Classification of Firewall Log Data Using Multiclass Machine Learning Models

These days, we are witnessing unprecedented challenges to network security. This indeed confirms that network security has become increasingly important. Firewall logs are important sources of evidence, but they are still difficult to analyze. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have emerged as effective in developing robust security measures due to the fact that they have the capability to deal with complex cyberattacks in a timely manner. This work aims to tackle the difficulty of analyzing firewall logs using ML and DL by building multiclass ML and DL models that can analyze firewall logs and classify the actions to be taken in response to received sessions as “Allow”, “Drop”, “Deny”, or “Reset-both”. Two sets of empirical evaluations were conducted in order to assess the performance of the produced models. Different features set were used in each set of the empirical evaluation. Further, two extra features, namely, application and category, were proposed to enhance the performance of the proposed models. Several ML and DL algorithms were used for the evaluation purposes, namely, K-Nearest Neighbor (KNN), Naïve Bayas (NB), J48, Random Forest (RF) and Artificial Neural Network (ANN). One interesting reading in the experimental results is that the RF produced the highest accuracy of 99.11% and 99.64% in the first and the second experiments respectively. Yet, all other algorithms have also produced high accuracy rates which confirm that the proposed features played a significant role in improving the firewall classification rate

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks