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Multi-pattern Signature Matching for Hardware Network Intrusion Detection Systems Abstract

Multi-pattern Signature Matching for Hardware Network Intrusion Detection Systems Abstract
Multi-pattern Signature Matching for Hardware Network Intrusion Detection Systems Abstract

Multi-pattern Signature Matching for Hardware

Network Intrusion Detection Systems

Haoyu Song,John W.Lockwood {hs1,lockwood }@https://www.doczj.com/doc/9315698530.html,

Department of Computer Science and Engineering Washington University in St.Louis,USA,63130

Abstract —Network Intrusion Detection System (NIDS)per-forms deep inspections on the packet payload to identify,deter and contain the malicious attacks over the Internet.It needs to perform exact matching on multi-pattern signatures in real time.In this paper we introduce an ef?cient data structure called Extended Bloom Filter (EBF)and the corresponding algorithm to perform the multi-pattern signature matching.We also present a technique to support long signature matching so that we need only to maintain a limited number of supported signature lengths for the EBFs.We show that at reasonable hardware cost we can achieve very fast and almost time-deterministic exact matching for thousands of signatures.The architecture takes the advantages of embedded multi-port memories in FPGAs and can be used to build a full-featured hardware-based NIDS.

I.I NTRODUCTION

Some content strings of Internet packet payload,also known as “signatures,”imply network intrusion attempts.Signature-based Network Intrusion Detection System (NIDS)collects these signatures and scans the payload of the Internet packets for them in order to identify,deter and contain such malicious behaviors.A scalable and fast solution is needed to accom-modate the largest signature set today and to sustain the real time processing of the high-speed network.

Bloom Filter [4]is an ef?cient data structure enabling fast membership query with tunable false positive rate.Dharma-purikar et al have designed a multi-pattern signature-matching scheme using Bloom Filters [6].On the scan process,when-ever the front-end Bloom Filter reports a possible match,the string is extracted and used to probe another independent hash table to decide the ?nal match.There are two drawbacks in this scheme.Firstly,the extra lookups in the hash table might become the performance bottleneck due to the hash collisions.Secondly,there are many different signature lengths and the signature distribution on length is unbalanced,so to assign each length a Bloom Filter is inef?cient in memory usage.We ?nd that the scheme does not effectively use the information revealed by the Bloom Filters and there is little consideration about the string load balancing among different Bloom Filters.To overcome these drawbacks,we propose an extension of the Bloom Filter data structure and a new lookup algorithm named Extended Bloom Filter (EBF).It is scalable and suitable for fast incremental updates.The hardware-based EBF is an alternative of the multi-pattern signature-matching problem and outperforms the software-based algorithms.

In this paper,we review the related work in Section II and then discuss our data structure and algorithms in Section III.A theoretical analysis and simulations follow in Section IV and V.Some improvements are presented in Section VI to further reduce the memory usage and boost the performance.The scheme to reduce the number of EBFs is introduced in VII.We brie?y talk about the hardware NIDS implementation in Section VIII and conclude our contribution in Section IX.

II.R ELATED W ORK

Given a packet payload T of length n and a set of m signa-tures S [1]...S [m ]of variable length for intrusion detection,the signature-matching problem is to determine any exact match of signature S [i ]and a substring of T .In NIDS,signature matching is a crucial component and decides the overall system performance.An analysis shows that in Snort,an open-source software-based NIDS,the signature matching alone consumes 30%to 80%of the CPU time [9].While the network bandwidth and the size of the signature set keep growing,to perform real time detection is still far from realistic.

Boyer-Moore is the best-known algorithm for single string matching and is actually adopted for the implementation of the Snort.Fisk extended the Boyer-Moore algorithm to support set-wise string matching [8].Coit does similar work in [5].Aho-Corasick [2]is a ?nite state automaton supporting multi-pattern string matching.The major drawback is its excessive memory consuming.A modi?ed algorithm of Aho-Corasick due to Tuck [14]reduces the amount of memory and improves its performance.Wu-Manber [15]uses a hash table plus the bad character heuristics to accelerate the searching speed.All these algorithms are developed mainly for software imple-mentation.Analysis and experiments show no such algorithm is fast enough for real-time string matching in high-speed network.Thus,a hardware-assisted or pure hardware solution is becoming more and more attractive.

