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Firewalls are critical components in securing communication networks byscreening all incoming (and occasionally exiting) data packets. Filtering iscarried out by comparing incoming data packets to a set of rules designed toprevent malicious code from entering the network. To regulate the flow of datapackets entering and leaving a network, an Internet firewall keeps a track ofall activity. While the primary function of log files is to aid introubleshooting and diagnostics, the information they contain is also veryrelevant to system audits and forensics. Firewalls primary function is toprevent malicious data packets from being sent. In order to better defendagainst cyberattacks and understand when and how malicious actions areinfluencing the internet, it is necessary to examine log files. As a result,the firewall decides whether to 'allow,' 'deny,' 'drop,' or 'reset-both' theincoming and outgoing packets. In this research, we apply variouscategorization algorithms to make sense of data logged by a firewall device.Harmonic mean F1 score, recall, and sensitivity measurement data with a 99%accuracy score in the random forest technique are used to compare theclassifier's performance. To be sure, the proposed characteristics didsignificantly contribute to enhancing the firewall classification rate, as seenby the high accuracy rates generated by the other methods.

Machine Learning Approach on Multiclass Classification of Internet Firewall Log Files