DSSF: Decision Support System to Detect and Solve Firewall Rule Anomalies based on a Probability Approach

Abstract

Currently, establishing a private network on the Internet is highly haz- ardous as attackers continuously scan computers for vulnerabilities within the connected network. The rewall ranked the highest as a network de-vice is selected to protect unauthorized accesses and attacks. However, rewalls can eectively protect against assaults based on adequately de- ned rules without any anomalies. In order to resolve anomaly problems and assist rewall administrators manage the rules eectively, in this pa-per, a prototype of a decision support system has been designed and de-veloped for encouraging administrators to optimize rewall rules and min-imize deciencies that occur in rules by using a probability approach. The experimental results clearly show that the developed model encourages ex-perts and administrators of rewalls to make signicant decisions to resolve rule anomalies. As a result, expert's condence increases by 14.8%, and administrators' condence soars similarly about 44.2%. The accuracy of correcting rule anomalies is 83%.