Patent Application: US-96106207-A

Abstract:
a proactive worm containment solution for enterprises uses a sustained faster - than - normal outgoing connection rate to determine if a host is infected . two novel white detection techniques are used to reduce false positives , including a vulnerability time window lemma to avoid false initial containment , and a relaxation analysis to uncontain those mistakenly contained hosts , if there are any . the system integrates seamlessly with existing signature - based or filter - based worm scan filtering solutions . nevertheless , the invention is signature free and does not rely on worm signatures . nor is it protocol specific , as the approach performs containment consistently over a large range of worm scan rates . it is not sensitive to worm scan rate and , being a network - level approach deployed on a host , the system requires no changes to the host &# 39 ; s os , applications , or hardware .

Description:
target worm . we consider udp / tcp - based scanning worms including bandwidth - limited worms like the slammer to be the target of pwc . we also take local preferential scanning and hit - list scanning worms into consideration . however , pwc is not designed against slow , stealthy worms that scan , for example , a few destinations per minute . worm scan . for convenience of presentation , worm scans are classified into three types : l - l scans from an internal ( local ) infectee to an internal address , l - r scans from an internal infectee to an external ( remote ) address , and r - l scans from an external infectee to an internal address . connection attempts and successful connections . in pwc , the containment and the relaxation are mainly triggered by the analysis on outbound tcp syn and udp packets . hereafter , we mean outbound syn and udp packets when we mention outbound connection attempts . in addition , we mean outbound syn - ack and inbound udp packets by mentioning successful inbound connections . pwc system consists of a pwc manager and pwc agents . each host in the network runs a pwc agent which performs detection and suppression of worm scans going out from its host . a pwc agent can be implemented as ; [ i1 ] an os component such as a kernel driver ; [ i2 ] a separated box ; [ i3 ] a part of nic . our consideration in this paper is limited to i3 only . fig1 shows pwc deployment in an enterprise network and structure of the pwc agents . before getting into details , we briefly summarize operations of pwc system , from a to g , in an event - driven manner . we will discuss about the details and many other issues on following operations below , following the time line in fig2 . a : when a pwc agent detects a scan activity . the agent takes following actions in order : ( a1 ) the pwc agent raises a smoking sign ; ( a2 ) the agent initiates containment on its host , which is called active containment ; ( a3 ) the agent reports the smoking sign to the pwc manager ; ( a4 ) the agent starts relaxation analysis on its host . a3 is required in order to let other pwc agents be aware of the situation and check their hosts if they are infected . a4 is required since the agent needs to detect sustained faster - than - normal connection attempt to distinct destination addresses , to determine the host is infected . b : when pwc manager receives a smoking sign . the pwc manager propagates the smoking sign to all other agents except the agent who reported the smoking sign . at current stage , we assume smoking signs are propagated using ip broadcast since our goal in this paper is to show the feasibility of a novel containment technique . the frequency of smoking sign propagation is limited by the system . c : when a pwc agent receives a smoking sign . the agent takes following actions in order : ( c1 ) examine its own host based on vulnerability window lemma , to see if the host is possibly infected or not ; ( c2 ) if no evidence of possible infection is found , the agent ignores the smoking sign ; ( c3 ) otherwise , the agent initiates containment on the host , which is called passive containment ; ( c4 ) and immediately starts relaxation analysis . c1 and c2 are required to minimize availability loss possibly cased by excessive passive containments . d : when a pwc agent is performing relaxation analysis . the agent keeps calculating the rate of outbound connection requests initiated during the contained period . this is to check if the host shows sustained fast connection rate to new destinations or not . to minimize availability loss , the duration of relaxation analysis is limited to t relax seconds . e : when a pwc agent completes relaxation analysis . the agent relaxes or continues on - going containment on its host , depending on the result of relaxation analysis . if the agent relaxes the containment , it will start over above operations from a . if the agent continues the containment ( or a relaxation failure ), it will repeat d once more . after f relaxation failures , the agent will isolate its host and report to the pwc manager for further handling . we observed no isolated uninfected host through number of experiments with f = 30 and t relax = 1 . please note that , differently from conventional detection techniques , a false positive in white detection