Patent Application: US-2272908-A

Abstract:
a system and method is provided to identify udp attacks . a processor determines a spectral density of packet timing intervals , a natural distance between the spectral density and a uniform distribution , and a non - linear amplifier applying a non - linear amplification to the natural distance to detect a denial - of - service attack . it uses the concept of traffic statistics analysis , i . e ., spectral densities of arrived - packet timing intervals , calculates the kl - distance measurement and makes decision based on the output of a non - linear gaussian amplifier , with which one can easily adjust the amplifier via selecting different parameters of mean and variance to satisfy system requirements of false - positive and false - negative udp attack detections .

Description:
in recent years ddos attacks have become one of the most serious security threats to the internet . in a typical ddos attack , attackers compromise multiple machines and use them to send large numbers of packets to a single victim server to overwhelm its capacity . a general networking approach to mitigate ddos attacks is to identify and rate - limit attack traffic , preferably at points as close to sources as possible , in order to reduce the collateral damage . however , identifying attack traffic is generally difficult because attackers can manipulate their traffic and packets to defeat detection . network traffic has a “ self - similar ” nature which manifests its presence through a number of equivalent behaviors , namely : ( i ) autocorrelation function decaying hyperbolically fast ( identified as long - range dependence ); ( ii ) spectral density function obeying a power law near the origin , i . e ., s ( f )= c · f − a ; 0 & lt ; a & lt ; 1 ; ( iii ) variance of the sample mean over limited samples decreasing more slowly than the reciprocal of the sample size . normal udp traffic incorporates a heavy tail probability density function ( pdf ) for the inter - arrival times and a self - similar behavior of the traffic intensity process . specifically , packet inter - arrival times are described by marginal distributions with heavier tail than that of the exponential . this delicate difference results in distinguishing the traditional udp attacks from legitimate udp flows . packet jitter spectral density metric ( pjsdm ) is provided to mitigate the udp attacks . pjsdm provides an efficient and effective approach based on general properties of network packet traffic , which can be verified by different analysis techniques using , for instance , wavelets and power spectral density analysis . those studies are identified below as references [ 5 ], [ 6 ], [ 12 ] and [ 13 ]. the majority of traffic analysis studies have focused on the typical , packet level and end - to - end behavior ( a notable exception being reference [ 7 ]). the focus of the present invention is mainly at the flow level , rather than the packet level , and on identifying frequency characteristics of anomalous network traffic . the combined packet arrival stream generated by all sources is considered , rather than focusing on the subset of packets generated by a single source . because of the highly multiplexed internet core , such primary performance metrics as packet delays and buffer occupancies should be insensitive to the details of an individual flow . as illustrated in fig1 , the detector of the present invention primarily has three techniques : ( i ) compute the spectral density of packet timing intervals , ( ii ) calculate the kullback - leibler distance ( kl - distance ) [ 1 ] between the spectral density and a uniform distribution , and ( iii ) go through a non - linear gaussian amplifier . the output of the pjsdm is compared with a threshed to make decisions . thus , the present invention provides a unique solution and significantly improves the udp flood attack detection performance , especially in the case of single directional udp traffic . in addition , it provides automated analysis of the flow characteristics , and the outputs of the pjsdm give network administrators &# 39 ; insight to malicious activities passing through their networks as well . the invention considers a random process , { x ( t ): x 1 , x 2 , . . . , x n }, where n is the number of considered packets and x ( t ) represents the set of packet inter - arrival times for a udp flow . for ease of description and without intending to limit the scope of the invention , this random process is generally referred to here as the packet jitter process . to study the periodicity embedded in the packet jitter process , the spectral density is calculated , which is the output of the discrete fourier transform ( dft ) applying on it , which is shown in equation ( 1 ). in equation ( 1 ), k denotes the k - th index of the spectral density ( k = 1 , 2 , . . . , m ), and m is the size of spectrum considerations . in practice , the packet jitter process can be sampled in periodical senses . for example , the invention can obtain a relative timing record every 10 packets . a recursive formula is used to efficiently calculate the dft , as shown in equation ( 2 ) by using equation ( 2 ), the network traffic is monitored on line so that the packet jitter spectral density is efficiently obtained at every time step . as a result , the spectral density of general udp flood attacks is approximately either uniformly distributed or with a high spike at the first index ( low frequency area ), which can be observed for instance through the use of computer simulations . therefore , the invention quantifies udp flow characteristics changes in terms of the kl - distance between the measured spectral densities and uniformly distributed values . kl - distance is a natural distance function from one probability distribution , to another probability distribution . it is referred to as relative entropy in information theory . for discrete probability distributions , p ={ p 1 , . . . , p m } and q ={ q 1 , . . . , q m }, the kl - distance is shown in equation ( 3 ). applying it to measure the distance of the spectral density away from a uniform distribution , normalization processing is performed on the magnitude of x ( k ), such as where h ( p ) denotes the density entropy . since the first item at the right side of equation ( 5 ) is a constant , it shows that the entropy measurement is equivalent to kl - distance in this particular application . in accordance with the invention , the packet jitter spectral density function of general udp flood attacks obeys a power law near the origin , i . e ., x ( f )= c · f − a ; 0 & lt ; a & lt ; 1 . 