Abstract:
The method and apparatus of the present invention provide real time detection of abnormal data streams in high bandwidth data pipes, such as those found at network service provider data hubs. Abnormally high data volumes, for example, those associated with Denial of Service [DOS] attacks, may be detected and a mitigating response to the attack initiated. Further, certain counterattack steps are instituted including reverse tracing to determine the source of the attack and attack signature recording for later comparison to suspected new attacks. The method and apparatus of the present invention are such that the entire volume of data passing through a high bandwidth data pipe may be monitored on a real time basis providing early warning of DOS attacks for very large network address spaces.

Description:
BRIEF DESCRIPTION 
   The subject of this invention relates to the data communications industry. Specifically, this invention describes a method and apparatus for determining the presence of abnormal data streams in high bandwidth data pipes and subsequent response decisions to mitigate the impact of the abnormal streams. 
   BACKGROUND OF THE INVENTION 
   The use of data communications networks has increased dramatically over the past ten years, driven by both technical progress, such as ease of use and economical access, and by necessity, for example the global business environment. Generally this progress has been good due to gains in efficiency and timeliness of information, resulting in more accurate data and hence better decision making. But as the use of networks increases by both private and public entities, so does the dependency upon the data carried over the networks. Attendant with the increased dependency is an increased vulnerability to attack by persons bent on mischief, for whatever reason. 
   While there has been an increase in the positive effects of wide-spread network use, so too there has been an increase in the negative effects. Specifically, the incidences of attacks by intruders, or so-called hackers, has seen dramatic increases, causing major network crashes such as those seen by Yahoo and eBay in late 1999. One such variety of attack, a Denial Of Service [DOS], also known generally as a “flood,” effectively swamps the target network with so many requests for service that no response is possible, thereby debilitating a family of routers and possibly, in theory at least, an entire network. This is an extremely difficult attack to prevent for a variety of reasons, thus there exists a need to mitigate. 
   But mitigation is difficult because modern network architectures are susceptible to hostile attack. Reasons for this susceptibility are, among others, the global nature of the threat including both recreational and terrorist attackers, the multi-platform/multi-protocol nature of the networks involved, and the constant change taking place in the network community. As noted at the CERT® Distributed-Systems Intruder Workshop, “Intruders are actively developing distributed tools . . . ” making attacks easier, in part “ . . . because of the large number of machines ‘available for public use.’” [Results of the Distributed-Systems Intruder Tools Workshop, Pittsburgh, Pa., Nov. 2–4 1999, p. 3]. Public use machines could be, for example, those located in libraries or academic computer labs and accessible to the general public. Such machines can be made the unwitting accomplices in a DOS attack, yielding a multiplier effect focused on the target server, router or network. 
   Attacks are made easier as well because it is difficult to separate legitimate traffic patterns from hostile patterns. Generally, network traffic may be separated into three broad categories: known good, known bad and questionable. Tools are prevalent which allow the determination of which category a specific data stream falls into, but each interferes with the data flow to one extent or another. Where the data flows are very high volume, as is the case in the emerging fiber optic network data pipes, this interference could become a burden on the system performance. Some current methods include serial data stream filtering, encryption, data stream sampling and data stream throttling. 
   By far the most widely used current method is serial filtering where all ingress data is sent through the filter and checked for known bad patterns. Encryption uses a key that is passed from client to server in order to validate the data. Sampling techniques look at random data streams over varying periods of time to recognize normal patterns. Throttling techniques involve reducing the amount of traffic allowed across the network in response to abnormal volume. Each of these methods, however, suffer from deleterious effects on the performance of the network ranging from mild to severe, depending upon the level of validation sought. 
