# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 2 Related Work  
The book 'Big Data Analytics in Cyber Security', written by [9] emphasizes the significance of data analytics in leveraging cyber security. The authors suggest that by analyzing vast amounts of network data, Big Data is creating new opportunities for cyber security, such as building models and configuring network infrastructure for organizations. [PERSON]'s study demonstrates how the efficiency of machine learning algorithms in the Internet of Things (IoT) industry can improve network security by maintaining the reliability, security, availability, and survivability of security assets. Deep learning is recommended to enhance the performance of network building in smart cities and IoT devices [10]. Note that machine learning and IoT are becoming an integral part of modern life, with around 25 billion IoT-connected devices generating significant amounts of data that require analysis for improving performance. Machine learning algorithms, such as decision trees, neural networks and Bayesian networks, enable devices to identify patterns in datasets and make decisions based on their analysis. According to [11], the increasing frequency of cyber-attacks on physical systems is the biggest concern for industrial control systems (ICS). A multilayer cyber-attack detection system can be utilized for detecting cyber-attacks and establishing additional security within the network to prevent physical impacts and consequences. The research group in [12] presents a survey on the impact of security analytics, which concluded that analytics could assist network managers and IT security teams in real-time monitoring and surveillance of network activity. Machine learning can also help in detecting real-time malicious and suspicious patterns, making security analytics a crucial solution for cyber security in the near future. According to [13], deep learning can detect cyber-attacks and should be considered in building IoT devices and smart cities. The paper represented by [5] talks about a new intrusion detection system that can detect five different types of threats in a network, including Exploit, DOS, Probe, Generic, and Normal. The system is based on the UNSW-NB15 dataset and uses an integrated classification-based model to detect malicious activity. The proposed model is evaluated and found to have a considerably higher accuracy of 83.8% on a real-time data set generated at NIT Patna CSE lab. According to [6], machine learning-based intrusion detection systems are effective in identifying network attacks. However, these systems are challenged by high-dimensional data and imbalanced datasets, resulting in lower accuracy and higher false positive rates. A filter-based feature reduction method utilizing the XGBoost algorithm is presented by the author [6] along with an analysis of the UNSW-NB15 intrusion detection dataset. The study implemented various machine learning approaches, such as Support Vector Machine (SVM), k-Nearest-Neighbors (KNN), Logistic Regression (LR), Neural Network (NN), and Decision Tree (DT), using the reduced feature space. According to the findings, the XGBoost-based feature selection approach enables techniques like the DT to raise the test accuracy of the binary classification scheme from 88.13% to 90.85%. The authors in [14] discussed the importance of Network Intrusion Detection Systems (NIDS) in the context of internet security issues in the IoT environment. According to this study, the UNSW-NB15 dataset is more appropriate for evaluating NIDS. The experimental results show that the SVM method outperforms other methods, achieving an accuracy of 85.99% for binary-classification and 75.77% for multi-classification. The study represented by [15] discusses the problem of classifying network intrusions and how ordinary machine learning algorithms are not efficient due to the large amount of data. According to this research, deep Learning is suggested as a more effective solution due to its ability to classify with high dimensionality and complex features.