Abstract:
Intrusions are consistent torment that networks have to encounter. To get rid of these
malicious attacks a variety of approaches have been developed, the use of machine
learning and deep learning is considered the most effective strategy to detect multifarious
attacks. This research presents two different intrusion detection systems that have been
trained by applying machine learning algorithms like Random Forest (RF), Support
Vector Machine (SVM), and deep learning algorithm Long-Short Term Memory (LSTM).
For better training of algorithms, we have developed an effective training dataset by
merging BoT-IoT and NB-15 and balancing the classes. For machine learning using
Random Forest (RF) and Support Vector Machine (SVM), we get the accuracy of 98.32%
and 99.60% respectively. For deep learning using LSTM, we achieve an accuracy of
99.93% for 3-class classification and 99.98% for 5-class classification. Hyperparameter
tuning and cross-validation techniques have also been used for effective training, and
less model execution or training time. The results show that our approach of merging
and balancing datasets outputs better accuracy as compared to state-of-the-art machine
learning and deep learning algorithms