   Further complicating the security problem is, that although intruder methods are well understood by those of skill in the art, an attack is difficult to detect until well after it is under way. Add to this the forging of IP addresses, or spoofing as it is called, the category of a particular data stream can be extremely difficult to determine in real time. All of the above mentioned methods suffer from this inability to rapidly detect an attack versus a legitimate variation in a data stream. While filtering methods may guarantee the validity of all data in a stream, it does so by severely limiting the amount of traffic that may pass. The same may be said about encryption and throttling to one extent or another. Sampling methods suffer from the inability to monitor the entire IP address space of a network in real time, thereby potentially missing the onset of an attack. 
   The present invention significantly advances the art through the ability to detect and react to certain types of attacks while they are commencing and to do so in the entire address space of a network. These and other advantages of the present invention are discussed in detail below in conjunction with the figures attached. 
   SUMMARY OF THE INVENTION 
   The method and apparatus of the present invention provide real time detection of abnormal data streams in high bandwidth data pipes, such as those found at network service provider data hubs. Abnormally high data volumes, for example, those associated with Denial of Service [DOS] attacks (e.g. request for service floods), may be detected at a very early stage and a mitigating response to the attack initiated. Further, certain counterattack steps are instituted including reverse tracing to determine the source of the attack and attack signature recording for later comparison to suspected new attacks. The method and apparatus of the present invention are such that the entire volume of data passing through a high bandwidth data pipe may be monitored on a real time basis providing early warning of DOS attacks for very large network address spaces. 
   The method of the present invention operates by continuously sampling the totality of data traffic in a high bandwidth data pipe in parallel with the normal operations of networks. Thus a Packet Activity Detector/Analyzer [PADA] looks at all packets traversing the data pipe while the routing and switching activity normally associated with network service continues uninterrupted. 
   The PADA steps through each IP address being serviced by the data pipe it is sampling comparing current packet volume with a dynamically updated reference volume. If the volume sampled is within tolerance no further action is taken with respect to the particular address being sampled. If the volume is outside of the reference tolerance, a second and then third review of the suspect data activity may be performed. The second review validates the packet format and the third review, if necessary, validates the packet content. As will be described in detail below, this three tiered analysis and comparison to reference data may be used to identify the commencement of a DOS attack at a very early stage. 
   Once an attack has been detected and/or confirmed several actions are taken including notification of an attack, tracing, and “fingerprinting” of the attack signature. A further advantage of the method of the present invention is the ability to handle an attack while continuing to monitor other normal traffic in the data pipe. 
   Since the traffic for a given IP address may legitimately demonstrate significant changes in volume, the method of the present invention provides a dynamic reference data update to accommodate such traffic pattern shifts. Thus yet a further advantage of the present invention is its ability to act as an advanced traffic profiler, allowing on-the-fly adjustments without triggering false attack reactions. This is accomplished by setting a normal volumetric tolerance, then comparing current volume to the reference over time. An attack will demonstrate a severe increase in volume in a very short period of time whereas a legitimate increase will exhibit a ramp characteristic. This difference permits the method of the present invention to discriminate between an attack and a legitimate upward shift in traffic volume. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a high level block diagram of a system according to an embodiment of the present invention; 
       FIG. 2  is a block diagram of a Packet Activity Detector/Analyzer [PADA] apparatus according to an embodiment of the present invention; 
       FIG. 3  is a schematic diagram of typical high bandwidth data pipes and placement of related PADAs that can make use of the method of the present invention; 
       FIG. 4  is a plot illustrating a typical data abnormality that can be detected through the use of the method of the present invention; 
       FIG. 5  is a plot of the sample rate timing for the parallel sampling of a high speed data pipe used in an embodiment of the present invention; 
       FIG. 6  is a top level state diagram illustrating an embodiment of the present invention; 
       FIG. 7  is a state diagram illustrating the Threshold Comparison and Attack Detection states according to an embodiment of the present invention; 
       FIG. 8  is a state diagram illustrating the Attack Management state of an embodiment of the present invention; and 
       FIG. 9  is a state diagram illustrating the Reference Generation state according to an embodiment of the present invention. 