means the case in which a detector flags an infected host as uninfected . f : when signature extractors identify new signatures . the signatures are reported to the pwc manager . g : when pwc manager receives a signature . the pwc manager sends it to security manager so that it may install the signature into firewalls to block inbound ( or outbound ) malicious messages . also , the signature is propagated to all pwc agents so that they may also install it into embedded packet filters . it helps the agents with reducing the rate of smoking signs , preventing known malicious messages from reaching the pwc agents . it also helps reducing unnecessary propagation of smoking signs . pwc consists of three major phases : smoking sign detection ( section iv - a ), initial containment ( section iv - b , iv - c ), and relaxation ( section iv - d ) phases . in this section , we will illustrate each of them in order . 1 ) smoking signs and active containment : smoking signs require to be raised early , but they are not necessarily required to have an extremely low false - positive rate . this characteristic allows pwc agents to contain earlier while requiring consequent relaxation phases . since , to survive in the wild , the worm must replicate itself to more than two new victims before being contained , the worm naturally sends infectious messages to as many distinct destination addresses as it can . therefore , abnormal growth in the number of distinct destination addresses at infected hosts has been reported in many literatures [ 3 ], [ 26 ], [ 27 ]. for fast worms , even a per - second observation shows this attitude of abnormally growing number of distinct destination addresses . we observe that most of the rates of the connection attempts to distinct destination addresses in a 24 - hour auckland - iv trace appear below 15 per second , and only few of them appear between 20 and 25 per second . in our lab computers traces , the rates of distinct destination addresses appear no more than 5 per second . in contrast , even the codered - i can probe more than a hundred destination addresses per second of which the majority are unique addresses . // pkt : a tcp syn or udp packet to be sent r : = rate of the most recent n elements in outconhist ; handler on_out_connection ( ) shows how pwc agents handle outbound connection attempts to raise smoking signs and initiate active containment ( fig2 a ). on_out_connection_contain ( ) in line 8 is to perform relaxation analysis when the host is contained . on every connection attempt to a new ip address , a pwc agent calculates the rate r based on the most recent n elements in outconhist , the outbound contact history which is a list of the timestamps of recent outbound connection attempts made to new addresses . if r exceeds the threshold λ , the pwc agent raises a smoking sign , initiates active containment on its host , and reports the smoking sign to the pwc manager . 2 ) smoking sign propagation : any smoking sign detected at a host imply the possibility of hidden infectees in the network . to proactively block the hosts that are infected but not detected , the pwc manager shares reported smoking signs with all the agents in the network through the smoking sign propagation . the following message carries the smoking sign reported to the pwc manager : [ t sent + t d + the agent &# 39 ; s ip ]. t d , the detection latency to be used in false containment avoidance , is defined as t sent = t in − t sent is the current time and t in is the timestamp of the latest successful inbound connection made before the n timestamps referenced in calculating r . to prevent possible bandwidth saturation caused by worms from interfering with the smoking sign report , the agent reports the smoking sign after containing its host . the receivers of either a reported or a propagated smoking sign would discard the smoking sign if t sent is too old . to prevent forged smoking sign injection , all the messages between pwc agents and the manager should be authenticated using rsa . to avoid denial - of - service and overwhelming traffic , smoking signs will not be reported to the pwc manager if the time elapsed since the most recently received smoking sign is less than the relaxation analysis duration t relax . the pwc manager also applies similar restriction . therefore , the smoking sign propagation rate is limited to 1 / t relax times per second . 3 ) reducing false smoking signs : to reduce false smoking signs caused by excessive small udp packets to many distinct destinations ( e . g ., p2p file sharing and mdns protocols ), a pwc agent ignores outbound udp packets that are shorter than 200 bytes . please note that the smallest payload length of udp based worms found in symantec &# 39 ; s viruses & amp ; risks search was 376 bytes ( sql slammer ). a propagated smoking sign makes every agent in the network start passive containment , which is shown in fig2 b . on receiving a propagated smoking sign , the agent validates the smoking sign first , which we named false - containment avoidance . note that passive containment initiated by the propagated smoking sign is a proactive action taken on a host that is not suspicious from local pwc agent &# 39 ; s knowledge . therefore , any propagated smoking sign can be ignored if the receiving agent ensures that the local host is not infected . a way to do this is the vulnerability window analysis which yields instant decision at each pwc agent on receiving a propagated smoking sign . the decision results in either of safe and unsafe , where safe means the pwc agent can safely ignore the smoking sign , and unsafe means the agent should not . 