An Evaluation of Negative Selection in an Artificial Immune System for Network Intrusion Detection

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1 An Evaluation of Negative Selection in an Atificial Immune System fo Netwok Intusion Detection Abstact This pape investigates the ole of negative selection in an atificial immune system (AIS) fo netwok intusion detection. The wok focuses on the use of negative selection as a netwok taffic anomaly detecto. The esults of the negative selection algoithm expeiments show a sevee scaling poblem fo handling eal netwok taffic data. The pape concludes by suggesting that the most appopiate use of negative selection in the AIS is as a filte fo invalid detectos, not the geneation of competent detectos. 1 INTRODUCTION The biological immune system has been successful at potecting the human body against a vast vaiety of foeign pathogens (Tizad, 1995). A gowing numbe of compute scientists have caefully studied the success of this competent natual mechanism and poposed compute immune models fo solving vaious poblems including fault diagnosis, vius detection, and motgage faud detection (Dasgupta, 1998; Kephat et al,1995). Among these vaious aeas, intusion detection is a vigoous eseach aea whee the employment of an atificial immune system (AIS) has been examined (Dasgupta, 1998; Kim and Bentley, 1999b; Hofmey, 1999; Hofmey and Foest, 2000; Foest and Hofmey, 2000). The main goal of intusion detection is to detect unauthoised use, misuse and abuse of compute systems by both system insides and extenal intudes. Cuently many netwok-based intusion detection systems (IDS s) have been developed using divese appoaches (Mykejee et al, 1994). Nevetheless, thee still emain unesolved poblems to build an effective netwok-based IDS (Kim and Bentley, 1999a). As one appoach of poviding the solutions of these poblems, pevious wok (Kim and Bentley, 1999a) identified a set of geneal equiements fo a successful netwok-based IDS and thee design goals Jungwon Kim and Pete J. Bentley Depatment of Compute Science Univesity College London Gowe Steet, London U.K. {J.Kim, P.Bentley}@cs.ucl.ac.uk to satisfy these equiements: being distibuted, selfoganising and lightweight. In addition, Kim and Bentley (1999a) intoduced a numbe of emakable featues of human immune systems that satisfy these thee design goals. It is anticipated that the adoption of these featues should help the constuction of an effective netwokbased IDS. An oveall atificial immune model fo netwok intusion detection pesented in (Kim and Bentley, 1999b) consists of thee diffeent evolutionay stages: negative selection, clonal selection, and gene libay evolution. This model is not the fist attempt to develop an AIS fo netwok intusion detection. Vaious appoaches to build an AIS have been attempted mainly by implementing only a small subset of oveall human immune mechanisms (Dasgupta, 1998). This is because the natue of human immune systems is vey complicated and sophisticated and thus it is vey difficult to implement pefect human immune pocesses on a compute. Howeve, as seen fom othe immunology liteatue (Paul, 1993; Tizad, 1995), an oveall immune eaction is the caefully co-odinated esult of numeous components such as cells, chemical signals, enzyme, etc. Theefoe, the omission of cucial components in ode to make the development of AIS simple and moe applicable may detimentally affect the pefomance of an AIS. This implies that appopiate atificial immune esponses can be expected only if the oles of cucial components of human immune systems ae coectly undestood and they ae implemented in the ight way. In this pape, we continue ou effot to undestand the oles of impotant components of atificial immune systems especially fo poviding appopiate atificial immune esponses against netwok intusions. Following ou pevious wok identifying thee diffeent evolutionay stages: negative selection, clonal selection, and gene libay evolution, of AIS by extensive liteatue study (Kim and Bentley, 1999a; 1999b), this pape focuses on the investigation of the oles of fist stage: negative selection. With implementation details of this stage, this wok pesents how and which aspects of negative

