Intrusion detection in wireless sensor networks using Deep Learning

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dc.contributor.authorHamel, Toufik
dc.contributor.authorMakhlouf, Bassem
dc.contributor.authorBourouis, Abdelhabib
dc.date.accessioned2023-11-27T21:22:14Z
dc.date.available2023-11-27T21:22:14Z
dc.date.issued2023
dc.description.abstractThe humanspecies, driven by the desire for a more adequate life, has constantlystrived to advance andcreate a modern civilization. Technology has been an unstoppable force pushing us into a brighter future, marked by great innovations and new challenges. Wireless Sensor Networks (WSN) have emerged as a prominent topic in ourcontemporary society, serving as a gateway to realizing the vision of global smart citiesthrough Internet of Things (IoT) devices. These networks are finding applications in fields as diverse as telemedicine and smart agriculture, offeringexcitingopportunities. However, WSNs face ongoingcybersecuritythreats. Whetherit is deliberate actions by enemies ormismanagement of the system, the security of wireless networks is of paramount importance, presentingsignificant challenges. The limitations inherent in sensors, includinglimited memory and powerconsumption, makesecuritymeasures a complextask. It is critical to design security solutions thatconsidertheseconstraints, ensuring optimal network performance withoutdelays, packetloss, or abnormalfunctionality. This thesis proposes an intrusion detection system (IDS) engine based on deeplearning techniques, with the aim of achievingsuperiorpacket classification results. The NSL-KDD dataset is used to evaluate the performance of the model. The data undergoespre-processing before beingfedinto thedeeplearning (DNN) model. Binary classification is used to distinguishbetween normal and abnormaltrafficusing a six-layer neural network consisting of an input layer, four hiddenlayers, and an output layer. The resultsobtained in thisstudyshowed high precision and accuracycompared to what the researchersachieved in theirpublishedpapers. The model achieves an accuracy of 99.65% and an F1 score of 99.67%.
dc.identifier.urihttp://dspace.univ-oeb.dz:4000/handle/123456789/17062
dc.language.isoen
dc.publisherUniversity of Oum El Bouaghi
dc.titleIntrusion detection in wireless sensor networks using Deep Learning
dc.typeOther
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