The proliferation of Internet of Things (IoT) devices has expanded the attack
surface, necessitating efficient intrusion detection systems (IDSs) for network
protection. This paper presents FLARE, a feature-based lightweight aggregation
for robust evaluation of IoT intrusion detection to address the challenges of
securing IoT environments through feature aggregation techniques. FLARE
utilizes a multilayered processing approach, incorporating session, flow, and
time-based sliding-window data aggregation to analyze network behavior and
capture vital features from IoT network traffic data. We perform extensive
evaluations on IoT data generated from our laboratory experimental setup to
assess the effectiveness of the proposed aggregation technique. To classify
attacks in IoT IDS, we employ four supervised learning models and two deep
learning models. We validate the performance of these models in terms of
accuracy, precision, recall, and F1-score. Our results reveal that
incorporating the FLARE aggregation technique as a foundational step in feature
engineering, helps lay a structured representation, and enhances the
performance of complex end-to-end models, making it a crucial step in IoT IDS
pipeline. Our findings highlight the potential of FLARE as a valuable technique
to improve performance and reduce computational costs of end-to-end IDS
implementations, thereby fostering more robust IoT intrusion detection systems.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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