2 selection can contibute to the development of an effective netwok-based IDS. 2 BACKGROUND 2.1 NEGATIVE SELECTION OF THE HUMAN IMMUNE SYSTEM An impotant featue of the human immune systems is its ability to maintain divesity and geneality. It is able to detect a vast numbe of antigens with a smalle numbe of antibodies. In ode to make this possible, it is equipped with seveal useful functions (Kim and Bentley, 1999a). One such function is the development of matue antibodies though the gene expession pocess. The human immune system makes use of gene libaies in two types of ogans called the thymus and the bone maow. When a new antibody is geneated, the gene segments of diffeent gene libaies ae andomly selected and concatenated in a andom ode, see figue 1. The main idea of this gene expession mechanism is that a vast numbe of new antibodies can be geneated fom new combinations of gene segments in the gene libaies. G ene Libay A ntigen to each self patten defined. If any andomly geneated patten matches a self patten, this patten fails to become a detecto and thus it is emoved. Othewise, it becomes a detecto patten and monitos subsequent pofiled pattens of the monitoed system. Duing the monitoing stage, if a detecto patten matches any newly pofiled patten, it is then consideed that new anomaly must have occued in the monitoed system. This negative selection algoithm has been successfully applied to detect compute viuses (Foest et al., 1994), tool beakage detection and time-seies anomaly detection (Dasgupta, 1998) and netwok intusion detection (Hofmey, 1999; Hofmey and Foest, 2000; Foest and Hofmey, 2000). Besides these pactical esults, D haeselee (1997) showed seveal advantages of negative selection as a novel distibuted anomaly detection appoach. 3 ALGORITHM OVERVIEW This wok used a negative selection algoithm to build an anomaly detecto. This was achieved by geneating detectos containing non-self pattens. The oveview of this algoithm is povided in figue 2 and 3. The negative selection algoithm fo netwok intusion detection used in this pape follows the algoithm of Foest et al (1994, 1997), descibed in the pevious section. Self was built by pofiling the activities of each single netwok connection. The detail of self pofiling is descibed in the next section. A ntibody S elf Stings Figue 1 Gene Expession Pocess Howeve, this mechanism intoduces a citical poblem. The new antibody can bind not only to hamful antigens but also to essential self cells. To help pevent such seious damage, the human immune system employs negative selection. This pocess eliminates immatue antibodies, which bind to self cells passing by the thymus and the bone maow. Fom newly geneated antibodies, only those which do not bind to any self cell ae eleased fom the thymus and the bone maow and distibuted thoughout the whole human body to monito othe living cells. Theefoe, the negative selection stage of the human immune system is impotant to assue that the geneated antibodies do not to attack self cells. 2.2 THE NEGATIVE SELECTION ALGORITHM Foest et al (1994; 1997) poposed and used a negative selection algoithm fo vaious anomaly detection poblems. This algoithm defines self by building the nomal behaviou pattens of a monitoed system. It geneates a numbe of andom pattens that ae compaed G en ea te R a nd om Sting s M atch y e s R e ject n o D e te cto Set Figue 2 Detecto Set Geneation of a Negative Selection Algoithm (Foest et al, 1995) D e te cto Set N e w Stings M a tch y e s Figue 3 Non-Self Detection by a Detecto Set D e te cted N o n-se lf Even though this wok follows the implementation details of Foest et al s negative selection algoithm, thee ae two implementation details diffeent fom Foest et al (1994, 1997). In the encoding of detectos, each gene of a

3 detecto has an alphabet of cadinality 10 with values fom 0 to 9 and the allele of this gene indicates the cluste numbe of coesponding field of pofiles. As pesented in the next section, the self pofile built fom the fist data set has 33 fields and this numbe detemines the total numbe of coesponding genes in the detectos. Fom these 33 fields, the values of 28 fields ae continuous and the values of the othe 5 fields ae discete. Specifically, the continuous values of 28 fields show a wide ange of values. In ode to handle this vaious and boad ange of values, an oveall ange of eal values fo each field is soted. Then, this ange is discetised into a pedefined numbe of clustes. The lowe bound and highe bound of each cluste ae detemined by ensuing that each cluste contains the same numbe of ecods. This modification is necessay in ode to save the