1 ) the vulnerability window analysis : consider pwc is fully deployed in an enterprise network . let us assume all the pwc agents configured with the same parameters since , typically with many organizations , most hosts within the same enterprise network would have similar ability to send packets . let us assume that infected host h 1 raises and propagates a smoking sign through the pwc manager . given that h 2 is one of recipients of the propagated smoking sign , let us depict the timeline of the propagation in fig3 where , i . t 1 at h 1 is the time of the last successful inbound connection before releasing the first scan . ii . t 2 at h 1 is the time when ( potentially ) the first scan is released . iii . t 0 at h 1 is the time when a smoking sign is raised . iv . δt is equal to ( t 0 − t 1 ) v . t ′ 0 at h 2 is the time of receiving smoking sign from h 1 vi . t ′ 1 at h 2 is equal to ( t ′ 0 − δt ) vii . t in at h 2 is the time of the last successful inbound connection . let us assume ( a ) h 2 is susceptible to the same worm as h 1 has ; ( b ) h 2 is not contained at t ′ 0 ; ( c ) δt & lt ; t relax ; ( d ) h 1 and h 2 have similar cpu / nic performance , ( a ) and ( b ) are considered to be true , pwc should be configured to hold ( c ), ( d ) is generally true in an enterprise network . we do vulnerability window analysis by testing the following hypothesis : ( e ) the connection attempt made at t in was infectious . the merit of this analysis is that if the hypothesis is proven false , h 2 can safely ignore the smoking sign and avoid containing an innocent host . to see if the hypothesis is false , we assume the hypothesis were true , then we prove by contradiction . to determine whether h 2 needs to be contained or not at time t ′ 0 , we must consider the following cases ( 1 ) and ( 2 ). ( 1 ) t in & lt ; t ′ 1 : if hypothesis ( e ) were true , h 2 should have been infected at t in , and pwc agent at h 2 must have raised a smoking sign within the time window [ t ′ 1 , t ′ 0 ] and become contained . from ( b ), h 2 is not contained at t ′ 0 , thus we can conclude h 2 was not infected at t in . because h 2 has never been connected since t in , h 2 is considered to be safe . ( 2 ) t in & gt ; t ′ 1 : h 2 should be considered to be unsafe , for we cannot reject hypothesis ( e ). lemma 1 : at t ′ 0 , if h 2 receives a propagated smoking sign ( t 0 , t d , h 1 ), h 2 can ignore the smoking sign and skip passive containment if the following assumptions hold : ii . h 2 is susceptible to the same worm as h 1 has . lemma 1 can be extended to handle multiple lands of worms by taking the larger t d when smoking signs report different t d &# 39 ; s . although a worm can evade passive containment by having a delay before starting scanning , the worm cannot successfully spread out since local pwc agent will initiate active containment after monitoring the first n scans . a limitation of vulnerability window analysis is that any inbound connection attempt within the vulnerability window makes the vulnerability window analysis result in unsafe . the result is affected by two factors : first , frequent legitimate inbound connections ; second , large vulnerability window δt . we will introduce two heuristics to address these limitations and will see how often the vulnerability window analysis would raise false positives with selected δt . from the definition of t d in a - 2 ), the largest δt can be approximated as n / λ seconds . 2 ) traffic filter for vulnerability window analysis : to make the vulnerability window analysis resilient to legitimate traffic , we set up two heuristics to sift out meaningful traffic within the vulnerability window . the heuristics are : h1 : reducing multiple inbound connection attempts made within h t seconds by the same source ip address . even an internal worm that scans 8 , 000 destinations per second with 50 percent of local preference in selecting the destinations would take more than 16 seconds to scan entire / 16 local network . therefore , we regard redundant connection attempts from the same ip address incoming within h t seconds as a noise , and reduce them leaving only the first one . h2 : removing inbound udp packets carrying the payloads shorter than h l bytes . pwc uses h l = 200 as we discussed in a - 3 ). we could reduce 96 percent of the legitimate inbound connection attempts appeared in our lab pc traces by h1 ( h t = 10 ) and h2 ( h l = 200 ). in addition , on the same traces , we calculated p [ n = 0 ], the probability that vulnerability window at a certain point of time may not include any legitimate inbound connection attempts . although we do not show the result due to the limited space , p [ n = 0 ] when δt = 0 . 57 seconds was above 95 % and when δt = 1 . 43 seconds was above 90 percent . 