   

   DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
   The present invention will be more clearly understood through a brief discussion of the environment in which the invention operates. Note that for the detailed discussion that follows the term ‘data pipe’ means a network segment terminating at a router. 
   Referring to  FIG. 1 , a high level block diagram of a system  100  that can make use of the present invention is shown. The system comprises one or more hosting sites  200  and  200   a  connected to one or more data pipes  120  and  120   a . Note that a specific site may contain one or more routers which may service one or more data pipes, and any specific router may be connected to one or more data pipes. The exact configuration does not affect the method of the present invention, thus the configuration shown in  FIG. 1  is exemplary only. 
   Referring now to hosting site  200 , a router  210  is connected to the Internet  110  via data pipe  120  in a manner well understood by those of skill in the art. Also connected to the Internet  110  is Packet Activity Detector/Analyzer [PADA]  300 . PADA  300  is preferably connected in parallel to the router  210  to process data streams in the conventional way through router  210  while accomplishing attack detection on PADA  300  simultaneously. Router  210  provides IP address to the PADA  300  and switches  230  in the customary way, thus will not be discussed in any further detail. However, PADA  300  has the ability to query router  210  over path  305 . This path is used for known protocol analysis purposes. 
   Note that in the embodiment of the present invention shown in  FIG. 1 , the PADA  300  and the router  210  are separate physical machines. It is possible, however, to combine the PADA  300  and the router  210  in the same physical machine. Thus the fact that they are shown separately is not a limitation on the scope of the invention and is done for clarity. 
   Router  210   a  and switches  230   a  located at hosting site  200   a  perform the same functions in the same way as their counterparts at hosting site  200 . PADA  300   a  is attached in parallel with router  210   a . In order to accommodate the high volume of data traffic being transported on the data pipes  120  and  120   a , each router will have its own PADA operating in parallel monitoring the IP address space serviced by that router. 
   Node Decision Server  150  provides route optimization data via data path  155  to all servers on the network. The purpose of Node Decision Server  150  is, among other functions, to provide route outage data to the PADAs to help identify reasons for sudden increases in traffic. For example, Node Decision Server  150  keeps an updated database of all network route segments available for handling traffic. If one of these segments normally served by router  210   a  is dropped, for example by an equipment failure, the excess traffic might be diverted to router  210 . Absent the data from the Node Decision Server  150 , PADA  300  might react to the increased traffic as if it were an attack. However, with the added data from Node Decision Server  150 , the PADA  300  will interpret the increase as legitimate and take no action. As well as route optimization data, the Node Decision Server  150  has the ability to query the router  210  via data path  155  for purposes of known protocol analysis in the same manner as described for the PADA  300  above. 
   Operation of the present invention proceeds through five general states, each discussed in detail below. By way of introduction, however, the present invention processes the totality of the address space serviced by its associated router in a maximum of three discreet polling cycles. Whether all three cycles are executed depends on the result of the previous cycle. As described in detail below, the three cycles are, in order, packet volume check, packet validation check and packet content check. 
   Packet volume check operates by an IP address pointer counter stepping serially through each address served by the associated router. If a packet volume check appears too high, the second cycle is initiated, and, in parallel, the next IP address is checked for volume. In this way, a continuous check of each IP address serviced by a given router may be made in real time. 
   By way of an example, consider a fiber optic data pipe capable of a 10 gigabyte egress data rate. To step through each possible address in the space a maximum of 10 minutes would be required using currently available commercial equipment. The reaction time of the PADA to an out-of-tolerance reading is less than three polling cycles, or roughly 18 seconds in a preferred embodiment. Thus every ten minutes and eighteen seconds the entire IP address space for the router is checked for an out-of-tolerance volume condition. In addition, the exact volume for each specific IP address is used to update a volume reference data base, thus normal periodic data stream fluctuations are accommodated without issuing false positive out-of-tolerance alarms. 