length of encoded detecto. Futhemoe, ou implementation of measuing the similaity between a geneated detecto and a self pofile is opeated at the phenotype level while Foest et al s (1994, 1997) is pefomed at the genotype level. In ode to measue the similaity between a given detecto and a self, the genotype of a detecto is mapped onto a phenotype. The phenotype mapped fom the evolved genotype is epesented in a fom of a detecto patten. As shown in figue 5, a field of a detecto phenotype is epesented by an inteval having a lowe bound and a highe bound while a field of a self phenotype is descibed by one specific value. Hence, the fist step of measuing the similaity checks whethe a value of each field of a self patten belongs to a coesponding inteval of a detecto phenotype. When any value of a self patten field is not included in its coesponding inteval of a detecto phenotype, these two fields ae not matched. Similaly, fo a nominal type of field, two fields match when the values of fields ae identical. The final degee of similaity between a given detecto and self example follows the same matching function of Foest et al (1994), the -contiguous matching function. Thus, the degee of similaity is measued simply by counting the matching coesponding fields. Fo instance, if an activation theshold,, is set as 2, the detecto phenotype and self phenotype in the figue 4 will match since two contiguous fields, Numbe of Packet and Duation, match and this numbe of contiguous matching fields equals to the activation theshold. Howeve, if this theshold is set as 3, it is egaded that two phenotypes do not match. Detecto Phenotype = ( Numbe of Packet = [10, 26], Duation = [0.3, 0.85], Temination = `half closed, etc) Self Phenotype = ( Numbe of Packet = 14, Duation = 0.37, Temination = `nomal,.etc) Figue 4: A Detecto Phenotype and a Self Phenotype 4 NETWORK TRAFFIC DATA VS NETWORK INTRUSION SIGNATURE The data chosen fo this wok was collected fo a pat of the Infomation Exploation Shootout, which is a poject poviding seveal data sets publicly available fo exploation, discovey and collecting the esults of paticipants 1. The set used hee was ceated by captuing TCP packet heades that passed between the inta-lan and extenal netwoks as well as within the inta-lan. This set consists of five diffeent data sets. The TCP packet heades of the fist set wee collected when no intusion occued and the othe fou sets wee collected when fou diffeent intusions wee simulated. These intusions ae: IP spoofing attack, guessing login o ftp passwods, scanning attack and netwok hopping attack. The details of attack signatues and attack points of the fou diffeent attacks ae not available. The data oiginally had the fields of netwok packets captuing tool s fomat such as time stamp, souce IP addess, souce pot, destination IP addess, destination pot, etc. Howeve, the pimitive fields of captued netwok packets wee not enough to build a meaningful pofile. Consequently, it was essential to build a datapofiling pogam to extact moe meaningful fields, which can distinguish nomal and abnomal. Many eseaches have identified the secuity holes of TCP potocols (Poas and Valdes, 1998; Lee, 1999) and so the fields used by ou pofiles wee selected based on the extensive study of this eseach. They wee usually defined to descibe the activities of each single connection. The automated pofile pogam was developed to extact the connection level infomation fom TCP aw packets and it was used to elicit the meaningful fields of the fist data set. Fo each TCP connection, the following fields wee extacted: Connection identifie: each connection is defined by fou fields, initiato addess, initiato pot, eceive addess and eceive pot. Thus, these fou fields ae included in the pofile fist in ode to identify each connection. Known pot vulneabilities: many netwok intusions attack using vaious types of pot vulneabilities. Thee ae fields to indicate whethe an initiato pot o a eceive pot potentially holds these known vulneabilities. 3-way handshaking: TCP potocol uses 3-way handshaking fo a eliable communication. When some netwok intusions attack, they often violate the 3-way handshaking ule. Thus, thee ae fields to check the occuences of 3-way handshaking eos. Taffic intensity: netwok activities can be obseved by measuing the intensity ove one connection. Fo example, numbe of packets and numbe of kilobytes 1 Available at netwok.html.

