Biblio

Intrusion Detection System (IDS) is used to identify malicious traffic on the network. Apart from rule-based IDS, machine learning and deep learning based on IDS are also being developed to improve the accuracy of IDS detection. In this study, the public dataset CIC IDS 2017 was used in developing deep learning-based IDS because this dataset contains the new types of attacks. In addition, this dataset also meets the criteria as an intrusion detection dataset. The dataset was split into train data, validation data and test data. We proposed Bidirectional Long-Short Term Memory (LSTM) for building neural network. We created 24 scenarios with various changes in training parameters which were trained for 100 epochs. The training parameters used as research variables are optimizer, activation function, and learning rate. As addition, Dropout layer and L2-regularizer were implemented on every scenario. The result shows that the model used Adam optimizer, Tanh activation function and a learning rate of 0.0001 produced the highest accuracy compared to other scenarios. The accuracy and F1 score reached 97.7264% and 97.7516%. The best model was trained again until 1000 iterations and the performance increased to 98.3448% in accuracy and 98.3793% in F1 score. The result exceeded several previous works on the same dataset.