Traffic-Aware Dynamic Firewall Policy Management:

Techniques and Applications

Presented by Group 2: Shan Gao (3412192)

Dayang Yu (3441202) Jiayu Zhou (3405232)

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Introduction Main Techniques

Matching Optimization Techniques Early Rejection Optimization Techniques

Comparative StudyConclusionReferences (23

slides in total)

Outline

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Firewall: permit or deny network traffic based on a firewall policy

Firewall policy: a list of ordered rules specifies what types of packets should be allowed from/into the protected network

Rule: filtering fields & an action field.

The packet is accepted or denied by a specific rule if the packet header information matches all the network fields of this rule

Firewall policy rule management: 1. To reduce the filtering overhead (FCAPS) 2. Security (FCAPS)

Introduction

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Figure 1 Classification of traffic-aware firewall policy techniques

Main Techniques

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Objectives: try to minimize the matching time of normal network traffic:

Static techniques (not traffic-aware) Algorithmic techniques: 1) hardware-based solutions; 2)

specialized data structures; 3) heuristics improve the search time

Adaptive techniques (traffic-aware) Rule-based optimization: 1) common branch tree; 2)

offline statistical-based rule generation; 3) dynamic rule ordering;

Field-based optimization: 1) multifield alphabetic tree; 2) huffman-tree-based filtering; 3) segment-list-based filtering

Matching Optimization

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1) Common Branch Tree

Number of rules & number of fields build common branch decision trees good average case performance:

Less memory than binary decision trees

Limitation: decision tree needs to be rebuilt every time the traffic pattern changes

Adaptive traffic-aware —— Rule-based optimization

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2) Offline Statistical-Based Rule Generation Traffic-aware firewall optimizer (TFO): Step 1: pre-optimization (removes redundancies) Step 2: a rule-set-based optimizer & a traffic-

based optimizer

The rule-set-based optimizer: Disjoint Set Creator (DSC) & Disjoint Set Merger (DSM) algorithms

The traffic-based optimizer: hot caching, default proxy, total re-ordering, online adaptation

Adaptive traffic-aware —— Rule-based optimization

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3) Dynamic Rule Ordering The optimal rule ordering (ORO) problem is

NP-hard a heuristic approximation algorithm achieves near-optimal results

Compute filtering rule weights based on: matching frequency & matching recency

Limitation: it is not good for policies with a large number of overlapping rules.

Adaptive traffic-aware —— Rule-based optimization

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1) Multifield Alphabetic Tree Calculates the field value frequency build the

alphabetic search tree adaptive packet searching

The alphabetic search tree: improve the overall average filtering

Limitation: the overhead of updating the tree can be significant

Adaptive traffic-aware —— Field-based optimization

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2) Huffman-Tree-Based Filtering The Huffman tree: represent the segmentation of

traffic address space in the firewall policy

Number of rules & number of segments build a Huffman tree enhance the performance of searching & good for policies with a large number of rules

Limitation: the Huffman tree needs to be rebuilt periodically to reflect changes in network flows.

Adaptive traffic-aware —— Field-based optimization

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3) Segment-List-Based Filtering Number of rules & number of segments build a

policy-segment-based search list get rid of the maintenance cost of the Huffman tree

A heuristic algorithm minimize the average search time

Limitation: it has transient behavior until a good order of segments is obtained

Adaptive traffic-aware —— Field-based optimization

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Early Rejection Optimization

Importance• Protect firewalls from DoS attacks that target the

default deny rule.• Minimize the filtering overhead.

ClassificationOnline early rejection• Field value cover-based early rejection• BDD-based relaxed policyBlacklist blocking• Longest common prefix(LCP)-based blacklist

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Online Early Rejection—— Field value cover-based early rejection

Objective Filter out as many discarded packets as possible

with the lowest overhead.Basic idea

The early rejection rules can be formed as a combination of the common field values that cover all rules in the policy.

A typical early rejection rule(RR)

The limitation of the technique is that it is not suitable for large number of rules.

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Online Early Rejection —— BDD-Based Relaxed PolicyBasic idea• Approximate the current policy with another new policy. Provided a packet, the technique evaluates it against the

policy, and reaches one of three options: accepted, rejected or more filtering is needed by the original policy.

• Efficient Boolean expression can be used to represent and approximate the policy Each Boolean expression represents the different packets that match a specific rule, and the variables used for this

expression correspond to the bits of individual packet header fields

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Online Early Rejection —— BDD-Based Relaxed Policy

The limitation of the technique is that the overhead to build the BDD is usually significant.

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Longest Common Prefix (LCP)-Based Blacklist BlockingObjective Minimize the impact of malicious sources in the

network using the available network resources.

Basic idea Addresses with certain prefixes should be blocked.

Data structure LCP tree ----a kind of binary tree.

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Example of LCP tree

The limitation of this technique is that all the malicious IP addresses must be known before the computation of the optimal solution.

Longest Common Prefix (LCP)-Based Blacklist Blocking

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Leaves of the tree

represent the malicious IP

addresses.

All the other nodes

represent the longest

common prefixes between

any pair of IPs in the tree.

Comparative Study

Table 1 . Comparison of the algorithms, data structures, and complexity of traffic-aware dynamic firewall policy techniques.

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Introduction to Related Terms

Skewedness: The measure of asymmetry in the

probability distribution of traffic.

Dynamic: An index to measure whether the

traffic pattern has frequent changes or not.

Comparative Study

Higherskewedness

Traffic “leans” to one side of

the mean

Smaller number of rules required

to match

Higherdynamic

Adapt firewalls more dynamically

Minimize the packets’

matching time

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Table 2. Comparison of limitations and application suitability of traffic–aware dynamic firewall policy techniques.

Comparative Study

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(+ denotes corresponding technique suits this property )(- denotes corresponding technique doesn’t suit this property)(N/A denotes this technique is not applicable for this property)

F C A P SFault managementConfiguration managementAccounting managementPerformance management: The decrease of matching time by

using different techniques can provide high performance packet filtering.

Security management: Border device only passes packets that satisfy rules, block the malicious sources in the network occupy the available network resources.

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Conclusion

References

[1] Duan Qi and Ehab Al-Shaer, “Traffic-aware dynamic firewall policy management: techniques and applications”, Communications Magazine, IEEE, 2013, vol. 51, no. 7.

[2] S. Acharya et al., “Traffic-Aware Firewall Optimization Strategies,” IEEE ICC, 2006.

[3] A. Attar, “Performance Characteristics of BDD-Based Packet Filters”, University of the Witwatersrand, 2001.

[4] H. Hamed and E. Al-Shaer, “Dynamic Rule Ordering Optimization for High-Speed Firewall Filtering,” ASIACCS’06, 2006.

[5] H. Hamed, A. El-Atawy, and E. Al-Shaer, “Adaptive Statistical Optimization Techniques for Firewall Packet Filtering,” IEEE INFOCOM ’06, Apr. 2006.

[6] F. Soldo, A. Markopoulou, and K. J. Argyraki, “Optimal Filtering of Source Address Prefixes: Models and Algorithms,” IEEE INFOCOM ’09, 2009, pp. 2446–54.

[7] M. Tim, Tele9752 Network Operations and Control, Lecture notes, 2013

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THANK YOU !

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