Sidhu [12]implemented Nondeterministic Finite Automaton (NFA)in hardware and later Moscola [10]implemented De-terministic Finite Automaton (DFA)in hardware to perform regular expression matching.While the match speed is fast,they both suffer the scalability problem:Too many states consume too many hardware resources.Dharmapurikar then proposed to use Bloom Filters to do the deep packet inspection [6].Attig implemented a prototype of this scheme [3].Our paper proposes signi?cant improvement to this work and

0-7803-9415-1/05/$20.00 (C) 2005 IEEE

Multi-pattern Signature Matching for Hardware Network Intrusion Detection Systems, by Haoyu Song and John W. Lockwood, IEEE Globecom 2005, St. Louis, MO, Nov. 28, 2005, pp. CN-02-3.

addresses more problems in a real hardware based NIDS im-plementation.Recently,Yu proposed a TCAM-based scheme for multi-pattern matching[16].Despite the widely criticized inef?ciency of TCAM device,the long string matching support involves multiple tables.

III.EBF D ATA S TRUCTURE AND A

LGORITHM

Fig.1.EBF Data Structure

EBF eliminates the need of another hash table for match veri?cation.Moreover,the information gained from the Bloom Filter is exploited to accelerate the lookups.As shown in Figure1,each EBF bucket has three?elds.The?rst?eld (Hit Bit)is one bit and has the same de?nition as in the original Bloom Filter.The second?eld is a counter that counts how many signatures address the bucket.Unlike the Counting Bloom Filter(CBF)[7],in which the counters are used for update purpose only,EBF uses the counters to accelerate the signature matching.The last?eld is a pointer.If some signature sets the Hit Bit,this pointer will eventually lead to it.All signatures hashed to a bucket are organized in a link list.In order to save memory,only one copy of each signature is stored and pointed by a pointer from the link list nodes.The algorithm can be described as two steps:EBF programming and membership query.Assuming the EBF contains m buckets and r hash functions:H1...H r,the algorithm to programming a signature S is described in Figure

2.

Fig.2.Algorithm for EBF Programming

Figure3shows an example of EBF for which k=3and m=13.Four signatures have been programmed into the EBF in sequence of S1,S2,S3,and S4.

Once the EBF is programmed,it is ready for membership query.As described in Figure4,the algorithm retains the basic feature of Bloom Filter by?ltering out the impossible match quickly.Moreover,in case all Hit Bits are set for a string,

Fig.3.Programming the Extended Bloom Filter

Fig.4.Algorithm for Signature Matching in EBF

the counter mechanism guides us to only search a shortest list to verify the membership.The algorithm is targeted at hardware implementation so we can fully exploit the hardware parallelism.All r hash functions can be calculated in parallel and the multiple EBF buckets can be probed simultaneously. It is straightforward to perform the signature update.To insert a signature,we call the procedure Programming EBF. To delete a signature,we use the similar method described in the CBF scheme with additional work to remove the corre-sponding list node and recycle the memories.In all operations, memory for signatures and list nodes are dynamically allocated and de-allocated.

IV.A NALYSIS

Firstly,we analyze the average length of the link list in any EBF bucket.Assuming there are n signatures,for a bucket, the probability f set that the Hit Bit is set by one signature is:

f set=1?(1?

1

m

)r(1) The probability f i set that a bucket’s Hit Bit is set by i signatures is:

f i set=

n

i

[1?(1?

1

m

)r]i(1?

1

m

)r(n?i)(2) So the average length of link list l avg is

l avg=E[i]=Σn i=0i×f i set=n(1?(1?

