A model for multi-attack classification to improve intrusion detection
  performance using deep learning approaches
Arun Kumar Silivery Ram Mohan Rao Kovvur
This proposed model introduces novel deep learning methodologies. The
objective here is to create a reliable intrusion detection mechanism to help
identify malicious attacks. Deep learning based solution framework is developed
consisting of three approaches. The first approach is Long-Short Term Memory
Recurrent Neural Network (LSTM-RNN) with seven optimizer functions such as
adamax, SGD, adagrad, adam, RMSprop, nadam and adadelta. The model is evaluated
on NSL-KDD dataset and classified multi attack classification. The model has
outperformed with adamax optimizer in terms of accuracy, detection rate and low
false alarm rate. The results of LSTM-RNN with adamax optimizer is compared
with existing shallow machine and deep learning models in terms of accuracy,
detection rate and low false alarm rate. The multi model methodology consisting
of Recurrent Neural Network (RNN), Long-Short Term Memory Recurrent Neural
Network (LSTM-RNN), and Deep Neural Network (DNN). The multi models are
evaluated on bench mark datasets such as KDD99, NSL-KDD, and UNSWNB15 datasets.
The models self-learnt the features and classifies the attack classes as
multi-attack classification. The models RNN, and LSTM-RNN provide considerable
performance compared to other existing methods on KDD99 and NSL-KDD dataset