Intelligent Defense and Filtration Platform for Network Traffic

Systems and methods for detecting and preventing cyber-attacks on communication networks provide a hybrid anomaly detection module (HADM) that uses a combination of linear algorithms and learning algorithms. The linear algorithms filter and extract distinctive attributes and features of the cyber-attacks and the learning algorithms use these attributes and features to identify new types of cyber-attacks. The learning algorithms, which may be algorithms that employ Artificial Neural Networks (ANN), Genetic Algorithm (GA), Extreme Learning Machines (ELM), Self-Organizing Map (SOM), Multi-Layer Perceptron (MLP), or Swarm intelligence (SI)and the like, have better detection accuracy when they are used along with linear algorithms, such as algorithms that employ Decision Tree,Support Vector Machine,or Fuzzy Ruleand the like. The use of linear algorithms in conjunction with learning algorithms allows the HADM to achieve improved cyber-attack detection over existing solutions.