# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 6 Conclusions and Future Work  
Cyber-attacks are a rapidly growing concern that poses a significant threat to global security. This study addresses this issue head-on by experimenting with the use of machine learning to detect and protect networks from these malicious acts. The study uses the UNSW_NB15 dataset, published by the ACCS lab in Australia, which contains 25,40,044 records. The dataset underwent various data cleansing and pre-processing activities to achieve the highest possible accuracy and the lowest false alarm rate. A variety of machine learning algorithms such as Logistic Regression, Support Vector Machines, Decision Trees, and Random Forests are used to solve this classification problem. The dataset is divided into training and testing sets, and some experiments were carried out on them to evaluate the effectiveness of the search. By experimenting with the feature selection approach, this study found that the Random Forest model has the highest accuracy of 99.45% and the lowest false alarm rate of 1.94% compared to the other recently published models. Although it is of a low value, the FAR will impact on the network bandwidth depending on the actions taken upon the intrusions being detected. When an IDS host detects an intrusion (but a false detection), it might coordinate and take actions with surrounding hosts of the same network. In such a case, unnecessary data exchange will occur, which will generate unnecessary network traffic, reducing an effective network bandwidth for data transmission. The penalty will be proportional to the value of FAR and the number of hosts involved in the intrusion detections and actions. This study not only demonstrates the effectiveness of machine learning in protecting networks from cyber-attacks, but it also highlights the crucial role of analytics in the cyber security world. The results of this study conclusively show that the Random Forest model is an efficient and superior approach for detecting cyber-attacks and securing networks. Overall, this research provides valuable insights into the potential of machine learning as a tool for defending against cyber-attacks and protecting networks. Although impressive accuracy and F1-score results were achieved in the experiment, there is still room for improvement and further optimization. More testing is necessary to evaluate the experiment's performance on actual network traffic data, and new network-related features can be derived from existing features. Additionally, it would be beneficial to experiment on a balanced dataset. There is a significant scope to experiment with deep learning and artificial neural networks. While the current accuracy is close to 100%, it is insufficient for a Network Intrusion Detection System, and additional research should focus on developing new methodologies and approaches to improve its performance. Conceptualization, S.M. and A.T.A. ; methodology, S.M. and A.T.A. ; software, S.M. ; validation, S.M., M.I., H.M. and A.T.A. ; formal analysis, S.M. and M.I. ; investigation, S.M., M.I., H.M. and A.T.A. ; resources, S.M. and A.T.A. ; data curation, S.M. and M.I. ; writing--original draft preparation, S.M. ; writing--review and editing, M.I., H.M. and A.T.A. ; visualization, S.M. and M.I. ; supervision, M.I., H.M. and A.T.A. ; project administration, A.T.A. ; funding acquisition, A.T.A. All authors have read and agreed to the published version of the manuscript. The work of A.T.A. was supported in part by the Monash University Indonesia--Seed Grant, grant number IF112034.