E3S Web of Conferences · 2020 · 38 citations · 12 references
ComputingComputer ScienceCybersecurityComputer SecurityMachine LearningNetwork SecurityAnomaly DetectionDecision TreesDenial-of-service AttacksIntrusion DetectionNetwork Traffic AnalysisSupport Vector MachinesDdos DetectionDistributed Denial Of ServiceHttp Flood AttacksHttp FloodingMachine Learning For Intrusion DetectionMultilayer PerceptronNaive BayesNaive Bayes Classification
The DDoS attacks are the most destructive attacks that interrupt the safe operation of essential services delivered by the internet community’s different organizations. DDOS stands for Distributed Denial Of Service attacks. These attacks are becoming more complex and expected to expand in number day after day, rendering detecting and combating these threats challenging. Hence, an advanced intrusion detection system (IDS) is required to identify and recognize an- anomalous internet traffic behaviour. Within this article the process is supported on the latest dataset containing the current form of DDoS attacks including (HTTP flood, SIDDoS). This study combines well-known grouping methods such as Naïve Bayes, Multilayer Perceptron (MLP), and SVM, Decision trees.
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NETWORK-BASED INTRUSION DETECTION USING NEURAL NETWORKS
Alan Bivens, Chandrika Palagiri, R.N. Smith et al. · 2002 · 178 citations