1

m

)r)(3)

For the Bloom Filter,when the false positive probability is minimized with respect to r,we get the relation

r=(m/n)ln2(4) Based on these analysis,the approximation of link list length as:

l avg=n(1?(1?1

m

)(m/n)ln2)≈ln2≈0.7(5)

Under the same conditions,the probability for a bucket to be

empty is roughly0.5.This implies that half of the total buckets

are never hit by any member https://www.doczj.com/doc/9315698530.html,ing same parameters

as in[3]’s Bloom Filter implementation,the false positive rate

is as low as0.00097and the average link list length is only 0.4.This result shows that the link list is typically very short. Theoretically,if the r buckets are randomly selected from

all the buckets with non-zero counter value,we would like to

know the expected shortest link list.Assuming the percentage

of buckets with a counter value of i over all the buckets with

non-zero counter value is p i.P(j)is the probability that the

smallest counter value is j among the r randomly selected

buckets.Therefore,we get the equation:

P(1)=1?P(>1)=1?(1?p1)r(6) and when j>1

P(j)=1?P(j)

=(1?Σj?1

k=1

p k)r?(1?Σj k=1p k)r(7) The expected value of j is

E[j]=Σ∞j=1j×P(j)=1+Σ∞s=1(1?Σs t=1p t)r(8) We know that in the optimal case,half of the bucket have a zero value counter,so we have the relation:

p i=2×f i set(9) Using the optimal EBF parameters,P(1)is greater than 0.99and E[j]is slightly larger than one.This shows in most cases actually,only the link lists with length one need to be checked if none of the r hashed counters is zero.Note that the analysis is not very strict and only re?ect the approximate results.We use simulations as backup in the following section.

V.S IMULATION

Simulation results support our theoretical analysis.We use

the same parameter con?gurations as in[3]:1419signatures

are programmed by10hash functions into a20K-bucket EBF.

In the worst and extremely rare cases,at most two memory

accesses are needed.In case that the signatures are all2-byte

long,we test all the65535possible strings,only seven of

them need two memory accesses and others need only one,

even though the link list lengths range from two to?ve.The

longer signature cases exhibit even better performance.

We also did some experiments based on the different com-

binations of the number of hash functions and EBF buckets

Fig.5.Simulation I

as shown in Figure5.We ran a million queries that cover all the programmed signatures.The“Max Length”curve indicates the length of the longest link list in EBF.As more signatures are programmed into EBF,the maximum length of link list grows.Fortunately,we do not need to traverse the longest list.The“Max Hit Length”curve shows the longest list we need to traverse in order to?nd the

matching in any query. By decreasing the number of hash functions or increasing the number of buckets,we can effectively lower this number. However,using less hash functions may increase the false positive rate and using more EBF buckets will increase the resource consumption.Finally,the“Avg Hit Length”curve shows the average length of the list we traverse during the simulation.This number is typically less than two.For the Snort signatures,we have shown for any speci?c length, there are about or much fewer than100distinct signatures. In this case,our experiments indicates only one list node check is needed.By?ne-tuning the parameters,we can support more than1000signatures with tolerable number of memory accesses.

To test the effect on Snort rule set,for each signature length, we only program200distinct signatures in EBF.This leaves spacious room for future update.By varying the number of EBF buckets and the number of hash functions,we see how the number of false positive match and average number of link list accesses change as shown in Figure6.

Fig.6.Simulation II

VI.EBF O PTIMIZATIONS

The EBF is stored in on-chip multi-port SRAM that enables parallel access in one clock cycle.Beside the Hit Bit,we budget4bits for the counter that supports link list length up to 15.We leave11bits to the pointer?eld that distinguish up to 2K signatures.Thus,each bucket is only2-byte.With a40K-byte on-chip SRAM,we could support2K any length signature matching.Now we introduce several optimizations to further improve the storage ef?ciency and lookup performance. Max-Min Threshold:After all signatures are programmed, the minimum link list length for each signature S i is min(S i).

A value

B is maintained along with EBF,where B= max{min(S i)...,?S i}.The usage of B is straightforward: whenever the Bloom Filter reports a match but the queried minimum counter value is greater than B,we know for sure this is a false positive so no link list access is needed.This heuristics comes form the observation that given the small false positive rate,min(S i)tends to be small.When20K buckets,10hash functions and2000signatures are used, simulation shows B=1and for1million test inputs there are 114false positive matches with shortest link list longer than 1.This simple improvement eliminates most of the expensive and unnecessary memory accesses.