1 ) overview on containment : during the period when pwc agents hold containment on their hosts , the hosts are prohibited from initiating connections to other hosts . once initiated , the containment holds until the end of relaxation analysis that has started along with the containment . 2 ) which packets to regulate : during the containment , pwc agents regulate only outbound connection attempts in order to preserve already established sessions . the outbound connection attempts are in three types : ( o1 ) outbound syn packets ; ( o2 ) outbound udp packets ; ( o3 ) inbound synack packets . for o1 and o2 , a pwc agent modifies the ttl value , moderates the rate , and forwards the packet . this is to integrate pwc seamlessly with other network - based signature identification and filtering techniques , which is discussed below . when the pwc agent forwards the packet with a modified ttl value , it buffers the original packet so that it may forward the packet when the containment is relaxed . the buffered connection attempts will be dropped with appropriate handling if the buffer becomes full or if the packets are delayed for longer than predefined timeout ( up to a couple of seconds ). for o3 , we drop them at each pwc agent under containment . the agent who drops an o3 packet must reply to the sender with a forged rst packet with the sequence number of the dropped packet in order to let the sender ( who accepted the connection request ) return to listen state . 3 ) integrating pwc with other techniques : we designed pwc to work with other network - based signature identification and filtering techniques [ 3 ], [ 4 ], [ 5 ], [ 6 ]. during the relaxation phase , pwc agents minimize the affect on other techniques by allowing the packets to be forwarded up to and no further than the signature extractor in fig1 a . when a contained host requests an outbound connection attempt , the pwc agent on the host replaces ttl value of corresponding packets with the number of hops to the border of the network . given the address of border router , the agent can measure exact number of hops to the border router , using the same method as traceroute [ 28 ] does . the signature extractor still see worm scans as if the sources are not contained while the scans from the contained host cannot reach external victims . potential l - l scans are to be dropped by pwc agents during contained period . to prevent very fast scanning worms from causing congestions on internal paths , the rate of the packets forwarded must be limited to a moderate level . during the period when a pwc agent is containing its host , it maintains dst , the number of distinct addresses to which the local host has initiated connection attempts , to see if the host shows sustained rate exceeding λ . we call this analysis relaxation analysis since the goal is to relax contained hosts . relaxation analysis for a containment initiated at time t contain monitors the host for at least t relax seconds . the connection rate r relax updated at the end of the relaxation analysis is defined as where t last — conn is the timestamp of the first outbound connection attempt initiated after t contain + t relax . the containment should be relaxed if r relax is lower than λ . otherwise , the containment should not be relaxed and the relaxation analysis should be performed again . when a pwc agent performs a series of relaxation analyses , r relax is cumulated across consecutive relaxation analyses . by calculating r relax over a series of consecutive relaxation analyses , we can avoid evasion attempts by such worms that periodically scan at a burst rate . f successive failures in relaxing containment will let the host isolated from the network . 1 ) effect on availability : containment during relaxation analysis may reject legitimate outbound connection requests , which causes availability loss . to find good t relax for an acceptable availability loss , we ran simulations ( with no worm ) on the busiest four hours of an auckland - iv trace and calculated length of every containment at every uninfected host participating in the communication . let us denote by φ i ; j the length of the j th containment at host i . φ i is defined to be the sum of all φ i ; j at a given host i , and φ k is to be max ( φ i = k ; j ). p i , the maximum number of relaxation analysis required for host i to be relaxed , is denoted by the number written in superscript is the value given for t relax . fig4 shows the distribution of φ i , which implies smaller t relax reduces the total amount of time spent under containment at each uninfected host . we observed that no more than one phase of the relaxation analysis was required to relax any of the uninfected but ( mistakenly ) contained hosts . for all the t relax &# 39 ; s that we tried , we observed that no outbound connection attempt had been made to a new destination during the contained period . the results empirically show that the relaxation analysis would not bring significant availability loss if t relax is less than a couple of seconds . for the hosts sending out time - critical packets such as streaming media packets over udp , to guarantee acceptable quality of service , we may configure the agents to disable the passive containment . 1 1 please note that the packets transmitted to the same destinations within a certain period are not counted by the smoking sign detector . 