4 fo one specific connection can descibe the nomal netwok activity of that connection. Thus, in total, self pofile fields had 33 diffeent fields fo the data set. Even though the netwok pofile fields wee extacted to descibe a single connection activity, the data used in this eseach was too limited to apply this initial pofile. The limit was that the data was collected fo a quite shot time, aound 15~20 minutes. Duing this bief peiod, most diffeent connections wee established only once. An insufficient quantity of data was collected to build diffeent connection pofiles. Theefoe, it was necessay to goup diffeent connections into seveal meaningful categoies until each categoy had a sufficient numbe of connections to build a pofile. Consequently, a total numbe of connections fo each potential pofile categoy wee counted. Fist of all, the data was categoised into two diffeent goups: inte-connection and inta-connection. Inteconnection was the goup of connections that wee established between intenal hosts and extenal hosts, and inta-connection was the goup of connections that wee established between intenal hosts. Futhemoe, to peseve anonymity, all intenal hosts had a single fake addess 2 and any exta infomation about extenal hosts and netwok topology was not povided. Theefoe, the pofiles accoding to specific hosts wee insufficient. Instead, in this eseach, only the pofiles of specific pots on any hosts wee consideed. Accoding to vaious possible categoies, the established connection numbe of each pofile was counted. Fom each case, apat fom a pofile class that had moe than 100 connections, othe pofile classes wee again gouped into othe diffeent classes until each class had moe than 100 connections. Finally, 13 diffeent self pofiles wee built. Thei class names and the numbe of established connections ae shown in table 1. In table1, the class column of inte-connection is shown as: {(a,b),(c,d)}, whee a is an intenal host, b is a intenal pot numbe, c is a extenal host addess and d is an extenal pot numbe. Hence, the connection is established between (a,b) and (c, d). Fo the class column of inta-connection, a is an intenal host addess, b is an intenal pot numbe, c is an intenal host addess and d is an intenal, pot numbe. * indicates any host addess and any pot numbe. In addition, well-known shows the pots in the ange 0 to 1023 ae tusted pots. These pots ae esticted to the supeuse: a pogam must be unning as oot to listen to a connection. The pot numbes of commonly used IP sevices, such as ftp, telnet, http, ae fixed and belong to this ange. But, many common netwok sevices employ an authentication pocedue and intudes often use them to sniff passwods. It is wothwhile to monito these pots sepaately fom the othe pots. Theefoe, if the numbe of connections fo any pofile categoy, which is based on a specific pot on any hosts, is not sufficient, these categoies ae egouped into two new classes, a wellknown pot and a not well-known pot. Table 1: Self Pofiles Class Inte-connection Numbe of Connection {(2, *), (*, 80)} 5292 {(2, *), (*, 53)} 919 {(2, *), (*, 113)} 255 {(2, *), (*, 25)} 192 {(2, *), (*, well-known)} 187 {(2, *), (*, not well-known)} 756 {(2, 53), (*, *)} 940 {(2, 25), (*, *)} 352 {(2, 113), (*, *)} 145 {(2, well-known), (*, *)} 114 {(2, not well-known), (*, *)} 6050 Inta-connection {(2, *), (2, well-known)} 190 {(2, *), (2, not well-known)} EXPERIMENT OBJECTIVE Although pevious wok using a negative selection algoithm fo anomaly detection (Foest et al 1994; Dasgupta 1998; Hofmey, 1999) showed pomising esults, thee had been little effot to apply this algoithm on vast amounts of data. One distinctive featue of a netwok intusion detection poblem is that the size of data, which defines self and non-self, is enomous. In ode fo this algoithm to be adopted to a netwok-based IDS, it is impotant to undestand whethe this algoithm is capable of geneating detectos in a easonable computing time. In addition, it is essential to examine whethe its tuning method, which deives an appopiate numbe of detectos to gain a good non-self detection ate, woks when it is used on the huge size of eal netwok data. Theefoe, a seies of expeiments wee pefomed to investigate these two significant featues of the negative selection algoithm. 6 DATA AND PARAMETER SETTING 6.1 SETTING As pesented in section 4, the data used in this wok poduced thiteen diffeent self pofiles. Fom 13 diffeent self sets, one self set, {(2, *), (*, 25)} in table 1, which has elatively smalle numbe of examples, 192, was selected fo the following expeiments. Fom the total of 192 examples of the selected self pofile, 154 examples wee used fo geneating detectos and 38 examples wee