Compressed Counter:In previous description,we separate the“Hit bit”and Counter in EBF.Actually,a non-zero counter implies some signatures hit the bucket so the“Hit bit”is redundant.A more aggressive counter scheme is used in EBF implementation.Each on-chip EBF bucket only contains k bits for a saturated counter,where k= log(B+1) +1. The pointer?eld is maintained in a table located in off-chip memories.In practical EBF con?gurations where B is often less than two,two bits are enough to represent the counter and differentiate all useful scenarios,where saturated value“11”means the bucket is hit by three or more signatures.With this optimization,same design mentioned at the beginning of this section can be achieved by using only40K-bit on-chip memories.Note that this saturated counter scheme loses the ability to track the real number of signatures in a list. Shared Link List Nodes:In EBF,up to r link list nodes are allocated for one signature since r hash functions are used. Assuming each link list node is4-byte and each signature is 32-byte,to support2000signatures we need roughly144K-byte memory.Considering in Snort rule set there are almost 50distinct signature lengths,we have the scalability issue to support multiple EBFs.By slightly modifying our algorithm, we can greatly eliminate the data structure redundancy.Ideally, if we always perform a deterministic lookup to the off-chip memory,we need only store a single list node for a signature. However,we want to support the fast incremental updates, and the simple and uni?ed control operations to EBF,so we introduce a scheme that is not optimal but still signi?cant in memory reducing.

When we program a signature in EBF,we need update r EBF buckets.We always insert the signature at the head of the list.For all the buckets that have not been set by any other signature,we only allocate one list node shared by these buckets.Since we already know that most of lists in EBF only include one node,this scheme can signi?cantly reduce the memory consumption compared with the original one. The formal description of the improved algorithm is shown in Figure

7

Fig.7.Improved EBF Programming Algorithm

In Figure8,we use10hash functions and20K EBF buckets to test our improved algorithm.Simulation shows63%to90% memory saving and the saving is more signi?cant when the number of signatures is smaller.However,the absolute saving becomes bigger and bigger when the number of signatures increases.

Fig.8.Memory Saving with Improved Algorithm

Other optimization is also possible.Since we only store each signature once by using exogenous link lists,this doubles the number of memory accesses to retrieve a signature.To improve the lookup performance,we can save a short hash digest in each link list node to avoid the unnecessary comparison with the long signature itself by comparing the hash digest?rst.

VII.L ONG S IGNATURE M ATCHING

The signature lengths distribute in a large range from a few bytes to hundreds.In a parallel computing environment,it is infeasible to maintain an EBF for each signature length. Based on the system resource availability,we set a threshold t.Any signature longer than t should be segmented into a set of substrings with length of t except the last substring that the

length is possible shorter than t.Two more bits are needed to store along with each substring in off-chip memory.One bit indicates if this substring is a partial match,the other bit indicates if this partial match substring is the?rst segment.We also maintain two other tables.One is the Partial Match Table (PMT)with t×t bytes.One extra bit for each entry indicates if this entry is valid.The other table is the Concatenate Veri?cation Table(CVT)that stores all the combinations of two segments that form the substrings of the signatures.One extra bit indicates if this is the last two segments.CVT could be implemented using hash table or CAM.

From the start of payload scan,a pointer p is calculates as (#of bytes scanned)%t.Table I shows the algorithm for long signature matching.

TABLE I

L ONG S IGNATURE M ATCHING A LGORITHM

if a string is partial and the?rst segment

register it in entry p

else if a string is partial but not the?rst segment

if the PMT entry p is valid

concatenate this string with content in entry p

lookup the CVT

if found in CVT

if indicate this is the last block

report match

else

replace the old entry in PMT with this string

else

drop it

invalidate the entry p

else

drop it

For Snort rule set,if we set t=24,then PMT is only 576bytes and the CVT needs to store only about20048-byte substrings.We need only maintain less than24EBFs instead of more than50.

VIII.I MPLEMENTATION

Network Intrusion Detection Systems need to inspect the packets based on both the header and payload.A hardware-based NIDS,taking the advantages of the stat-of-art FPGA technology,is proposed to implement the Snort rule matching. Figure9illustrates the high-level overview of the system. The layered protocol wrappers are used to handle the TCP/IP header and maintain the?ow state[11]and the module of packet header classi?cation is used to inspect the packet header and help to match the full rules[13].We implement our data structure and lookup algorithm in string matching module to identify the signatures.

