Systematic Detection And Resolution Of Firewall Policy Anomalies

Systematic Detection And Resolution Of Firewall Policy Anomalies 1.M.Madhuri 2.Knvssk Rajesh Dept.of CSE, Kakinada institute of Engineering & Tech., Korangi, kakinada, E.g.dt, AP, India. Abstract: In this paper the problem of discovering the set of troublesome rules in a large firewall policy is investigated and consequently eliminating or resolving them. all the rules in the policy are consistent and can be reordered to make them effectively and optimally functional In the existing approach they can only detect the firewall policy and conflict detection time was also increases. Based on these risk value conflict rule can be effectively resolve. Keywords: Anomaly, FIREMAN, Firewall, Firewall policy, Segmentation. 1. INTRODUCTION: Network security is essential to the development of internet and has attracted much attention in research and industrial communities. With the increase of network attack threats, firewalls are considered effective network barriers and have become important elements not only in enterprise networks but also in small-size and home networks. A firewall is a program or a hardware device to protect a network or a computer system by filtering out unwanted network traffic. The filtering decision is based on a set of ordered filtering rules written based on predefined security policy requirements. Firewalls can be deployed to secure one network from another. It is very crucial to have policy management techniques and tools that users can use to examine, refine and verify the correctness of written firewall filtering rules in order to increase the effectiveness of firewall security. It is true that humans are well adapted to capture data essences and patterns when presented in a way that is visually appealing. The amount of data that can be processed and analyzed has never been greater, and continues to grow rapidly. As the number of filtering rules increases largely and the policy becomes much more complex, firewall policy visualization is an indispensable solution to policy management. Firewall policy visualization helps users understand their policies easily and grasp complicated rule patterns and behaviors efficiently. 2. EXISTING SYSTEM: Firewall is the de facto core technology of today s network security and defense. However, the management of firewall rules has been allowto be complex, error-prone, costly and inefficient for many large-networked organizations. These firewall rules are mostly custom-designed and hand-written in this constant need for tuning and validation, due to the dynamic nature of the traffic characteristics, everchanging network environment and its market demands. Firewalls are the most widely deployed security mechanism to ensure the security of private networks in most businesses and institutions. 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. Therefore, effective mechanisms and tools for policy management are crucial to the success of firewalls. Existing policy analysis tools, such as Firewall Policy Advisor[4] and FIREMAN[3], with the goal of detecting policy anomalies have been introduced. Firewall Policy Advisor only has the capability of detecting pairwise 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[3]. www.ijrcct.org Page 1387

DISADVANTAGES OF EXISTING SYSTEM: a) 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. b) For each firewall rule, FIREMAN only examines all preceding rules but ignores all subsequent rules when performing anomaly analysis. 3. PROPOSED SYSTEM: They can only detect the firewall policy anomaly and can not resolve these policy anomaly, and also policy conflict detection time was also increased. 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. Policy-Anomaly- Discovery Algorithm that takes a policy and utilizes the dependency data structure to find and eliminate anomalies returning a list of validated policy. algorithm has time complexity O(n2 log n), Efficient in detection of anamoloies. 92 percent of conflicts can be resolved. The proposed system resolves conflicts in each conflict correlation group independently. ADVANTAGES OF PROPOSED SYSTEM: a) In our framework conflict detection and resolution, conflicting segments are identified in the first step. b) Each conflicting segment associates with a policy conflict and a set of conflicting rules. c) Also, the correlation relationships among conflicting segments are identified and conflict correlation groups are derived. d) Policy conflicts belonging to different conflict correlation groups can be resolved separately, thus the searching space for resolving conflicts is reduced by the correlation process. 4. FIREWALL POLICIES AND ANOMALIES : A firewall policy rule is defined as a set of criteria and an action to perform when a packet matches the criteria. The criteria of a rule consist of the elements direction, protocol, source IP, source port, destination IP and destination port. Therefore a complete rule may be defined by the ordered tuple <direction, protocol, source IP, source port, destination IP destination port, action>. Each attribute can be defined as a range of values, which can be represented and analyzed as sets. Firewall policy anomaly is defined as the existence of two or more filtering rules that may match the same packet. Till date, five types of anomalies are discovered,shadowing Anomalies, Correlation Anomalies, Generalization Anomalies, Redundancy Anomalies, and Irrelevance Anomalies. 4.1 Shadowing anomaly: Two rules are said to have shadowing anomaly,whenever the rule which comes first in rule set matches all the packets and the second rule which is positioned after the first rule in rule set does not get chance to match any packet because the previous rule has matched all the packets. It is a very critical problem since the rule coming later to the previous rule will never get activated. Hence the traffic to be blocked will be allowed or the traffic to be permitted can be blocked. 4.2 Correlation anomaly: Two rules are said to have correlation anomaly if both of them matches some common packets that is the rule one matches some packets, which are also matched by the rule second. The problem here is that the action performed by both the rules is different. Hence in order to get the proper action such correlated rules must be detected and should be specified with proper action to be performed. 4.3 Generalization anomaly: Two rules which are in order one of them is said to be in generalization of another if the first rules matches all the packets which can be also matched by the second rule but the action performed is different in both the rules. In this case if the order is reversed then the corresponding action will also be changed. The rule, which comes later in the rule list, is shadowed by the previous rule and also it has no effect on incoming packets. The super set rule is called General rule and the subset rule is called Specific rule. 4.4 Redundancy anomaly: Two rules are said to be redundant if both of them matches some packets and the action performed is also the same. So there is no effect on the firewall policy if one of redundant rules will be removed from the rule set. It is very necessary to search and remove the redundant rules from the rule set because they increase the search time, space required to store the rule set and thus decrease the efficiency of the firewall. The firewall www.ijrcct.org Page 1388