5 applied fo testing geneated detectos. In addition, the detectos wee tested on five diffeent test sets. The fist fou sets wee collected when fou diffeent intusions wee simulated (as explained in section 4) and the last set was ceated by geneating andom stings. These five sets have 273, 190, 1151, 273 and 500 examples espectively. As descibed in section 3, the negative selection algoithm used in this pape employed the -contiguous matching function. Fo the following expeiments, its matching theshold should be defined. In ode to define this numbe, the fomulas to appoximate the appopiate numbe of detectos when a false negative eo is fixed (D haeselee, 1997; Foest et al, 1994) wee used. These fomulas ae as follows (Foest et al, 1994) : - Pm m [( l )( m 1) / m 1].... (1) P m whee, P m 1.. (2) N s the matching pobability between a a andomly chosen self sting, detecto sting and N s the numbe of self stings, m the detecto genotypealphabet cadinality, l the detecto genotype sting length and = the theshold of -contiguous matching function. Since N s,m, l ae aleady known, can be calculated by using equation (1) and (2). The calculated was used in the following equation in ode to deive an appopiate numbe of detectos, N, and a total numbe of tials to geneate these detectos, eo, N N 0 P f, is fixed (Foest et al, 1994)., when the false negative ln P f (3) and P m m ln P N 0 P (4) f N 1 Pm S The selected self set, {(2, *), (*, 25)}in table 1, was used fo calculating N and N when P f is fixed. Table 2 shows calculated N and 0 N 0 Pf and have vaious values. using (3) and (4) when (D haeselee, 1997; Foest, et al, 1994) showed that the lage matching theshold dives the ceation of less geneal detectos and thus it equies a lage numbe of detectos but a smalle numbe of detecto geneation etials. This is because less geneal detectos ae easie to avoid the matching a self pofile. N and follows the same tendency. N 0 in table 2 Table 2 Numbe of equied detectos, N and numbe of tials to geneate equied numbe of detectos, N 0 when false negative eo, P f, and the theshold of -contiguous matching function ae given. These numbes ae calculated when a self sting length, l = 33, an alphabet cadinality, m = 10 and the numbe of self stings, N = 192. P f N = 3 = 4 N 0 S N N Even though this fomula is clealy useful to pedict the appopiate numbe of detectos and its geneation numbe, its pedicted numbe showed how infeasible this appoach is when it is applied on a moe complicated but moe ealistic seach space. Fo instance, when the expected false negative eo ate is fixed as 20%, its pedicted detecto geneation tial numbe is 51 and the appopiate numbe of geneated detectos is fo the matching theshold is 3. Similaly, when we define the matching theshold as 4, it pedicted 535 fo the fome and 955 fo the latte. In addition, it was obseved that when we fixed the matching theshold numbe as fou and an the system, the system could not manage to geneate any single valid detecto afte one day. None of these cases seem to povide any feasible test case in tems of computing time. This esults cetainly did not follow the pedicted detecto geneation tial numbe. Thus, fo the following expeiments, we geneated valid detectos by setting a matching theshold numbe that allowed a system to geneate a valid detecto in a easonable time. It was obseved that the aveage time of single successful detecto geneation took about 70sec CPU time and the aveage numbe of tials to geneate a valid detecto was 2~3 when a matching theshold was nine. These esults wee gained afte unning the negative selection algoithm fo peliminay expeiments. This numbe is used as the matching theshold fo the following expeiments. The details of these expeiment esults ae descibed in the next section. 7 EXPERIMENT RESULT Five diffeent sets of detectos wee geneated afte the AIS with the negative selection was un five times. Even though the matching theshold, 9, gave easonable computing time to geneate a valid detecto, it equies a lage numbe of detectos to gain a good non-self