   If an out-of-tolerance volume condition is detected, the IP address involved is then subjected to the second tier review. In this polling cycle, the format of the data packets is reviewed. Should the packets contain invalid data, the third tier is entered where the data contained in the packet is validated. In this third tier cycle the data are compared to historically known bad data signature to attempt a match and subsequent identification of a known attack signature. If the identification is made then immediate remedial action is taken. 
   Supposing that the second and third tier reviews show no invalid packet formatting or no known bad data signature, and supposing further that the increased volume persists, alternative reasons for the increase are examined. For example, router outages or a new server being brought on line might cause a short term increase in traffic for a specific IP address. If the traffic appears to be legitimate, the volume reference data base is updated and the system returns to standard polling operations. 
   Referring now to  FIG. 2 , a block diagram of a PADA  300  is shown. Input Buffer  310  is connected to the high volume data pipe and receives all packets being transported on the pipe. The buffered input data stream is passed to the Activity Comparator  340 , containing the Threshold Monitor  320  and the Pattern Generator  330 . The Pattern Generator  330  is used to provide input to the Pattern Comparator  345  in order to determine if a known attack signature is present while the Threshold Monitor  320  is used to generate the data packet volume for the current IP address. 
   The Threshold Monitor  320  passes its output to the Threshold Comparator  343 . Also appearing as input to the Threshold Comparator  343  is data from the Threshold Reference  325 . Each time the IP address counter is stepped, the volume reference data for that specific IP address is placed at the input to the Threshold Comparator  343 . Output from the Threshold Comparator  343  is passed to the Activity Analyzer  350 . 
   The Activity Analyzer  350  determines if the current data stream volume for the current IP address represents legitimate traffic using volume tolerance, route data, and other management data supplied to it by the Command and Control block  315 . The output of the Activity Analyzer  350  passes to a Signal Shunt  360 . If the volume has been determined to be illegitimate, the shunt dumps the data stream into a bit bucket for further analysis. If the data traffic is legitimate, the shunt simply passes the data stream to the Output Buffer  370  for normal processing. 
   Command and Control  315  provides the necessary administrative functions normal to computing devices, and will be well understood by those of skill in the art. Of importance within the Command and Control  315  is the Memory  318  containing the machine code representing the instruction necessary to implement the state machine of  FIG. 6 . As illustrated in  FIG. 2 , the Memory  318  contains a Nominal Activity State Machine  500 , a Reference Generation State Machine  600 , a Threshold Comparison State Machine  700 , an Attack Detection State Machine, and an Attack Management State Machine  900 . Each of these state machines will be discussed in detail below. 
     FIG. 3  illustrates two typical high volume Data Pipes  1000  and  1000   a  of the type described above. Each of the data pipes has a PADA ( 300  and  300   a  (ref)) attached to it, and as described above, each will also be associated with a router which is not shown for clarity. Recall that the PADAs, preferably connected in parallel with their associated routers, receive all data packets being transported by their respective pipes. 
   Looking more closely at Data Pipe  1000 , there exists at one end IP SOURCE    1010  comprised of IP CLIENT1    1100  through IP CLIENTn    1200 . Each IP CLIENT  represents a specific source IP address in the totality of IP source address space serviced by the Data Pipe  1000 . At the other end of Data Pipe  1000  there exists IP DEST    1020  comprised of IP SRVR1    1500  through IP SRVRn    1600 . Each IP SRVR  represents a specific destination IP address in the totality of IP destination address space serviced by the Data Pipe  1000 . 
   As is understood by those of skill in the art, typically an IP CLIENT  will request service from an IP DEST , and a data communication path will be established and maintained until the request is terminated. In a DOS attack, more than one IP CLIENT  will request service from the same IP DEST , or in other words, flood the server with requests for service, causing a data stream capacity overload to occur. More sophisticated floods using spoofed IP addresses to multiply the number of IP CLIENT  requests can clog even the broadest band data pipes. 