administrator should detect and remove such redundant rules to increase the performance of the firewall. 4.5 Irrelevance anomaly Any rule is said to be irrelevant if for a given time interval it does not matches any of the packets either incoming or outgoing. Thus if any type of the packets do not match a rule then it is irrelevant i.e. there is no need to put that rule in the rule set. 5 POLICY ANOMALY DISCOVERY: In order to precisely identify policy anomalies we adopts a rule-based segmentation technique[1]. 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 among those rules. To enable an effective anomaly resolution, complete and accurate anomaly diagnosis information should be represented in an intuitive way. Algorithm 1[1] given below is the segment generation for a network packet space of a set of rules R 5.1 ANOMALY MANAGEMENT FRAMEWORK: The overall flow of our proposed anomaly management is depicted in fig 2 and 3. Fig.1 Administrator aspect in proposed system. www.ijrcct.org Page 1389

Fig. 2 End user aspect in proposed system Proposed system divides the task of detecting and resolving the conflict firewall policy and firewall log analysis into framework, which are enumerated as follows: 1. Rule Generation: The administrator generates a rule by giving rule name and various fields.here we calculate the threshold value. Depending upon the threshold value, the action may be allow or deny. 2. Conflicted Rule Updating There are various types of firewall policy anomalies. If there is any conflicted rule occurred in that means it will automatically updated. The conflicts can be resolved by conflict resolution mechanism depending upon the value occurred in the risk assessment. It is shown in fig 3. Once we identify the conflicts in a firewall policy, the task of risk assessment for conflicts is performed on firewall policy. When the value of risk assessment is maximum, then the imagined action should deny or block the data packets against the consideration for the security of network perimeters. In contrast when the value of risk assessment is minimum, then the imagined action be supposed to permits the data to flow through the firewall. 3. File Transformation: The file which should be going to transfer is chosen. Afterwards, the file is first encrypted and sends to the rule engine. During the transformation the encrypted file only selected to broadcast the data. The file should be encrypted with regard to one of the firewall policy, and then it is selected for the transferring process. 4. Rule Engine: Conflict resolution strategy obtains the most ideal solution only when all the action constraints for each conflicting segments is fulfilled by reordering the anomaly rules. In conflict resolution, Reordering of conflict occurred rules which meet the expectations of all action constraints then this sort be the best resolution. 5. Firewall Log Analysis: It would generate a set of primitive rules with repeated and rare outcomes. This is used to add more security in frequent log. Design of firewall log analysis is shown in fig 3. Fig.3 Firewall log analysis design 5.3 Experimental Results: This anomaly management framework provides a user friendly tool for purifying and protecting the firewall policy from anomalies. The administrator can use this framework for firewall policy generation and it was able to detect and resolve anomalies in rules written by expert network administrators. The end user can transfer file based on the risk value using the firewall rules.this framework can perform firewall log analysis that can be used to add more security in frequent log. Our proposing framework resolves the policy conflicts for firewall in short duration of time and proves to be useful for the deployment in firewall technology. We evaluate the conflict resolution rate of our strategy-based approach, which is reflected by the number of resolved conflicts (i.e., satisfied action constraints). We compared the results of applying our strategy-based approach with the results of directly applying the existing first-match mechanism for conflict resolution. As shown in Fig. 6, we could observe that directly applying the existing first-match mechanism can only solve an average 63 percent of conflicts. Moreover, for some small-scale policies, we noticed that FAME was capable of resolving all policy conflicts. 2.5 1.5 2 0.5 1 0 Performance Usage Fig 4. Network Firewall Perfomance www.ijrcct.org Page 1390