An FPGA chip can contain more than10-Mbits SRAMs, con?gured in different depth and width[1].We implement the front-end EBF data structure on-chip by taking advantage of these?exible multi-port SRAMs.

The link lists and signatures are stored in off-chip memory. The memory management logic dynamically allocates the memory for them.The operation takes several clock cycles. This extra complexity only happens when we initialize or

Fig.9.FPGA-based NIDS

update the signatures in EBF.As we know,the database updating is relatively infrequent compared with the signature matching task,so this is not a serious concern.

IX.C ONCLUSION

In this paper,we present a new data structure that extends the classical Bloom Filter.We propose the corresponding al-gorithms for hardware-based multi-pattern signature matching. We also present a scheme to support long signature matching. We demonstrate that our EBF algorithm is highly ef?cient in terms of both throughput and memory storage.We integrate this algorithm into a full-featured hardware-based NIDS.We believe this is a key step towards building a real time NIDS, which is capable of actively monitoring and?ltering all pass-through traf?c even in a high-speed network.

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表面等离子体激元研究现状及应用

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英语翻译技巧实例

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进行全部倒置,原则是使汉语译句安排符合现代汉语论理叙事的一般逻辑顺序。有时倒置法也用于汉译英。如: (1)At this moment, through the wonder of telecommunications, more people are seeing and hearing what we say than on any other occasions in the whole history of the world. 此时此刻,通过现代通信手段的奇迹,看到和听到我们讲话的人比整个世界历史上任何其他这样的场合都要多。(部分倒置) (2)I believe strongly that it is in the interest of my countrymen that Britain should remain an active and energetic member of the European Community. 我坚信,英国依然应该是欧共体中的一个积极的和充满活力的成员,这是符合我国人民利益的。(部分倒置) (3)改革开放以来,中国发生了巨大的变化。 Great changes have taken place in China since the introduction of the reform and opening policy.(全部倒置) 7.倒置法: 在汉语中,定语修饰语和状语修饰语往往位于被修饰语之前;在英语中,许多修饰语常常位于被修饰语之后,因此翻译时往往要把原文的语序颠倒过来。倒置法通常用于英译汉, 即对英语长句按照汉语的习惯表达法进行前后调换,按意群或进行全部倒置,原则是使汉语译句安排符合现代汉语论理叙事的一般逻辑顺序。有时倒置法也用于汉译英。如: (1)At this moment, through the wonder of telecommunications, more people are seeing and hearing what we say than on any other occasions in the whole history of the world.