6 Table 3 The mean and vaiance values of intusion and self detection ates when detecto set size vaies The means values ae followed by the vaiances in the paentheses. Num. Of Detectos Intusion1 Intusion 2 Intusion 3 Intusion 4 Intusion 5 Test Self Set (2.11) 10.11(8.50) 11.14(9.44) 10.62(4.03) 0.48(0.012) 7.89(17.31) (5.37) 11.58(13.71) 12.98(11.52) 12.89(10.43) 0.88(0.092) 9.47(36.70) (4.25) 11.89(13.24) 13.73(9.48) 13.63(9.15) 1(0.12) 10(29.08) (2.79) 12.32(11.30) 14.58(10.18) 14.36(6.87) 1.28(0.112) 10.53(31.16) (3.15) 12.74(13.63) 14.89(10.40) 14.51(7.35) 1.36(0.068) 11.05(25.62) (3.80) 13.16(11.91) 15.07(10.24) 14.65(8.12) 1.68(0.412) 11.58(29.78) (3.80) 13.16(11.91) 15.26(9.46) 14.65(8.12) 2.04(0.388) 11.58(29.78) (4.09) 13.26(11.27) 15.45(10.09) 14.80(8.22) 2.04(0.388) 11.58(29.78) (4.13) 13.47(10.47) 15.67(9.69) 15.02(8.52) 2.08(0.352) 12.63(46.40) (4.32) 14.08(11.52) 15.90(8.71) 15.09(8.68) 2.28(0.312) 12.63(46.40) detection ate. Afte taking into account pactically easonable time to geneate a whole data set, up to 1000 valid detectos wee geneated pe un. All expeiments wee un on a PC with AMD K Mhz pocesso and 128M RAM. Table 4 Time is an avaage time of single detecto geneation and Tial is an aveage tial numbe to geneate a single detecto. The aveage values ae followed by the standad deviations in paentheses. System Run Time (Sec) Detecto Geneation Tial (26.85) 2.80(2.16) (28.88) 2.21(1.65) (33.72) 2.81(2.22) (39.86) 3.12(2.69) (26.62) 2.72(2.07) Aveage 71.81(32.75) 2.63(2.14) Table 3 shows the aveage time of single successful detecto geneation and the aveage numbe of tials to geneate a valid detecto. Compaed to the esult when the matching theshold is fou, which did not geneate any single detecto afte 24 hous, these esults cetainly look moe applicable. We monitoed five diffeent non-self sets and one peviously unseen self sets afte evey 100 detecto geneation and the monito esults of five diffeent uns ae shown in table 4. The oveall non-self detection ate was vey poo: less than 16%. In paticula, the non-self detection ate fo the last intusion set, which was atificially geneated by andom stings, is extemely low and its maximum aveage non-self detection ate eaches only 2.28%. In addition, its aveage false positive detection ate, which is self detection ate by a detecto set, shows 12.63% and this ate is not hugely diffeent fom the othe fou aveage non-self detection ates except intusion 5. This implies that the collected self and non-self sets pehaps have some ovelapping pattens because they showed quite simila detection ates. Thus geneated detecto sets completely failed to distinguish the hidden self and non-self pattens. These poo esults wee anticipated. This is because the matching theshold was set in ode to obtain a easonable detecto geneation time. If, fo example, we wanted a moe usable 80% non-self detection ate, detectos would be equied (this numbe is also obtained fom equation 3). The lagest size of a geneated detecto set, 1000, was much smalle than this numbe and this caused such poo esults. In addition, each un aleady took about 20 hous 2 to geneate 1000 detectos. If we wished to geneate detectos, it would equie hous, o about 1,429 yeas on the same compute. Accoding to Mooe's Law, the pocessing speed of computes doubles evey 18 months. We would have to wait aound 35 yeas befoe the aveage pocessing speed of computes became fast enough to geneate these detectos in an hou - and this is fo just 15~20 minutes of a tiny subset of the netwok taffic data. 8 ANALYSIS In contast to the pomising esults shown in Hofmey s negative selection algoithm fo netwok intusion detection (Hofmey, 1999; Hofmey and Foest, 2000), the esults of these expeiments aise doubt whethe this algoithm should be used fo netwok intusion detection. In ode to answe this question, the negative selection algoithm fo netwok intusion detection is analysed in detail. The main poblem of the negative selection algoithm is a sevee scaling poblem. Unlike pevious wok using 2 Since it took, on aveage, 72 seconds to geneate each detecto, seconds wee needed to poduce 1000 detectos seconds ae 20 hous.