6 5 4 3 2 1 0 1 2 3 4 FAME Fig. 5. Evaluation of redundancy removal. Traditional From Fig. 5, we observed that FAME could identify an average of 6.5 percent redundant rules from the whole rules. However, traditional redundancy analysis approach could only detect an average 3.8 percent of total rules as redundant rules. Therefore, the enhancement for redundancy elimination was clearly observed by our redundancy analysis approach compared to traditional redundancy analysis approach in our experiments. 5 CONCLUSION: A novel anomaly management framework that facilitates systematic detection and resolution of firewall policy anomalies with low time complexity. Thus, just having a firewall on the boundary of a network may not necessarily make the network any secure. One reason for this is the complexity of managing firewall rules and the potential network vulnerability due to rule conflicts. Our proposing anomaly management framework facilitates systematic detection and resolution of firewall policy anomalies and firewall log analysis. This Future its extend our anomaly analysis approach to handle distributed firewalls. 6. Future work: It was includes extending our anomaly analysis approach to handle distributed firewalls. 7. REFERENCES: 1 E. Al-Shaer and H. Hamed, Discovery of Policy Anomalies in Distributed Firewalls, IEEE INFOCOM 04, vol. 4, pp. 2605-2616, 2004. 2 A. Wool, Trends in Firewall Configuration Errors: Measuring the Holes in Swiss Cheese, IEEE Internet Computing, vol. 14, no. 4, pp. 58-65, July/Aug. 2010. 3 J. Alfaro, N. Boulahia-Cuppens, and F. Cuppens, Complete Analysis of Configuration Rules to Guarantee Reliable Network Security Policies, Int l J. Information Security, vol. 7, no. 2, pp. 103-122, 2008. 4 F. Baboescu and G. Varghese, Fast and Scalable Conflict Detection for Packet Classifiers, Computer Networks, vol. 42, no. 6, pp. 717-735, 2003. 5 L. Yuan, H. Chen, J. Mai, C. Chuah, Z. Su, P. Mohapatra, and C. Davis, Fireman: A Toolkit for Firewall Modeling and Analysis, Proc. IEEE Symp. Security and Privacy, p. 15, 2006. 6 E. Lupu and M. Sloman, Conflicts in Policy- Based Distributed Systems Management, IEEE Trans. Software Eng., vol. 25, no. 6, pp. 852-869, Nov./Dec. 1999. 7 I. Herman, G. Melanc on, and M. Marshall, Graph Visualization and Navigation in Information Visualization: A Survey, IEEE Trans. Visualization and Computer Graphics, vol. 6, no. 1, pp. 24-43, Jan.-Mar. 2000. 8 H. Hu, G. Ahn, and K. Kulkarni, Anomaly Discovery and Resolution in Web Access Control Policies, Proc. 16th ACM Symp. Access Control Models and Technologies, pp. 165-174, 2011. 9 L. Yuan, C. Chuah, and P. Mohapatra, ProgME: Towards Programmable Network Measurement, ACM SIGCOMM Computer Comm. Rev., vol. 37, no. 4, p. 108, 2007. 10 A. El-Atawy, K. Ibrahim, H. Hamed, and E. Al-Shaer, Policy Segmentation for Intelligent Firewall Testing, Proc. First Workshop Secure Network Protocols (NPSec 05), 2005. 11 G. Misherghi, L. Yuan, Z. Su, C.-N. Chuah, and H. Chen, A General Framework for Benchmarking Firewall Optimization Techniques, IEEE Trans. Network and Service Management, vol. 5, no. 4, pp. 227-238, Dec. 2008. 12 M. Frigault, L. Wang, A. Singhal, and S. Jajodia, Measuring Network Security Using Dynamic Bayesian Network, Proc. Fourth ACM Workshop Quality of Protection, 2008. www.ijrcct.org Page 1391

Mrs.Madhuri mandavilli is a student of Kakinada institute of Engineering & Technology, korangi. Presently she is pursuing her M.Tech [Computer Science Engineering] from this college and she received her B-tech from kiet college, affiliated to JNTUK University, Kakinada in the year 2009. Her area of interest includes Computer Networks and Object oriented Programming languages, all current trends and techniques in Computer Science Mr.KNVSSK Rajesh, well known and excellent teacher received M.Tech (CSE) from JNTUK, Kakinada. He has 4 years of teaching experience in Engineering College and 1year of experience as corporate trainer. Currently working as Asst. Professor in KIET. His Area of interest includes data mining, information security and embedded systems. www.ijrcct.org Page 1392