Deep learning based cyber bullying early detection using distributed denial of service flow

Cyber-bullying has been on the rise especially after the explosive widespread of various cyber-attacks. Various types of techniques have been used to tackle cyber-bullying. These techniques focused primarily on data traffic for monitoring malicious activities. This research proposes a methodology where we can detect early Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks. First, we formulate the problem in a practical scenario by comparing flow and non-flow-based datasets using Mann Whitney U statistical test. Flow and non-flow-based datasets and Artificial Neural Network (ANN) and Support Vector Machine (SVM) is used for classification. To keep original features, we use variance, correlation, ¾ quartile method to eliminate the unimportant features. The forward selection wrapper method for feature selection is used to find out the best features. To validate the proposed methodology, we take multiple DoS and DDoS single flow and validate it on 10%, 20%, 30%, 40%, and 50%. For validation, the experimental results show + 90% accuracy on the early 10% flow.