7 the negative selection algoithm fo anomaly detection, hee we apply a much lage self set to the negative selection algoithm. The definition of lage self set was essential to cove divese types of netwok intusions. Fo instance, (Hofmey 1999; Hofmey and Foest, 2000) defines self as a set of nomal paiwise connections between computes. These include connections between two computes in the LAN and between one compute in the LAN and extenal computes. The connection between computes is defined by data-path-tiple : (the souce IP addess, the destination IP addess, the pot called fo this connection). This self definition is chosen based on the wok by (Hebelein, et al, 1990). Howeve, as othe IDS liteatue pointed out (Lee, 1999), this self definition is vey limited in ode to detect vaious types of netwok intusions and it will cetainly be impossible to detect some intusions that occu within a single nomal connection such as unauthoised access fom a emote machine. Howeve, as obseved in section 4, when the self definition widens, a binay sting to encode a detecto lengthens. As the esult of long length of binay detectos, an appopiate numbe of detectos to gain an acceptable false negative eo becomes huge and thus equies an unacceptably long computation time. Ou pevious expeiment esults clealy show this poblem. It should be noted that Hofmey (1999) developed a efined theoy and multiple seconday epesentations and these help to educe the numbe of tials to geneate detectos on stuctued self as much as thee odes magnitude less. These methods made the distibution of a self set clump and it esulted in the eduction of the numbe of detecto geneation tials. Howeve, the efined theoy and seconday epesentations add exta space and computing time. Moe impotantly, all of the suggested seconday epesentations, such as pue pemutation, impefect hashing and substing hashing, ae matching ules which check matching only on genotypes. Unfotunately, matching ules that opeate only at the genotype level have a weakness to be applied fo a netwok intusion detection poblem. This deficiency can be explained by unavelling the poblem of -contiguous matching function. We used the -contiguous ule to check the match between a given detecto and antigen. The main pupose of using it was in ode to employ the fomula to appoximate an appopiate numbe of detectos to gain a cetain non-self detection ate. Howeve, the -contiguous matching ule is too simple to detemine the matching between athe complicated and high-dimensional pattens. It has been aleady known that most ules to epesent intusion signatues descibe coelation among significant netwok connection events and tempoal cooccuences of events (Lee, 1999; Poas, 1998). Since the -contiguous bit matching only measues the contiguous bits of genotypes of given two stings, it is had to guaantee that the -contiguous bit matching can catch this kind of coelation fom given self and non-self pattens. The wide ange of self definition shown in section 4 is also suggested in ode to extact this type of coelation fom given self and non-self netwok taffic examples. But, if any new matching function is employed, D haeselee s (1997) fomula is no longe valid. Thee is no way to tune the ight numbe of detectos fo negative selection. Theefoe, this difficulty may foce the negative selection algoithm to adopt an abitay numbe of detectos and this may cause an unexpectedly low detection accuacy o inefficient computation by geneating moe than sufficient numbe of detectos. In addition, D haeselee s (1997) new detecto geneation algoithms using a linea-time algoithm and a geedy algoithm that guaantees a line time of detecto geneation is also not applicable when a diffeent matching function is used. In summay, it is necessay to use a moe sophisticated matching function to detemine the degee of coelation among significant netwok connection events and tempoal co-occuences of events. This equies deiving a new way to tune an appopiate numbe of detectos, which can be used fo moe sophisticated matching function. These dawbacks of the negative selection algoithm made the AIS stuggle to monito vast amount of a netwok self set despite its othe impotant featues 3. Consequently, the initial esults of ou expeiments motivated us to e-define the ole of negative selection stage within an oveall netwok-based IDS and design a moe applicable negative selection algoithm, which follows a newly defined ole. As much of the othe immunology liteatue (Tizad, 1995) addesses that the antigen detection powes of human antibodies ise fom the evolution of antibodies via a clonal selection stage. While the negative selection algoithm allows the AIS to be an invaluable anomaly detecto, its infeasibility to be applied on a eal netwok envionment is caused fom allocating a athe oveambitious task to it. To be moe pecise, the job of a negative selection stage should be esticted to tackle a moe modest task that is close to the ole of negative selection of human immune system. That is simply filteing the hamful antibodies athe than geneating competent ones. This view has been cooboated by futhe wok (Kim and Bentley, 2001) which has ecently shown how succesful the use of clonal selection with a negative selection opeato can be fo this type of poblem. 3 Hofmey and Foest (2000) s final system employs some othe extensions to suppot the opeation of AIS unde a eal netwok envionment. Among them, affinity matuation and memoy cell geneation follow the clonal selection concept and these povide a kind of evolution of a detecto set distibuted on monitoed hosts. Howeve, it still uses only the negative selection algoithm to geneate an initial detecto set. Even though it may confom to human immune systems moe closely, this appoach could equie excessive computation time to geneate the initial detecto set, if a boade definition of self is used. In addition, the usefulness of initial detectos is not poven befoe they ae distibuted to othe hosts. This may also cause a waste of othe computing esouces.

