Abstract
In modern era, the most pressing issue facing modern society is protection against cyberattacks on networks. The frequency of cyber-attacks in the present world makes the problem of providing feasible security to the computer system from potential risks important and crucial. Network security cannot be effectively monitored and protected without the use of intrusion detection systems (IDSs). DLTs (Deep learning methods) and MLTs (machine learning techniques) are being employed in information security domains for effectively building IDSs. These IDSs are capable of automatically and timely identifying harmful attacks. IntruDTree (Intrusion Detection Tree), a security model based on MLTs that detects attacks effectively, is shown in the existing research effort. This model, however, suffers from an overfitting problem, which occurs when the learning method perfectly matches the training data but fails to generalize to new data. To address the issue, this study introduces the MIntruDTree-HDL (Modified IntruDTree with Hybrid Deep Learning) framework, which improves the performance and prediction of the IDSs. The MIntruDTree-HDL framework predicts and classifies harmful cyber assaults in the network using an M-IntruDtree (Modified IDS Tree) with CRNNs (convolution recurrent neural networks). To rank the key characteristics, first create a modified tree-based generalized IDSs M-IntruDTree. CNNs (convolution neural networks) then use convolution to collect local information, while the RNNs (recurrent neural networks) capture temporal features to increase IDS performance and prediction. This model is not only accurate in predicting unknown test scenarios, but it also results in reduced computational costs due to its dimensionality reductions. The efficacy of the suggested MIntruDTree-HDL schemes was benchmarked on cybersecurity datasets in terms of precisions, recalls, fscores, accuracies, and ROC. The simulation results show that the proposed MIntruDTree-HDL outperforms current IDS approaches, with a high rate of malicious attack detection accuracy.