Abstract
Firewalls are essential devices to protect the communication networks by means of filtering out all incoming (and sometimes outgoing) traffic packets. The filtration process is performed by matching the traffic packets against predefined rules aiming to preclude cyber-threats from getting into the network. Accordingly, the firewall system proceeds with either to “allow,” “deny,” or “drop/reset” the incoming packet. Thus, an automated smart actions’ classification process is essential for improved firewall operations. In this paper, we propose an intelligent classification model that can be employed in the firewall systems to produce proper action for every communicated packet by analyzing packet attributes using a shallow neural network (SNN). Specifically, the proposed model employs SNN with 150-neurons at the hidden layer to train and classify the Internet Firewall-2019 (IFW-2019) dataset into three classes, including: “allow, “deny,” and “drop/reset.” The experimental results exhibited our classification model's superiority, scoring an overall accuracy of 98.5% with a cross-entropy loss of 0.022 attained after 381 epochs for the 3-class classifier. Also, the proposed model was evaluated using several other evaluation metrics, including confusion matrix parameters, positive predictive value, true positive rate, harmonic mean, and false positive/negative rates. Eventually, the proposed model outperformed many other recent firewall classification systems in the same area of study.
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Al-Haija, Q.A., Ishtaiwi, A. (2022). Multiclass Classification of Firewall Log Files Using Shallow Neural Network for Network Security Applications. In: Ranganathan, G., Fernando, X., Shi, F., El Allioui, Y. (eds) Soft Computing for Security Applications . Advances in Intelligent Systems and Computing, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-5301-8_3
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