8 9 CONCLUSIONS This pape has investigated the ole of negative selection in an atificial immune system (AIS) fo netwok intusion detection. The negative selection stage within ou AIS was implemented following the algoithm ceated by Foest et al (1994; 1997) and applied to eal netwok data. The expeiments showed the infeasibility of this algoithm fo this application: the computation time needed to geneate a sufficient numbe of detectos is completely impactical. This esult diects this eseach to e-define the ole of negative selection algoithm within ou oveall atificial immune system famewok. Cuent wok is now investigating the intusion detection mechanism of the clonal selection stage. A new undestanding of the task of the clonal selection stage has now esulted in the development of a moe appopiate use fo negative selection as an opeato within a novel clonal selection algoithm (Kim and Bentley, 2001). Refeences D haeselee, P, (1997), A Distibuted Appoach to Anomaly Detection, ACM Tansactions on Infomation System Secuity. Dasgupta, D., (1998), An Oveview of Atificial Immune Systems and Thei Applications, In Dasgupta, D. (edito). Atificial Immune Systems and Thei Applications, Belin: Spinge-Velag, pp Foest, S. et al, (1994) Self-Nonself Discimination in a Compute, Poceeding of 1994 IEEE Symposium on Reseach in Secuity and Pivacy, Los Alamos, CA: IEEE Compute Society Pess. Foest, S., et al, (1997), Compute Immunology, Communications of the ACM, 40(10), Foest, S and Hofmey, S. (2000) "Immunology as Infomation Pocessing", in Design Pinciples fo Immune Systems and Othe Distibuted Autonomous Systems, (Ed) Segal, L.A. & Cohen, I. R. eds., Oxfod Univesity Pess. Hebelein, L. T., et al. (1990), "A Netwok Secuity Monito", Poceeding of 1990 Symposium on Reseach in Secuity and Pivacy, Oakland, CA, pp , May, Hofmey, S., (1999) An Immunological Model of Distibuted Detection and Its Application to Compute Secuity, Phd Thesis, Dept of Compute Science, Univesity of New Mexico. 14th Intl. Joint Conf. on Atificial Intelligence, Monteal, August, pp Kim, J. and Bentley, P. (1999a), The Human Immune System and Netwok Intusion Detection, 7th Euopean Confeence on Intelligent Techniques and Soft Computing (EUFIT '99), Aachen, Gemany. Kim, J. and Bentley, P. (1999b), The Atificial Immune Model fo Netwok Intusion Detection, 7th Euopean Confeence on Intelligent Techniques and Soft Computing (EUFIT 99), Aachen, Gemany. Kim, J. and Bentley, P. (2000), Negative Selection within an Atificial Immune System fo Netwok Intusion Detection, the 14th Annual Fall Symposium of the Koean Infomation Pocessing Society, Seoul, Koea. Kim, J. and Bentley, P. (2001), The Atificial Immune System fo Netwok Intusion Detection: An Investigation of Clonal Selection with a Negative Selection Opeato. Submitted to CEC2001, the Congess on Evolutionay Computation, Seoul, Koea, May 27-30, Lee, W., (2000) A Data Mining Famewok fo Constucting Featues and Models fo Intusion Detection Systems, PhD Thesis, Dept of Compute Science, Columbia Univesity. Mykejee, B., et al, (1994), "Netwok Intusion Detection", IEEE Netwok, Vol.8, No.3, pp Paul, W. E., (1993), The Immune System: An Intoduction, in Fundamental Immunology 3 d Ed., W. E. Paul (Ed), Raven Pess Ltd. Poas, P. A., (1992), STAT: A State Tansition Analysis Tool fo Intusion Detection, MSc Thesis, Depatment of Compute Science, Univesity of Califonia Santa Babaa. Poas, P. A. and Valdes, A., (1998), Live Taffic Analysis of TCP/IP Gateways, Poceeding of ISOC Symposium of Netwok and Distibuted System Secuity. Tizad, I. R., (1995), Immunology: Intoduction, 4 th Ed, Saundes College Publishing. Hofmey, S., and Foest, S., (2000), Achitectue fo an Atificial Immune System, Evolutionay Computation, vol.7, No.1, pp Kephat, J. O., et al, (1995), "Biologically Inspied Defenses Against Compute Viuses", the Poceeding of

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