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Neural networks Misuse intrusion detection KDD features Attack categories a b s t r a c t This paper presents a critical study about the use of some neural networks (NNs) to detect and classify intrusions. The aim of our research is to determine which NN classifies well the attacks and leads to the higher detection rate of each attack. This study focused on(More)
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—Wireless sensor networks (WSN) are constrained by the processing speed, storage capacity, collision avoidance and energy which they are fundamental aspects in the development of communication protocols, and in this context, most research projects in this area focus on the aspect of energy, and ignore some QoS supports also interesting and unexplored, such(More)
We introduce a novel algorithm to detect unknown attacks, based on the Communicating Ant for Clustering (CAC) [1], which despite the other ants algorithm, lead to a better detection rate (DR). Secondly, having noted the low DR of R2L attacks, we improve this approach by hybridizing it with association rules approach. In addition to the measure of similarity(More)
In this work we present two approaches to improve the " Collision Avoidance " mechanism of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. Our first approach : the " Uniform Law with Doubling Interval CSMA/CA (ULDI_CSMA/CA) " uses a uniform law in an interval of values I=[a, b] to calculate the random starting time when(More)