0 , and the normal udp traffic is however approximately with higher α . for 10 spectral indexes only , the corresponding packet jitter spectral densities are plotted in fig2 . fig3 displays the kl - distance with respective to the parameter α . on the other hand , udp attacks are very short packet size ( say less than 10 bytes ) however with huge packet rates . in this case , the packet arrivals appear as a poisson distribution , see reference [ 15 ], and the packet inter - arrival times follow an exponential distribution . due to lack of periodicity , the packet jitter spectral density exhibits tiny and evenly spread across all frequencies except the first index , i . e ., [ 1 δ ⁢ ⁢ … ⁢ ⁢ δ ︸ m - 1 ] , δ ⁢ ⁢ 1 . to extract this extreme case , it is determined how much the kl - distance is affected by this distribution . particularly , a lower bound of the kl - distance is given by in a uniform distribution , it is { q i = 1 / m , ∀}, and the normalized packet jitter spectral density may be expressed by hence the right side of equation ( 6 ) can be calculated as with different values of m and δ , the lower bounds of the kl - distance have been plotted in fig4 . together with fig3 , these properties are useful to set up the decision thresholds . as m is to infinite and δ → 0 , then the asymptotical value is 0 . 7213 . therefore , two thresholds in the kl - distance measurement are set up such that either kl ( p , q )& gt ; 0 . 7213 or kl ( p , q )≦ 0 . 1341 . these thresholds define udp attacks , since that of the normal udp traffic is empirically in the between , i . e ., 0 . 1341 & lt ; kl ( p , q )≦ 0 . 7213 . however , in order to provide a robust detector , instead of using the hard decision with two thresholds , the result is passed to a non - linear amplifier so that one threshold may be sufficient to make decisions . the kl - distance measurement is either zero or log m based on equation ( 5 ). for normal udp traffic it is approximately around the median therefore , we can use a non - linear gaussian amplifier , i . e ., n ( μ , σ 2 ), to suppress both side values ( small / large ) as to amplify center values of the distance measurements . technically , the non - linear gaussian amplifier is just one example . any one with this property would be selected as the non - linear amplifier . in the non - linear gaussian amplifier , we select there are two parameters ( σ , β ), which can be used to adjust the amplifier . the amplified distance measurement is termed as the pjsdm . the pjsdm can efficiently and effectively detect general udp flood attacks . fig5 illustrates examples using the non - linear gaussian amplifiers . if selecting the decision threshold equals 0 . 4 , it can be observed that for the case of σ = 0 . 25 , the decision is equivalent to the hard decision of 0 . 1341 & lt ; kl ( p , q )≦ 0 . 7213 mentioned previously for normal udp traffic . by decreasing the parameter σ to 0 . 10 , the good traffic range will be shrunk into [ 0 . 27 ˜ 0 . 57 ]. by doing that , it can be expected that the false - positive probability will be increased and the false - negative probability will be decreased . hence , selecting σ and β , together with the threshold ( τ 0 ), can build the udp attack detector satisfying system requirements of false - positive and false - negative attack detections in practice . due to the fact that manipulated udp attacks may mimic normal traffic statistics , it is highly possible that the kl - distance measurements of udp attacks also appear in the range of [ 0 . 1341 0 . 7213 ]. hence , the threshold decision may not be effective for all network scenarios . however , the primary problem can be cast as a hypothesis - testing problem , in which it is assumed that there are two possible hypotheses , i . e ., h 0 and h 1 , corresponding to two possible states of normal and under - attack . fig6 illustrates this concept by assuming uniform probability distributions . within the binary hypothesis testing , there are observations of the kl - distance measurements and a decision is made between the normal and under - attack . there are a number of possible decision strategies or rules that could be applied in this model , and the optimal decision rule could be found in some senses . for illustrations , as in the fig6 , if an observation belongs to the area between a and b , keep monitoring and no conclusion will be derived ; otherwise , the flag of under - attack will be trigged since it is never supposed in the blue areas for good udp traffic . the following example is provided to illustrate the invention without intending to limit the scope of the invention . first , unidirectional legitimate udp and attack data sets are presented . the specific parameters are then selected for the design variables of the pjsdm scheme . then the results are displayed in real network scenarios . audio / video broadcasting is the typical feature for unidirectional udp applications . mainly , three software packages may use udp flows for broadcastings in internet : ( 1 ) microsoft real producer ; ( 2 ) vlc media player ; and ( 3 ) quick timer ( qt ) broadcasting . in the present example , a single hosted network with a fast upstream link is monitored . some information of interest is that this link keeps a sustained rate of 100 mbps with peaks higher than 300 mbps and contains a rich network traffic mix carrying both standard network services like web traffic , peer - to - peer application traffic , as well as streaming audio and video traffic . one windows pc ( ipb ) was located inside a company lan network with 10 mbps link whereas the linux pc ( ipa ) was outside the lan coming directly from its isp . it is treated as a legitimate user when ipa broadcasts normal udp packets , and as an attacker if the ipa performs udp attacks . a popular network protocol analyzer , wireshark , can be used to dump traffic . large different scenarios using the three considered software packages can be utilized to identify that the kl - distance measurements for this illustration are in the range of [ 0 . 