2 ) stalled - scan : another problem is stalled - scan . the scanning of certain tcp - based worms that scan victims in a synchronous manner could be delayed during the relaxation analysis . blocking outbound syn and inbound syn - ack packets during containment would let the worm wait until tcp retransmission time - out expires , slowing the scan rate down dramatically . 2 thus , syn - ack packets arriving during containment must be translated to appropriately forged rst packets by agents to let the worm immediately close the connection and try the next victim . since we observed few outbound connection requests were made during containment in simulations based on an enterprise - scale real traces , users would not experience unacceptable connection problems . 2 however , the synchronous scanning is not suitable for implementing tcp counterpart of very fast scanning worms [ 29 ], where proactive response is necessary . symbols and notations used in following sections are described in table ii . we have evaluated cost - effectiveness of pwc using both real world traces and simulation experiments . we have used following three metrics through out the evaluation : the enterprise network simulations run 13 , 000 hosts including 50 percent of vulnerable hosts . local address space for the enterprise network is assumed to be / 16 , and test worms with different scanning behaviors and different scanning rates are tested in the network . for pwc is a host - based unidirectional worm containment approach , we assume no inbound scans from external infectee . also , round - trip - time ( rtt which is typically less than 1 ms ) within the same enterprise network is ignored for brevity . to determine parameters and to render the normal background traffic , we have used a 24 - hour trace of the auckland - iv traces [ 30 ] collected in 2001 . apparently , the traces collected at the border of the university of auckland do not contain local - to - local traffic at all . however , we assume that the omitted traffic would not affect the experiment results since the observation on our own local traffic showed that ( 1 ) h1 and h2 in section iv - b . 3 could remove 96 % of the legitimate inbound connection attempts ; ( 2 ) the outbound connection attempts to local addresses implied high locality of the destination addresses ; and ( 3 ) the burst rate of normal outbound connection attempts did not sustain . in addition , the omitted traffic will also affect existing techniques being compared with our system . we evaluate pwc against two existing techniques , williamson &# 39 ; s virus throttle [ 1 ] and the hamsa [ 6 ] in terms of each metric . since the virus throttle generates false positives on seven hosts in the tested background traffic , we set up another configuration ( wil - 5 - 1500 ) besides the default ( wil - 5 - 100 ). wil - 5 - 1500 was the most conservative configuration among those would not raise false positives on the test traffic . for the hamsa , we deployed it at the border of the enterprise network in the simulator . we assume the hamsa starts generating signatures when the suspicious pool size reaches 500 and the delay for signature extraction is 6 seconds [ 6 ]. three types of test worms include ( t1 ) randomly uniformly scanning worm , ( t2 ) 0 . 3 local preferential scanning worm , and ( t3 ) 0 . 5 local preferential scanning worm . we assumed entire address space to have 232 addresses , and local address space to have 216 addresses . t2 and t3 worms give idea of pwc &# 39 ; s effect on the local preferential scanning worms in real world . for example , the codered - ii worm scans the same / 8 network with 50 % probability and scans the same / 16 network with 37 . 5 % probability . the blaster worm picks the target within local / 16 network with a probability of 40 % and a random ip with 60 % probability . in this section , we first tune two key parameters of pwc . then we compare pwc with virus throttle [ 1 ] and hamsa [ 6 ]. the sign threshold λ and the detection delay n need to be tuned based on the characteristics of normal traffic , and both parameters are critical to the effectiveness of pwc . we tuned pwc using the auckland - iv traces . the criterion that we used for a good λ is nmi , the number of mistakenly isolated hosts . 3 to let the smoking signs be raised for the slower worms , λ should be small . however , the smaller values for λ degrade the accuracy of the smoking signs . to see the correlation between λ and the accuracy , we calculated nmi varying λ and n , running pwc on a 24 - hour long real - traffic trace . given that n = 5 to reduce the effect of the false alarms caused by n , pwc isolated more than 3 naive hosts for the λ smaller than 7 . when the λ was greater than 7 , pwc isolated none of the naive hosts , even if the n was set to 2 for the most aggressive configuration . 