Detecting and Resolving Firewall Policy Anomalies

The advent of emerging computing technologies such as service-oriented architecture and cloud computing has enabled us to perform business services more efficiently and effectively. However, we still suffer from unintended security leakages by unauthorized actions in business services. Firewalls are the most widely deployed security mechanism to ensure the security of private networks in most businesses and institutions. The effectiveness of security protection provided by a firewall mainly depends on the quality of policy configured in the firewall. Unfortunately, designing and managing firewall policies are often error prone due to the complex nature of firewall configurations as well as the lack of systematic analysis mechanisms and tools.

In this paper, we represent an innovative policy anomaly management framework for firewalls, adopting a rule-based segmentation technique to identify policy anomalies and derive effective anomaly resolutions. In particular, we articulate a grid-based representation technique, providing an intuitive cognitive sense about policy anomaly. We also discuss a proof-of-concept implementation of a visualization-based firewall policy analysis tool called Firewall Anomaly Management Environment (FAME). In addition, we demonstrate how efficiently our approach can discover and resolve anomalies in firewall policies through rigorous experiments.

Existing System:

Firewall policy management is a challenging task due to the complexity and interdependency of policy rules. This is further exacerbated by the continuous evolution of network and system environments. The process of configuring a firewall is tedious and error prone. Therefore, effective mechanisms and tools for policy management are crucial to the success of firewalls. Existing policy analysis tools, such as Firewall Policy Advisor and FIREMAN, with the goal of detecting policy anomalies have been introduced. Firewall Policy Advisor only has the capability of detecting pair wise anomalies in firewall rules. FIREMAN can detect anomalies among multiple rules by analyzing the relationships between one rule and the collections of packet spaces derived from all preceding rules.

However, FIREMAN also has limitations in detecting anomalies. For each firewall rule, FIREMAN only examines all preceding rules but ignores all subsequent rules when performing anomaly analysis. In addition, each analysis result from FIREMAN can only show that there is a misconfiguration between one rule and its preceding rules, but cannot accurately indicate all rules involved in an anomaly.

Proposed System:

In this paper, we represent a novel anomaly management framework for firewalls based on a rule-based segmentation technique to facilitate not only more accurate anomaly detection but also effective anomaly resolution. Based on this technique, a network packet space defined by a firewall policy can be divided into a set of disjoint packet space segments. Each segment associated with a unique set of firewall rules accurately indicates an overlap relation (either conflicting or redundant) among those rules. We also introduce a flexible conflict resolution method to enable a fine-grained conflict resolution with the help of several effective resolution strategies with respect to the risk assessment of protected networks and the intention of policy definition.

Modules:

  • Correlation of Packet Space Segment
  • Action Constraint Generation
  • Rule Reordering
  • Data Package

Tools Used:

Front End : Java