# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 8 Conclusions and Future Work  
Currently, the most urgent issue facing modern society is network attacks. All networks are susceptible to network risks, regardless of size. A network must have an intrusion detection system (ID) for detecting and mitigating hostile attacks. As malicious threats continually emerge and evolve, the network requires a highly advanced security solution. Using deep learning to detect malicious traffic enables the detection of various changes in traffic, which in turn enhances the performance and allows only normal traffic to pass into the system. Due to this, the development of an effective and intelligent ID system is of the utmost importance. In this study, a hybrid ID framework based on Deep Learning is created using a convolutional recurrent neural network (CRNN) that detects hostile network attacks. The model is built by combining an RNN with a CNN in which two convolutional layers are followed up by various RNN layers; the result is then passed into fully connected, flattened, and SoftMax layers that make the model capable of detecting and classifying traffic. To enhance the accuracy and predictability of the ID system, the CNN collects local features via convolution, whereas a deep-layered RNN captures HDLNIDS features. Experiments are conducted using publicly available intrusion detection data, in particular, the modern and realistic CICIDS-2018 data, to determine the efficacy of the proposed HDLNIDS system. The simulation results demonstrate that the proposed HDLNIDS gives promising results in terms of accuracy and data loss. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Dataset** & **Methodology** & **Results** & **Limitations** \\ \hline \multirow{3}{*}{[PERSON] et al. [52]} & \multirow{3}{*}{KDD99} & \multirow{3}{*}{DNN, LSTM, CNN} & Reported accuracy of 98.5\%, & \multirow{3}{*}{Limited to 3 classes only: SQL, XSS, and DoS} \\  & & & Precision of 97.63\%, and & & \\  & & & recall of 99.59\% & & \\  & & & (multi-class classification) & & \\ \hline \multirow{3}{*}{[PERSON] et al. [53]} & \multirow{3}{*}{UNSW-NB15, NSL-KDD} & Deep Belief Networks (DBF) and modified density peak & Reported FPR of 2.62\% and accuracy of 82.08\% & \multirow{3}{*}{\begin{tabular}{c} They synthesized U2R \\ and R2L attacks to \\ increase the \\ model performance. \\ \end{tabular} } \\  & & & clustering algorithm & & \\ \hline \multirow{3}{*}{[PERSON] et al. [54]} & \multirow{3}{*}{AWID} & \multirow{3}{*}{Sparse Autoencoder} & Reported F1-score of 89.06\%, & \multirow{3}{*}{-} \\  & & detection Rate of 92.18\%, and & & \\  & & accuracy of 94.81\% for & & \\  & & multi-class classification & & \\ \hline \multirow{3}{*}{[PERSON] et al. [55]} & \multirow{3}{*}{CICIDS 2018} & \multirow{3}{*}{Rule-Based Classifiers} & Reported accuracy 99.9\% & \multirow{3}{*}{\begin{tabular}{c} Lack of detail about \\ experiments and \\ unknown measure of \\ buildup time. \\ \end{tabular} } \\  & & & \\ \hline [PERSON] et al. [56] & \multirow{3}{*}{CICIDS 2018} & \multirow{3}{*}{Random Forest} & Reported accuracy 99.9\% & \multirow{3}{*}{\
\begin{tabular}{c} Classification \\ information is \\ not available. \\ \end{tabular} } \\ \hline [PERSON] et al. [57] & \multirow{3}{*}{UNSW-NB15} & \multirow{3}{*}{Various ML algorithms} & Reported average accuracy of 99\% & \multirow{3}{*}{
\begin{tabular}{c} Conducted experiments \\ using hand- \\ engineered methods. \\ \end{tabular} } \\ \hline Proposed & \multirow{3}{*}{CICIDS-2018} & \multirow{3}{*}{CNN, RNN} & Average Accuracy of 98.90\%, & \multirow{3}{*}{-} \\  & & F-measure of 99.03, precision & & \\  & & of 98.64\%, recall of 99.15\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._In the near future, we want to expand our model with more parameters that contribute to enhanced performance and detection, by designing more effective algorithms for other malicious network traffic using multiple deep learning techniques. We will also analyze and expand our model's capacity to handle zero-day assaults as our initial avenue for improvement.