2 0 . 8 ] with probability one , including audio / video signals such as mp3 music , movies and etc . as examples , fig7 - 9 display the probability distribution densities of kl - distance measurements in typical movie broadcast scenarios with the three tools respectively . on the other hand , the udp attacks can be simulated by two typical udp flood tools : ( 1 ) jhu - scripts ( generated from john hopkins university ); and ( 2 ) ixia udp attacks . the first one is simply a linux script with a non - exit loop to keep sending udp packets to an ip address with fixed / random port number and data contains . the second one is from an ip performance testing equipment , reference [ 20 ], which can generate most ip traffic with different parameter settings . fig1 shows the probability distribution density of kl - distance measurements using the jhu - script . there are about 5 % probabilities in the range of [ 0 . 1341 0 . 7213 ], and more than 55 % probabilities under 0 . 1341 . fig1 presents the probability distribution density of kl - distance measurements using the ixia generator with all possible parameter settings of fixed - length 64 bytes , uniform distribution 64 - 1518 bytes , increment 1 - 1518 bytes and auto - length . there are over 85 % probabilities larger than 0 . 7213 , and less than 1 % probabilities under 0 . 1341 . moreover , the jhu - script is modified by introducing random timing delays and random packet lengths , so as to mimic the normal udp traffic . as plotted in fig1 , the probability distribution density of kl - distance measurements will occur at the normal range of [ 0 . 1341 0 . 7213 ] with about 50 % probabilities . based on the statistics of legitimate and attack udp flows , a binary hypotheses testing model will be needed . as illustrated in fig6 and considering 0 . 05 allowance , the specific parameters that are chosen for the design variables of this pjsdm scheme are thresholds of a = 0 . 1341 , b = 0 . 7713 . within the binary hypothesis testing , there are observations of the kl - distance measurements and a decision is made between the normal and under - attack . if a kl - distance measurement belongs to the range of [ 0 . 1341 0 . 7713 ], keep monitoring and no conclusion will be derived ; otherwise , the flag of under - attack will be trigged since it is never supposed in the blue areas for good udp traffic . with the help of computer simulations and practical implementations , the performance is evaluated and applied to real network scenarios . the correct detection probabilities of udp attacks are higher than 98 % and false negative probabilities are less than 2 %. accordingly , a novel udp flood detector is provided , termed as pjsdm , which consists of three techniques of computing the spectral density of packet timing intervals , calculating the kl - distance between the spectral density and a uniform distribution , and going through a non - linear gaussian amplifier . in comparison with traditional approaches , this invention provides a unique solution and significantly improves the performance of udp flood attack detection , especially in the case of single directional udp traffic . the binary hypotheses testing model is used for robust detections . referring to fig1 , a communication network is shown having isp peering points , routers , switches and various servers . the routers , switches and servers include processors and software to perform its operations . in addition , the detector of the present invention is shown located between the peering points and the routers to block attack traffic . the detectors include a processor or controller and software that operates the processor to perform the operation of the detection in accordance with the invention . the detectors may also have memory and other elements to facilitate operation , as well as user input devices to allow user control . the detectors are preferably deployed in - line at the perimeter of the network as a border defense against attacks . located at the edge of the network , this first line of defense keeps attack traffic out of the hosting data center network . when deployed in this fashion , the detectors automatically identify and block ddos attack traffic . network personnel can monitor our actions and results but they do not need to take any actions to identify or block a ddos attack . in addition , the system produces zero false positives so that it does not block valid customer traffic . it also does not require a blacklist to operate . therefore , a user &# 39 ; s customer can still communicate with the user even when the detector is blocking attack traffic from the same computer . by sitting on the perimeter of the network , attack traffic is blocked from entering any part of the user &# 39 ; s network that sits behind the detector . thus , the detector removes the ddos attack traffic before it enters the network so that existing network defenses can operate . by eliminating the flood of attack traffic , defense resources can fully focus on intercepting invasive probes that are often imbedded in , or which immediately follow , a ddos attack . m . kalantari and m . shayman , “ quantifying responsiveness of tcp aggregates by using direct sequence spread spectrum cdma and its application in congestion control ”, ieee globecom , dallas , tex ., november 2004 . 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