     FIG. 4  illustrates a typical periodic data stream  2000  volume over time. For example, the data volume at 12:00 AM is low, decreasing to a minimum in the early morning hours. However, at noon, or 12:00 PM, data volume reaches a mid-day peak. Shorter duration peaks and valleys may occur, such as depicted at  2200 . These short term data stream anomalies typically vary less than +/−5% over a ten minute sampling period. Using the three tiered polling cycle described above, the PADA will allow these aberrations to pass unencumbered. Also shown in  FIG. 4  is an out-of-tolerance data stream anomaly  2300 . Here the volume aberration is well outside the tolerance band, and the attack slope so severe as to cause the PADA to treat the data stream anomaly  2300  as a possible attack. As will be discussed below, the PADA reaction takes different paths depending upon the amplitude and duration of the data stream aberration. 
     FIG. 5  defines the time relationship  3000  between the three polling cycles described earlier. The time t 0    3100  is the start of a polling cycle for a specific IP address. The time t Pcnt    3200  is the point at which the packet count has been completed. The time t Pval    3300  is the point at which packet format validation has been completed, and the time t Cval    3400  is the time at which the packet content has been validated. As seen, each succeeding polling cycle is an order of magnitude greater in length than the preceding polling cycle. Also recall that when the time t Pcnt    3200  expires for the current IP address, the time t 0    3100  for the next IP address begins. For the preferred embodiment of the present invention time x  3250  is 0.16 seconds, time 10+  3350  is 1.6 seconds, and time 100×  3450  is 16 seconds. Including processor overhead, the total polling time for all three cycles is approximately 18 seconds. 
   The remaining figures,  FIGS. 6 through 9 , provide the details of the five operational states of the method of the present invention. Beginning with  FIG. 6 , the overall state diagram  400  shows a Nominal Activity State  500 , a Reference Generation State  600 , a Threshold Comparison State  700 , an Attack Detection State  800 , and an Attack Management State  900 . 
   In the Nominal Activity State  500  three main activities are accomplished. First, the next polling cycle is started at  510 , and the Reference Generation State  600  is entered. Then at  520  the next data pipe IP address is fetched in preparation for threshold measurement in the Threshold Comparison State  700 . The last activity accomplished in this state is the update of any displays being monitored by human operators at Update Display  530 . This nominal activity cycle continues unless and until inputs are received indicating some unusual event or activity have been detected. 
   The Reference Generation State  600 , discussed in greater detail below, is used to dynamically compute any changes needed to the reference data stream volume used by the Threshold Comparator ( 343  of  FIG. 2 ) and then updates the reference data at  610 . The Threshold Comparison State  700  provides two outputs: a Potential Attack Identified signal at  720  and an Increment Data Pipe IP address pointer at  710 . Should a Potential Attack Identified signal be generated as at  720 , the Attack Detection State  800  either deliverers an Activity is Legitimate signal at  810  or an Attack Confirmed signal as at  820 . The Attack Management State  900  accomplishes actions necessary to mitigate the attack and, when over, delivers an Attack Resolved signal  910  to the Nominal Activity State  500 . 
   Turning now to  FIG. 7 , the Threshold Comparison State  700  and Attack Detection State  800 , are shown in detail. Looking at the Threshold Comparison State  700 , when a Fetch Next Data Pipe IP Address  520  is received from the Nominal Activity State  500  the current data pipe IP address packet volume is sampled at  715 . The ˜0.16 second sample time of the packet volume polling cycle is sufficient to provide an accurate reading of the total data stream volume for that IP address. The packet volume reference data for the current IP address is fetched as at  718  and at  720  it is compared to the sampled volume. If the packet volume is within tolerance the OK path at  725  is taken, the Data Pipe Address Pointer is incremented at  710  and the Nominal Activity State  500  reentered. If the packet volume is outside of tolerance, the Potential Attack Identified signal is given at  730  and the Attack Detection State  800  entered. 
