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Title	Data Flooding Intrusion Detection System For Manets Using Deep Learning Approach
ID_Doc	17213
Authors	Sbai O.; El Boukhari M.
Year	2020
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3419604.3419777
Abstract	Today mobile ad hoc networks (MANETs) and its derivatives such as vehicular ad-hoc networks (VANETs) wireless sensor network (WSN) are more interesting subject for researchers seen particularly from the appearance of the paradigm of smart cities smart homes and Internet of Things (IoT). In addition to this widespread use several vulnerabilities and attacks appear like for instance black hole attack and data flooding attack. Nevertheless the limitations of hardware generally used in MANETs make many views the tasks of detection and countermeasure of attacks. In this paper using the technology of deep neural network (DNN) deep learning we try to propose an intrusion detection system (IDS) for the subclass of the big class DDoS: Data flooding attack with using the dataset CICDDoS2019. Our obtained results show that the proposed architecture model can achieve very interesting performance (Accuracy Precision Recall and F1-score). © 2020 ACM.
Author Keywords	CICDDoS2019 dataset; Data-flooding attack; Deep learning; Deep Neural Network (DNN); Intrusion Detection System (IDS); Mobile ad hoc networks (MANETs)

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