Abstract
Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains.
Highlights
Intrusion detection is a method for observing and tracking events on a computer system
This study proposes an automatic and efficient network intrusion detection system based on the Adaboost technique
We evaluate the results of Support vector machine (SVM), artificial neural network (ANN), and Adaboost on top of the decision tree classifier for a binary classification problem of network intrusion detection on the UNSW-NB15 dataset
Summary
Intrusion detection is a method for observing and tracking events on a computer system. With the exponential increase in internetbased facilities, the number of devices for computing and consumers connected to data networks and computer networks has increased significantly. These devices provide and serve online services to users and public/private sector organizations. The intrusion detection system is one of the methods used to address the network security issue. An intrusion detection system is a technique for tracking network activities between different entities by estimating their integrity and availability principles [10,11]. The second step covers the construction of features passed to the decision-making method to identify possible threats [12]. The previous research studies have used KNN, SVM, and ANN models for network anomaly detection [13,14,15]
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