十四种雅思阅读题型做题技巧

十四种雅思阅读题型做题技巧 一、Matching(从属关系搭配题) 1.题型要求 Matching(搭配题)是IELTS最常考的题型之一,每次考试至少有一组,很多时候达到两组甚至更多,应该引起同学们的重视。搭配题通常有三部分组成:题目要求、选相的集合、题目的集合。要求你根据某种关系将题目与选项配对。这种关系在题目要求中说明。搭配题通常不具有顺序性,也就是说,题目的顺序和原文的顺序是不一致的。所以做搭配题,通读一遍原文一般是不可避免的。大多数同学会感觉这种题型不太难,但比较烦。如果有足够的时间,肯定都能做出来。这种题型就是考大家的快速阅读能力,即很快从文章中找到所需信息的能力。大家在做搭配题的练习时,不仅要保证准确率,还要注意时间。一般每个题目1-2分钟。例如一组6个题目的搭配题,应在8-10分钟做完。搭配题本身有分为三种类型:从属关系搭配题、因果关系搭配题、作者及其观点搭配题。 不同类型的搭配题有不同的解题方法,所以我分为三种题型分别介绍,希望这样会更清楚。本节主要介绍从属关系搭配题。从属关系搭配题是搭配题中最常靠的类型。选项中的元素和题目中的元素是从属的关系,要求根据原文,将每个题目与相应的选项搭配。 请看下面中文阅读例子: 原文:介绍中国四个直辖市的情况。(略) 题目要求:将直辖市的特征与相应的城市搭配。 选项:A 北京市B 上海市 C 天津市D 重庆市 题目:1 狗不里包子2 黄浦大桥3 天安门4 十八街麻花 答案:1C 2 B 3A 4C 考试中,A类和G类一般都是每次必考,考两组,共十题左右。有时也考一或三组。 2.解题步骤 (1)仔细阅读题目要求,搞清选项和题目之间的关系。 虽然都是从属关系,但也有很大的不同。有的是汽车制造公司和它们的设计特点,有的是俱乐部和它们举办的活动等等。所以在做题前一定要仔细阅读题目要求,搞清选项和题目之间的关系。 (2)先把题目从头到尾看一遍,尽可能多记些关键词。做这种题,通读一遍原文一般是不可避免的,但最好只读一遍原文。能做到这点的前提是:在读原文之前,先把题目从头到尾看一遍,记住尽可能多的题目中关键词。这样,在读文章时,就知道要找什么东东。例如,前面介绍的中文阅读文章,在做题前应看以下1-4题,知道要找:狗不里包子、黄浦大桥、天安门、十八街麻花。在读文章是,就特别注意这些词。这么做的主要原因是因为这种题型没有顺序性,如果只看第1题:狗不理包子,就阅读原文。狗不理包子的描述在原文任何位置都有可能,很有可能就在文章的结尾处。这样,你读完一遍文章,只做了一个第1题,做第2题时,你还需要从头再阅读原文,这样很浪费时间。这一步是做这种题型的关键,否则,就会造成反复地读原文,浪费时间。 (3)从头到尾快速阅读原文,遇到所记住的关键词或其对应词,当即解答该题。从头到尾依顺序快速阅读原文,在阅读原文时,注意寻找所记住的题目中的关键词,如果关键词是专用名词或很生僻的词,一般在原文中出现的就是该词本身,否则会有同义词或同义表述的对应。选项常常是一些专用名词,包含大写字母。这时,在阅读原文时,应特别注意包含大写字母的地方,注意其前后的词是否与所记住的关键词相同或相对应。在阅读原文是,要特别注意原文中一些有特别标记的词,如反复出现的词、括好里的词、引号里的词、黑体字、斜体字,这些词常常是题目中的关键词或其改写。如果关键词比较抽象,比如是专有名词或者很生僻的词,不好记忆,大家也不要害怕。这可能是件好事,在原文中出现的很可能就是该词本身。所以即使记不住,在阅读原文时,可以采用边读文章边看选项的方法。因为在读原文之前,已把题目从头到尾看了一遍,所以最好只读一遍文章就能做出绝大多数的题目。比如一组6个题目,能找到4-5个。 (4)解答没有匹配上的题目。由于有的关键词没有记住,或者有的与原文没有对应上。读完一遍文章后,有的题目没有找出来。比如一组6个题目,1-2个没能找到。这已是很不错的表现了。对于这1-2个题目,可以有如下的解决办法:

表面等离子体激元简介

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种:(1)棱镜耦合:棱镜耦合的方式包括两种,一种是Kretschmannt方式;另一种是Otto方式。(2)采用波导结构(3)采用衍射光栅耦合(4)采用强聚焦光束(5)采用近场激发。目前主要的仿真方法有以下三种(1)时域有限差分法(finite difference time domain,FDTD):FDTD方法是把Maxwell方程式在时间和空间领域上进行差分模拟,利用蛙跳式(leaf flog algorithm)空间领域内的电场和磁场进行交替计算,电磁场的变化通过时间领域上更新来模仿。优点是能够直接模拟场的分布,精度比较高,是目前使用较多的数值模拟方法之一。(2)严格耦合波法(rigorous coupled—wave analysis,RCWA):该方法是分析光栅的有利工具,它是基于严格的矢量maxwell 方程来分析。由于在很多的表面等离子的结构中都会引入衍射光栅结构,所以RCWA方法也被越来越多的学者用来分析相关的问题,并且取得了不错的效果。(3)限元法(finite element method,FEM):该方法是从变分原理出发,将定义域进行有限分割,离散成有限个单元集合。通过区域剖分和分偏差值,把二次泛函的极值问题化为普通多元二次函数的极值问题,后者等价于一组多元线性代数方程的求解。该方法分析的是一种近似结果,不过很多的问题能近似模拟,目前应用也比较广泛。三.SPPs的若干应

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