Firewall Decision Research Articles

Machine Learning Algorithm as a Firewall Decision and Reinforcement in Market Segmentation and Big Data

In the modern digital landscape, vast amounts of data are generated daily, challenging traditional analytical approaches in both data security and market segmentation. This paper presents a machine learning (ML) algorithm designed to serve a dual purpose: as a decision-support firewall system and as a reinforcement tool for market segmentation within big data environments. By integrating supervised and unsupervised learning models, the proposed algorithm effectively detects anomalies and potential security threats while also identifying distinct customer segments with high precision. The firewall decision-making component utilizes predictive models to detect malicious activity in real-time, enhancing cybersecurity by proactively learning from previous threat patterns. Concurrently, the reinforcement learning component analyses customer behaviour and preferences, dynamically adapting segmentation strategies to maximize marketing effectiveness. The dual implementation of ML in these domains demonstrates significant potential in improving both data security and personalized marketing outcomes. Experimental results indicate enhanced firewall accuracy and a refined segmentation process, suggesting that the proposed ML model provides a comprehensive solution for the challenges posed by big data in cybersecurity and market segmentation. s. Keywords Machine Learning; Firewall Decision; Reinforcement Learning; Market Segmentation; Big Data

FARE: FDD-based firewall anomalies resolution tool

Problems arising from firewall misconfigurations are common and have dramatic consequences for networks operations. Therefore, the discovery and removal of these misconfigurations is a serious and complex problem to solve. In this paper, we address this problem using a data structure (FDD: firewall decision diagram). We propose a new approach to rule-set optimization and clean-up, by removing superfluous rules from a simple firewall and a totally automatic method to detect and fix misconfigurations. We present also a new classification of anomalies in multi-firewall environment bringing out real configurations errors. We proved the correctness and completeness of our method and demonstrated its scalability and applicability on configurations provided by the Tunisian Ministry of Finance Computer Centre (CIMF), and found promising results.

Structured firewall design

A firewall is a security guard placed at the point of entry between a private network and the outside Internet such that all incoming and outgoing packets have to pass through it. The function of a firewall is to examine every incoming or outgoing packet and decide whether to accept or discard it. This function is conventionally specified by a sequence of rules, where rules often conflict. To resolve conflicts, the decision for each packet is the decision of the first rule that the packet matches. The current practice of designing a firewall directly as a sequence of rules suffers from three types of major problems: (1) the consistency problem, which means that it is difficult to order the rules correctly; (2) the completeness problem, which means that it is difficult to ensure thorough consideration for all types of traffic; (3) the compactness problem, which means that it is difficult to keep the number of rules small (because some rules may be redundant and some rules may be combined into one rule). To achieve consistency, completeness, and compactness, we propose a new method called structured firewall design , which consists of two steps. First, one designs a firewall using a firewall decision diagram instead of a sequence of often conflicting rules. Second, a program converts the firewall decision diagram into a compact, yet functionally equivalent, sequence of rules. This method addresses the consistency problem because a firewall decision diagram is conflict-free. It addresses the completeness problem because the syntactic requirements of a firewall decision diagram force the designer to consider all types of traffic. It also addresses the compactness problem because in the second step we use two algorithms (namely FDD reduction and FDD marking) to combine rules together, and one algorithm (namely firewall compaction) to remove redundant rules. Moreover, the techniques and algorithms presented in this paper are extensible to other rule-based systems such as IPsec rules.