# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 6 Conclusions  
The challenge of creating tools and methods for identifying and blocking unauthorized access to networked and computer systems falls under the purview of the computer science and engineering discipline known as NIDS. It is possible to recognize and respond to potential IoT security risks using intrusion detection systems (IDS), firewalls, and other IoT security technologies and methodologies. The R2L and U2R attacks in the NSL-KDD dataset, which are the categories with the lowest performance, will be overlooked more frequently in the future. Thus, a method was planned to be developed in order to enhance this detection rate. A variety of DL approaches were utilized in this work to find anomalies in IDS. The approach produced convincing and trustworthy results when measured against various parameters. One of the work's most noteworthy accomplishments was using the feature selection strategy to train classifiers on the most significant feature correlations while avoiding missed leads during training, to give the best results. The proposed strategy focused on binary classification using DL techniques. When the results of the algorithms were compared, some classifiers' results were close together, and the CNN classifier generated the best results. Deep learning demonstrated its viability and superiority when applied to the binary classification of network IDS.  
The NSL-KDD data package also includes internet traffic records from the fundamental intrusion detection system. R2L, DoS, U2R, and unauthorized remote system access are the four types of assaults included in the target variable \"Class\". The same hybrid model outperformed the MLP model for all-class classification. Class 1 was correctly identified for 1467 out of 2885, Class 2 was accurately predicted for 2215 out of 2421, and Class 0 was correctly anticipated for 6945 out of 7460. The hybrid model had accuracy, F1 score, precision, recall, and AUC score greater than 89%. The F1 score and accuracy of the projected network model on the KDDTest+ test set were 90.45% and 89.12%, respectively. According to the comparison, the suggested MLP and hybrid models outperformed prior reference network models in categorization. The proposed paradigm is helpful in NIDS development.  
Future research on intrusion detection and network security can explore improving the detection of R2L and U2R attacks, exploring different deep learning architectures, advancing feature selection strategies, extending the approach to multi-class classification, conducting real-world evaluations, and enhancing the interpretability of deep learning models for network administrators and security analysts.  
Conceptualization, M.Z. and S.A.A.; methodology, M.Z.; software, M.S.A.-R.; validation, M.Z., S.A.A., and M.Z.; formal analysis, S.A.A.; investigation, M.Z.; resources, M.S.A.-R.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, M.S.A.-R.; visualization, S.A.A.; supervision, M.Z.; project administration, M.S.A.-R.; funding acquisition, S.A.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, for funding this research (IFKSURC-1-7104).  
Not applicable.  
The dataset used in this study is publicly available at [[https://www.kaggle.com/datasets/hassan06/nslkdd](https://www.kaggle.com/datasets/hassan06/nslkdd)]([https://www.kaggle.com/datasets/hassan06/nslkdd](https://www.kaggle.com/datasets/hassan06/nslkdd)) (accessed on 20 January 2018).