3 regarding the isolation , please see fig2 . the first criterion for n is the impact on active containments . repeated active containments resulted by frequent smoking signs will increase availability loss to the hosts . we calculated per - minute rate of active containments caused by false smoking signs at each host , varying tv , running 24 - hour long naive traffic . the values at the hosts ranked within top three in the rate for each n are shown in fig5 a . the second criterion is the impact on the passive containments . at each host , a smaller may increase the chance of passive containments , resulting in an increased availability loss . since the passive containments caused by false smoking signs could be partially solved by the vulnerability window analysis , we calculated the per - host rate of the vulnerability window analysis failures that the vulnerability window analysis mistakenly initiated the passive containment on each naïve host . the per - minute failure rates at selected hosts are shown in fig5 b . the curves where n is within the range [ 4 , 8 ] have shown similar performance . the false smoking signs initiated less than 1 passive containment within 10 minutes for more than 98 % of entire hosts when n = 4 and the same rate for more than 99 % of entire hosts when ¢= 10 . based on the results , we configured n = 4 for the more conservative configuration , and n = 10 for the less conservative yet more accurate configuration . the most significant contribution of pwc is the suppression of the local - to - local worm propagation . we set up 10 hosts initially compromised to stimulate local infection rate . with 10 initially infected hosts , the assumption that 50 % of entire hosts are vulnerable to the worm attack could be an extreme case , however , as shown in fig6 , pwc successfully suppresses local - to - local infections for t2 and t3 worms . fig6 shows that worm scan rate did not affect pwc &# 39 ; s performance . a successful worm containment strategy must minimize the number of scans that escapes the perimeter of defense during the delay when the containment system detects the enemy and prepares its weapon ( i . e ., signatures ). fig7 shows pwc outperforms the virus throttle and the hamsa in terms of m2 , the number of escaped scans . while the virus throttle performs better for the faster worms and the hamsa does for the slower worms , pwc shows consistent performance for all the range of scan rates tested . as the worm scans local address space more aggressively , the performance gap between pwc and other techniques becomes more significant . wil - 5 - 100 seems to perform better than the generous configuration of pwc ( pwc - 7 - 10 ) for the worms whose scan rates are faster than 25 to 50 scans per second . however , wil - 5 - 100 isolated 7 hosts due to the false positives . we observed no naive host had been isolated by pwc during the simulations on m2 . we compared pwc in terms of availability loss that the containments caused by false smoking signs introduced . please note that , in spite of its longer detection delay , the hamsa does not introduce the availability loss . therefore , we evaluated availability loss that pwc introduced , in comparison with the virus throttle only . to compare pwc and the virus throttle fairly based on the same metric , we calculated the average delay per request ( rd ) running both systems on a 24 - hour long naive trace , assuming that the outbound connection requests dropped by the pwc agent at a host would be retransmitted by users when the host would be relaxed . as shown in fig8 , pwc significantly outperformed wil - 5 - 100 and wil - 5 - 1500 in terms of m3 . due to the long delay queue , wil - 5 - 100 and wil - 5 - 1500 delayed outbound connection requests for couples or even tens of seconds in average at several hosts while the maximum rd was 0 . 95 seconds / request for pwc - 7 - 4 and 0 . 5 for pwc - 7 - 10 . the variations were 0 . 0016 and 0 . 0002 for pwc - 7 - 4 and pwc - 7 - 10 respectively . massive emailing viruses and stealthy worms fall outside of pwc &# 39 ; s scope . also , we save authentication and message exchange related issues for future research topics . some of possible attacks to pwc are discussed below . 1 ) attack on the vulnerability window analysis : wait - and - scan attack . after it infects a host , the worm can deliberately have certain amount of delay before sending out the first scan message . then , by the time when the worm starts scanning victims , the record of the message that infected the host can be removed from the vulnerability window , which makes the worm evade the passive containment when a propagated smoking sign is received . however , it will not harm overall performance of pwc system much since the pwc agent at the host will raise a smoking sign and initiate active containment after first n scans are tested . 2 ) attack on the relaxation analysis : scan - and - wait attack . to evade isolation , worms may wait for more than t relax seconds after sending out burst scan messages , before resuming scanning . the blaster is an example . the blaster sends out 20 tcp connection requests , sleeps for 1 . 8 seconds , and then resumes processing with the connections if there are any responses [ 31 ]. in this case , although containment and relaxation will be repeated , the scan rate of the worm will be restricted since pwc agent will allow only n out of the 20 scans to escape . when n is 4 and t relax is 1 , the scan rate will be limited below 4 scans per second which is much lower rate than λ (= 7 scans per second ) used in our evaluation . being independent to worm &# 39 ; 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