   When the Potential Attack Identified signal  730  is given, it is not necessarily true that an attack is under way, but in order to free up the Nominal Activity and Threshold Comparison States ( 500  and  700  respectively) to continue to look at other IP addresses within the data pipe address space, the determination as to the nature of the out-of-tolerance condition is passed to the Attack Detection State  800 . In this state the Node Decision Server [NDS] is queried at  820  to determine if an outage or route segment disturbance has created a spike in the affected data pipe IP data stream. If the answer is yes, then the data volume perturbation can be explained as legitimate, thus the Activity is Legitimate is given at  810 , the Threshold Reference updated at  815  and the Threshold Comparison reentered. 
   Assuming for the moment that no route segment problems have been identified from the NDS, the packet format is checked at Validate Packet Format  830 . If the format of the packets is proper, the OK is sent at  835  and the process returns, waiting for the next sample cycle approximately 10 minutes in the future. If the packet format was found to be invalid, the Validate Packet Content step  840  occurs. Should the content of the packet be found to be valid, the data is still assumed to be valid and the OK at  845  is sent. As with the Validate Packet Format step  830 , the data stream is allowed to continue until the next cycle approximately 10 minutes in the future. 
   When that next cycle occurs, and finding that the volume is still out of tolerance and/or the packet format continues to be proper, the packet contents are reviewed at Validate Packet Content  840 . If the content of packets is invalid the Query Pattern History step at  850  is executed and a check made for known attack signatures at Check for Known Attack Patterns  855 . If a known pattern is matched, an identified attack is under way, the Pattern Match Verified is given at  860  and the Attack Management State ( 900  of  FIG. 6 ) is entered. 
   If no match was found at Check for Known Attack Patterns  855  but the aberration continues, a manual intervention occurs by a human operator. A Throttle Control Calculation is made at  870  and a Throttle-back signal sent at  875 . Note that while the preferred embodiment of the present invention utilizes human intervention for the throttle-back calculation, it should be recognized by those of skill in the art that this calculation could be reduced to machine executable instructions and thereby automated, thus this manual intervention is not meant as a limitation on the scope of the invention. 
   Turning now to  FIG. 8 , and recalling from above that a Pattern Match Verified signal occurred at  860  (shown here for reference only), the Attack Management State  900 , which is part of the overall state machine  400 , is shown. Since a verified attack is occurring a Send System Alarm occurs at  920 , followed by another query of the NDS at Query NDS  925 . This additional NDS query cycle is accomplished both to assist in the tracing of the intruder and to confirm currently available routes and/or route outages that may be used for alternate service delivery. At this point, and again since a confirmed attack is under way, an automatic throttle-back is accomplished at Throttle Decision  930  in order to regulate the traffic volume in the affected data pipe, thereby preventing the type of DOS congestion sought by the intruder. If the data stream representing the DOS attack is coming from a spoofed IP address, as can be known through methods well understood by those of skill in the art, the data are eliminated from delivery to the targeted server at Dump Spoofed Data Stream  935 . 
   The offending IP address that was eliminated from the delivery to the targeted server at Dump Spoofed Data Stream  935  is redirected to an intruder detection system at Divert to Intruder Det System  940 . A number of such systems are commercially available, for example, the Cisco Secure IDS, manufactured by Cisco Systems, of San Jose, Calif. These systems assist in the rapid tracing of forged IP addresses and corrupted data analysis. The output of the intruder detection system is used to update the data bases at Pattern &amp; IP Addr Log  945 . By updating the data with each new occurrence of an attack, a library of known attacks and related addresses is built which further improves the ability of the present invention to detect future attacks. Tracing is attempted at Attempt Tracing  948 , however as the sophistication of the intruder population increases, tracing becomes more and more difficult. This is so due to the forging of IP addresses and the use of many rouge machines, in many cases unwittingly, to make service requests simultaneously. This multiplier effect makes tracing of the true source of the attack quite a difficult proposition. 
