things and Software DefinedNetworking leverage lightweight strategies for the early detection of DDoSattacks. Previous literature demonstrates the utility of lower number ofsignificant features for intrusion detection. Thus, it is essential to have afast and effective security identification model based on low number offeatures.  In this work, a novel Attention-based Isolation Forest Intrusion DetectionSystem is proposed. The model considerably reduces training time and memoryconsumption of the generated model. For performance assessment, the model isassessed over two benchmark datasets, the NSL-KDD dataset & the KDDCUP'99dataset. Experimental results demonstrate that the proposed attention augmentedmodel achieves a significant reduction in execution time, by 91.78%, and anaverage detection F1-Score of 0.93 on the NSL-KDD and KDDCUP'99 dataset. Theresults of performance evaluation show that the proposed methodology has lowcomplexity and requires less processing time and computational resources,outperforming other current IDS based on machine learning algorithms.

ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion Detection System