Hybrid-Network Intrusion Detection (H-NID) Model Using Machine Learning Techniques (MLTs)

Providing computer security is one of a significant challenge. Software and its mechanisms have been established to provide the security to avoid intrusion, which includes Intrusion Detection Systems (IDS). IDS helps to detect the exertions to outbreak a network and detect anomalous actions and its activities. It includes the details of uncertainty in probing of different kinds of attacks. IDS demands the need for combination of Machine Learning (ML) methods integrated into a hybrid model. In this paper, the Hybrid Machine Learning (HML) model is proposed that predicts the attacks against the network provided with better performance. The proposed system includes an innovative IDS having good network act helping to perceive the strange attacks, achieved by ML algorithms such as Decision Tree (DT), Naive Bayes (NB), Random Forest (FR), K-Means, and SVM. The algorithms with the good results are used to build a hybrid model. The proposed HML method improves the accurateness and efficiency for detecting the attacks by the IDS system. This research work recommends a system for access within a hybrid model, i.e., Hybrid-Network Intrusion Detection (H-NID) which is a hybrid network combination of DT, K-NN, NB, SVM, and RF to extract temporary and local data of network traffic which advances the accurateness of IDS. The training phase in H-NID uses stage wise approaches to scale out the model. This technique decreases this consequence quantity of unparalleled trials of various attacks on training of model execution. It progresses the strength of training and prediction. Lastly, test of H-NID is done for several types of network traffic from the CICIDS-2017 database as it works on a real network traffic dataset that simulates real-world conditions. The predicted results demonstrate that H-NID got 98.50% of accuracy, and the accuracy for each type of attack remained more than 94.65%, which achieved excellent results in all models. Various rules and restrictions do not work well.