Biblio

With the advancement of network technology, users can now easily gain access to and benefit from networks. However, the number of network violations is increasing. The main issue with this violation is that irresponsible individuals are infiltrating the network. Network intrusion can be interpreted in a variety of ways, including cyber criminals forcibly attempting to disrupt network connections, gaining unauthorized access to valuable data, and then stealing, corrupting, or destroying the data. There are already numerous systems in place to detect network intrusion. However, the systems continue to fall short in detecting and counter-attacking network intrusion attacks. This research aims to enhance the detection of Denial of service (DoS) by identifying significant features and identifying abnormal network activities more accurately. To accomplish this goal, the study proposes an Intrusion Analysis System for detecting Denial of service (DoS) network attacks using machine learning. The accuracy rate of the proposed method using random forest was demonstrated in our experimental results. It was discovered that the accuracy rate with each dataset is greater than 98.8 percent when compared to traditional approaches. Furthermore, when features are selected, the detection time is significantly reduced.

It is clear that cyber-attacks are a danger that must be addressed with great resolve, as they threaten the information infrastructure upon which we all depend. Many studies have been published expressing varying levels of success with machine learning approaches to combating cyber-attacks, but many modern studies still focus on training and evaluating with very outdated datasets containing old attacks that are no longer a threat, and also lack data on new attacks. Recent datasets like UNSW-NB15 and SANTA have been produced to address this problem. Even so, these modern datasets suffer from class imbalance, which reduces the efficacy of predictive models trained using these datasets. Herein we evaluate several pre-processing methods for addressing the class imbalance problem; using several of the most popular machine learning algorithms and a variant of UNSW-NB15 based upon the attributes from the SANTA dataset.

Deep Learning has been proven more effective than conventional machine-learning algorithms in solving classification problem with high dimensionality and complex features, especially when trained with big data. In this paper, a deep learning binomial classifier for Network Intrusion Detection System is proposed and experimentally evaluated using the UNSW-NB15 dataset. Three different experiments were executed in order to determine the optimal activation function, then to select the most important features and finally to test the proposed model on unseen data. The evaluation results demonstrate that the proposed classifier outperforms other models in the literature with 98.99% accuracy and 0.56% false alarm rate on unseen data.