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The transport layer security TLS protocol is widely adopted by apps as well as malware.
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- Flexible neural trees based early stage identification for IP traffic
- Flexible neural trees based early stage identification for IP traffic
- Packet Classifiers In Ternary CAMs Can Be Smaller ... Packe - PowerPoint PPT Presentation
Identifying network traffics at their early stages accurately is very important for network management and security. Recent years, more and more studies have devoted to find effective machine learning models to identify traffics with few packets at the early stage. In this paper, we try to build an effective early stage traffic identification model by applying flexible neural trees FNT. Three network traffic data sets including two open data sets are used for the study. We first extract both packet-level features and statistical features from the first six continuous packets and six noncontinuous packets of each flow.
With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes.
Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering.
Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm.
Citation: Bi X-a, Zhao J Hierarchical trie packet classification algorithm based on expectation-maximization clustering. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The experimental configuration can be referred as "Full technical report". Other relevant data are within the paper and its Supporting Information files. D Hunan Normal University No. Competing interests: The authors have declared that no competing interests exist. The core equipment of computer network is the router and firewall. Packet classification technology is the key technology of these core devices, which restricts the development of computer network bandwidth.
Thus, packet classification technology has great significance on the next-generation Internet network equipment[ 1 ], and plays important roles in routing, quality of service, firewall, multimedia communications, accounting, traffic monitoring, and so on[ 2 ].
With the rapid development of high-speed network, packet classification technology has become one of the main factors that affect the improvement of network equipment[ 3 ]. Meanwhile, packet classification algorithms are required to deal with larger number of rule sets. Researches on efficient packet classification algorithms which support large-scale rule sets are of great significance[ 4 ]. The main entities of packet classification are packets and rules.
Rules are defined as multiple fields of packet headers and actions. Fields are usually divided into five parts: source IP address prefixes, destination IP address prefixes, source port, destination port and protocols[ 5 ]. The role of packet classification is to distinguish the numerous data packets to different types based on rules and then deal with different types of packets with distinguishing actions, such as routing forward, packet filtering.
Although packet classification technology exists in computer network equipment, it is an independent technology that needs to be studied. An effective packet classification technology needs to get rid of the shackles of network services and could be deployed in various devices. Packet classification technology develops rapidly and diverse flows of packet classification algorithms have been proposed in the past decades. Nevertheless, most literature mainly focuses on the performance improvement of the packet classification algorithm, and neglects the theoretical analysis and the problems which occur in the implementation process[ 6 — 7 ].
Therefore, the performance evaluation of packet classification algorithms include several metrics, among which the processing speed and memory storage are the most fundamental and commonly-used.
Incremental scalability and update performance of the algorithms have turned into another two important metrics, and become growing concerns in the existing literature[ 8 — 9 ]. Existing packet classification algorithms are divided into three flows: basic data structure algorithms[ 10 — 14 ], space mapping algorithms[ 15 — 19 ] and hardware-based algorithms[ 20 — 22 ].
Basic data structure algorithms and space mapping algorithms are featured with complex data structures, and easy to implement and deploy, but these two types of algorithms face the bottleneck of performance due to the complex data structures. Hardware-based algorithms usually use hardware such as TCAMs. This type of algorithms has high searching speed performance. However, these hardwares are expensive and do not support flexible scalability.
Moreover, this type of algorithms are only suitable for small-scale rule sets because of the high energy consumption, which hinders their widespread use. Therefore, a new solution is required to achieve high scalability and update performance as well as high classification performance.
To fill out the research gap, this paper uses cluster analysis theory to construct Hierarchical Trie to solve the matching problems between packets and rules. Secondly, this paper uses Expectation-Maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed.
Finally, this paper respectively conducts simulation experiments and real-environment experiments to test the performances of the proposed algorithm, and analyzes the results of the experiments. By combining expectation maximization algorithm and hierarchical tries, this paper makes the following contributions.
In theory, we propose the formalization of the packet classification problem based on geometric space. This method uses mathematical models to map data packets and rules into the rectangular area in two-dimensional space. Then we use the theoretical analysis to prove the mathematical model established by this method, and conclude that the packets and rules still keep the original features and the mapping rectangular area still meets the packet matching process.
In terms of algorithm, this paper design a novel hierarchical trie structure which not only adopts trie path compression to eliminate backtracking, but also solves the problem of update performance, and thereby the performance of the algorithm has been greatly improved.
In practice, we deploy our algorithm in the network traffic monitoring system to test the performances of the algorithms and further improve our algorithm. The experimental results show that the proposed packet classification algorithm has high-speed packet classification performance, and low storage requirement.
At the same time, it can be easily implemented and deployed. The rest of this paper is organized as follows. Section 2 reviews the related works.
In Section 3, the formalization of packet classification is presented in details. Section 5 discusses the experimental evaluation, and Section 6 gives the conclusions. In this section, we provide a brief discussion on the packet classification algorithms. General packet classification algorithm are roughly divided into basic data structure algorithms, space mapping algorithms and hardware-based algorithms. The survey of the packet classification algorithms is shown in Table 1. Existing basic data structure packet classification algorithms are mainly divided into trie-based packet classification algorithms, tuple space-based packet classification algorithms and Bloom Filter- based packet classification algorithms.
Basic data structure packet classification algorithms have better scalability, thereinto trie-based packet classification algorithms are widely used[ 23 ]. However, trie-based packet classification algorithms need to search for all possible matching rules by backtracking. When this type of algorithms are applied to IPv6, the performance significantly reduces. Therefore, we need to develop a data structure-based packet classification algorithm that supports fast-speed classification as well as large-size rule sets.
