Machine Learning Based Intrusion Detection in IoT Network Using MLP and LSTM
Keywords:
Intrusion Detection System, IDS, Machine Learning, MLP, LSTM, CICDDoS2019, Network SecurityAbstract
Each network's security design must include an intrusion detection system. Monitoring and analysing network traffic is its main purpose in order to spot and halt hazardous activities. Machine learning algorithms have shown considerable promise in the realm of intrusion detection systems due to their ability to learn from large and complex data sets (IDS). In intrusion detection systems, the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) classifiers are two of the more popular machine learning techniques (IDS). We conducted an investigation in which we compared the performance of MLP and LSTM classifiers for IDS using the CICDDoS2019 dataset. We utilised label encoding to perform some basic processing on the dataset before using feature selection to identify the most important features. Using the preprocessed dataset for training and testing, the MLP and LSTM classifiers' performance was assessed twice, and the results were compared in terms of accuracy and loss. The results of our study show that both classifiers were capable of reaching high accuracy with little loss, with the LSTM classifier doing just slightly better than the MLP classifier in terms of accuracy and loss. The results of this research can help security experts and researchers choose the machine learning algorithm for IDS that is most appropriate for them based on the particular requirements and criteria they have.
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