   At this stage of a DOS attack the method of the present invention enters a monitor-and-react mode at Monitor Attack Stream  950 . In this mode the activity on the offending IP address and its related data stream in monitored for volume increases and throttle adjustments made at Throttle or Dump as Required  955 . Note that the activities of monitoring and tracing occur in parallel and continue throughout the life of the attack. Recall also that during the detection and management of an attack, the method of the present invention continues to look at the entire IP address space served by a given PADA. By so doing, the method of the present invention optimizes the detection of intruder DOS attacks while minimizing any deleterious effects on legitimate traffic over the related IP address space. 
   At some point in time the attack will have run its course. When this occurs the attack data are updated at Update Attack Data  903 , the system updated at Update System Status  906  and an all clear signal sent at Send Attack Resolved  910 . At this point the method of the present invention returns to the Nominal Activity State  500  and resumes normal operation. 
     FIG. 9  illustrates the Reference Generation State  600 , which is part of the overall state machine  400 . This state is entered from the Nominal Activity State  500  via Start Next Polling Cycle  510 . Within the Reference Generation State  600  the next sequential IP address for the associated data pipe is fetched at Fetch Next Data Pipe IP Addr  620 . The data stream volume for this IP address is sampled at Sample Traffic Volume  623 . The reference data volume for the current IP address is fetched at Fetch Reference Data  625 , then the two are compared at Compare  627 . 
   The results of the comparison are updated at Update Nominal Traffic Data  610 . By constantly cycling through the entire IP address space for a given data pipe and updating the data stream volumes related to the individual IP addresses, the method of the present invention is able to dynamically adjust for the normal periodic variations in data stream volume. These variations occur, for example, as a result of service demand peaks during the mid-day hours as shown in  FIG. 4  above. By providing this dynamic adjustment capability, the present invention minimizes the occurrences of false positive detections of service request attacks. Once the nominal traffic has been updated the cycle pointer is incremented at Increment Cycle Pointer at  615 , and the Nominal Activity State  500  reentered where normal operation continues. 
   A first advantage of the present invention is the rapid detection of known attack patterns over large IP address spaces in effectively real time. For the preferred embodiment of the present invention, a DOS attack in a 10 gigabyte data pipe can be detected in as little at 18 seconds to a maximum of 10 minutes and 18 seconds, depending on where in the IP address space cycle the IP address pointer is when the attack commences. 
   A second advantage of the present invention is the ability to isolate a rouge data stream while having minimal effect on throughput of legitimate traffic. Since the detection and reaction to an attack is accomplished on an address-by-address basis, normal activity in non-affected IP addresses continues unaffected. This can be done since the PADA, responsible for detection of, reaction to and management of an attack, is connected in parallel with its associated router. All legitimate traffic being handled by the router passes without throughput compromise. 
   A third advantage of the present invention is the constant update of forged IP addresses and corrupt data patterns in an attack reference data base. By providing a current library of known attack signatures, the method of the present invention is capable of more rapid confirmation of, and thus reaction to, known intruder behavior. This feature also aids the tracing activity of adjunct intruder detection systems. 
   A fourth advantage of the present invention is its advanced traffic profiling feature. This dynamic adjustment to periodic data volume changes reduces the incidence of false positive attack indications. The method of the present invention samples the data streams related to each of the IP addresses being serviced by a given router at a minimum of once every ten minutes. In so doing the volume variations associated with peak service periods are accommodated. False positive attacks are further reduced through use of a multi-tiered attack detection method that applies the dynamically updated volumetric data to rules based analysis for both packet and data validity. 
   A fifth advantage of the present invention is its scalable nature and platform independence. The method of the present invention can be programmed for use on a multitude of hardware platforms. It is scalable by the fact that as routers are added to a network system, additional PADAs can simply be added in parallel.