Most space mapping packet classification algorithms fall into three main categories: geometric area-based packet classification algorithms, dimension decomposition-based packet classification algorithms and clustering-based packet classification algorithms. The representative algorithms are Hierarchical Intelligent Cuttings[ 15 ], HyperCuts[ 16 ], Recursive Flow Classification[ 17 ], GroupCuts[ 18 ], unsupervised co-clustering algorithm[ 19 ] and so on.
Space mapping packet classification algorithms take up less searching time but require large memory storage. This type of algorithms could not satisfy the requirements of high searching speed brought by Gigabit challenge[ 24 ]. Clustering-based packet classification algorithms can solve the problem of backtracking, which exists in hierarchical trie packet classification algorithm. However, clustering-based packet classification algorithms also have several demerits such as low update performance of rules.
TCAM-based packet classification algorithms, which are featured with parallel searches and matching result reports in a single cycle, are the preferred choice by the industry up till now. Because of the parallel operation, the high speed advantage always comes at a price like huge energy consumption[ 20 ]. FPGA-based packet classification algorithms are featured with reconfigurability.
Although this kind of customized architecture provides high performance, it is not easy to implement and deploy [ 22 ]. In the field of high performance computing, general-purpose computing with GPU has become a new research trend. Such algorithms are featured with several types of memory storage and usage in various functions on the GPU[ 21 ]. However, how to effectively enhance the ability of parallelism is still a great challenge. In conclusion, existing algorithms usually stand out in a certain aspect of performance, but little literature proposes the packet classification algorithms which are easy to implement and deploy and are featured with high speed performance, low storage requirements, flexible scalability and high update performance.
Therefore, this paper propose a novel algorithm to solve the problem. This paper formulates the packet classification problem as a mapping problem. It is assumed that the number of two-dimensional rules in a rule set R is n. Then this rule have been mapped to a small rectangular area in the two-dimensional space. Let us make the center point of this rectangle represent the rule, and thereby the rule Rm can be written as a point:.
And we can obtain: 1 2 3 4 where w R is the prefix length of R m , VR i is the value of i-th bit in the prefix of R m VR i is either 0 or 1 , k is any positive integer. Then this packet have been mapped to a smaller rectangular area in the two-dimensional space compared with the rule Rm. Let us make the center point of this rectangle represent the packet, and thereby the packet P can be written as a point:.
And we can obtain: 5 6 7 8 where w P is the address length of packet P i. Packet matching process is a matching process between packets and the rules in the rule set. Specifically, the aim of packet matching process is to find the matching rules in accordance with one or more packet header fields, and then perform the appropriate actions.
In this paper, we use the prefix matching which is the most widely-used and important among all the matching types. Lemma 1. If a packet P matches with the rule R m , then and. Because 9. And Similarly, we can get. Then the conclusion can be obtained.
The conclusion could be proved in the same way. This section proposed a hierarchical trie algorithm for packet classification based on expectation-maximization clustering. The algorithm has two stages, one is the preprocessing stage of rules and packets, one is the packet matching stage. In the first stage, we firstly adopt the formalization method of packet classification problem to map the rules and packets into rectangular area in the two-dimensional space.
Then we use expectation-maximization algorithm to cluster the formalized rules and thus a plurality of clusters could be formed. In the second stage, we construct a hierarchical trie based on the existing clusters and complete the packet matching process. The hierarchical trie structure in this algorithm adopts the path compression to eliminate backtracking and overcomes the difficulty of trie update, which greatly improves the performance of the proposed algorithm.
Flexible neural trees based early stage identification for IP traffic
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matched filter ppt 3 Matched Filters The double frequency terms in the above equation Signal Processing: Estimation Theory, Prentice Hall, ppt), PDF File . 3 Structure of the proposed classifier Our main idea is thus to transform every If both IP addresses match, the packet is considered secure and verified.
Flexible neural trees based early stage identification for IP traffic
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Packet Classifiers In Ternary CAMs Can Be Smaller ... Packe - PowerPoint PPT Presentation
Identifying network traffics at their early stages accurately is very important for network management and security. Recent years, more and more studies have devoted to find effective machine learning models to identify traffics with few packets at the early stage. In this paper, we try to build an effective early stage traffic identification model by applying flexible neural trees FNT. Three network traffic data sets including two open data sets are used for the study. We first extract both packet-level features and statistical features from the first six continuous packets and six noncontinuous packets of each flow. Packet sizes are applied as packet-level features. And for statistical features, average, standard deviation, maximum and minimum are selected.
PDF | An increasing number of Internet applications and services render network management more troublesome for bandwidth misuse and has higher average accuracy and performance than L7-ﬁlter. trafﬁc classiﬁcation module gets a packet, four policies are have improved the presentation.
This documentation section is targeted at developers and users who want to understand BPF and XDP in great technical depth. While reading this reference guide may help broaden your understanding of Cilium, it is not a requirement to use Cilium. BPF is a highly flexible and efficient virtual machine-like construct in the Linux kernel allowing to execute bytecode at various hook points in a safe manner. It is used in a number of Linux kernel subsystems, most prominently networking, tracing and security e. Even though the name Berkeley Packet Filter hints at a packet filtering specific purpose, the instruction set is generic and flexible enough these days that there are many use cases for BPF apart from networking.
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