Patent Publication Number: US-9430645-B2

Title: Method and system for analysis of security events in a managed computer network

Description:
STATEMENT OF RELATED PATENT APPLICATION 
     This patent application is a continuation of U.S. patent application Ser. No. 14/227,610, filed on Mar. 27, 2014, which is a continuation of U.S. patent application Ser. No. 11/359,261, filed on Feb. 22, 2006, which claims priority under 35 U.S.C. §119 to U.S. Provisional Patent Application No. 60/655,158, filed Feb. 22, 2005. Each application is hereby fully incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to the security of computing devices in a computer network. More particularly, the present invention relates to a method and system for receiving a set of data for a device, categorizing the data based on potential security events, and comparing the current number potential security events to a stored value for the device to determine if an alert should be activated. 
     BACKGROUND OF THE INVENTION 
     As e-commerce, or doing business over the Internet, becomes a way of life rather than being characterized as novel commercial activity, protecting computer systems against malicious attacks or alleged pranks becomes vital to both businesses and individuals because of potential economic disasters. In other words, because businesses and individuals are becoming more and more dependent upon computer networks that are integrated with the Internet, any interruptions in service or attacks on such computer systems could have devastating financial repercussions. 
     Security threats come in a variety of forms and almost always result in a serious disruption to a network. Hackers can gain unauthorized access by using a variety of readily available tools to break into the network. The hacker no longer needs to be an expert or understand the vulnerabilities of the network—they only need to select a target and attack, and once in, the hacker has control of the network. Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks aim to disable a device or network so users no longer have access to network resources. Using Trojan horses, worms, or other malicious attachments, hackers can plant these tools on countless computers. Worms are programs designed to infect networks, such as the Internet. A worm travels from network to network replicating itself along the way. Trojan horses pretend to be a program that the user wishes to launch. A Trojan horse can be a program or file that disguises itself as normal, helpful programs or files, but in fact are viruses. 
     In addition, viruses can attach to email and other applications and damage data and cause computer crashes. A computer virus is a broad term for a program that replicates itself. A virus can cause many different types of damage, such as deleting data files, erasing programs, or destroying everything found on a computer hard drive. Not every virus can cause damage; some viruses simply flash annoying messages on a computer screen. A virus can be received by downloading files from the Internet to a personal computer or through electronic mail. Users increase the damage by unknowingly downloading and launching viruses. Viruses are also used as delivery mechanisms for hacking tools, putting the security of the organization in doubt, even if a firewall is installed. Hackers can deploy sniffers to capture private data over networks without the users of this information being aware that their confidential information has been tapped or compromised. 
     As noted above, the nature of a distributed network makes it vulnerable to attack. The Internet was designed to allow for the freest possible exchange of information, data, and files. However, this free exchange of information carries a price: Some users will try to attack the Internet and computers connected to the Internet; while others will try to invade other users&#39; privacy and attempt to crack databases of sensitive information or snoop around for information as it travels across a network. 
     The field of managed security grew out of a need by companies with distributed networks to protect and monitor their devices on their network from attacks. Through a thorough understanding of the devices and network topology security providers attempt to monitor the network, and the data flowing through it, to recognize a potential attack or security event before the network is adversely affected. Security providers typically monitor a customer&#39;s network by obtaining information from intrusion detection sensors and other network devices. One conventional method of analyzing this data is through the use of security engineers manually looking at one or more screens of data representing customers&#39; networks to determine if an attack is occurring. However, even in a relatively small network, the network traffic can generate an excessive amount of data, such that, it is unlikely that the security engineer could spot all or even most of the attacks. 
     In addition, the conventional method is not an efficient and effective use of engineering resources. Instead of searching to determine where a problem might be, it would be more efficient to signal the security engineers when network usage is outside a predetermined norm so that the engineer&#39;s time is spent solving, not searching for, the problem. Furthermore, under the conventional method, security providers have a difficulty retaining qualified security personnel because the monotonous time spent looking for problems is mentally and physically stressful, leading to a high burnout rate. 
     Accordingly, there is a need in the art for an automated system for receiving categorized event data representing a type or severity of an attack on the network and comparing the count of each category of event data to a normal count of potential attacks on the device to determine if an alert should be generated, the alert representing a significant increase in one or more types of attacks on the device. Furthermore, there is a need in the art for generating a normalized profile of event count data for each device in the network and updating this normalized profile as the network matures so that a determination can be made if activity rises to the level such that alerts should be triggered and action should be taken by the security engineers. 
     SUMMARY OF THE INVENTION 
     The event retrieval and analysis system can retrieve event data from a device on a network, categorize recordable events in the event data, and compare the categorized counts to stored profiles of data for that device against a threshold matrix to determine if alerts should be triggered for the device. In support of its alert determination, a sensor associated with a device passes event data to the analysis system. The event data can then be sorted into categories of recordable events. Categories generally represent one or more groupings of security events in the event data that represent a type or severity of a potential attack on the device or network. For one aspect, the categories being summarized include low priority event count, medium priority event count, high priority event count, total event count, unique signatures count, scanned event count, worm signature event count, sweeps signature event count, hot decodes signature event count, and staging signature event count. 
     Each category can be summed into current count data and compared to a normal profile or prior event count data for the device and stored in a database. A threshold matrix can be retrieved and used to analyze the current event count data as compared to the normal profile or prior event count data to determine if an alert should be triggered. The alert can include an audible or visual alarm, a report describing the reason for the alert, or a notification of the alert sent to a pager, phone, cell phone, email address or workstation for viewing and analysis by a technician. The threshold matrix typically includes a table having rows for “minimum count” and “maximum count”, and “percentage change required to trigger an alert”. The threshold matrix can also include one or more columns of count ranges that provide the range of event count and the percentage change needed at that event count level to trigger an alert. 
     For one aspect of the present invention the analysis system can receive a current event count for a category of recordable events for a device in a computer network. The device can be the entire network, a portion of the network, or a single node in the network. A normal profile for the device can be retrieved from a database. The normal profile typically includes normal event counts in each category for the device. The difference between the current event count and the normal event count can be calculated for each category. If the current event count is greater than the normal event count, a percentage increase can be calculated by dividing the difference between the current event count and the normal event count by the normal event count. An alert percentage can be obtained from a table stored in a database. The alert percentage is typically determined based on the current event count for the category. Each alert percentage can be associated with a range of current event counts. The correct alert percentage is determined by finding the range of count data that the current event count data fits in and retrieving the associated alert percentage. A comparison can then be made between the alert percentage and the percentage increase of event counts. Percentage increase for event counts greater than or equal to the alert percentage will result in an alert being triggered in the analysis system. 
     For another aspect of the present invention, the analysis system can receive a current event count for a category of recordable events for a device in a computer network. A previous profile count for the device can be retrieved from a database. The previous profile count typically represents event count data of the device for the most recently completed event count analysis. The difference between the current event count and the previous profile count can be calculated for each category. If the current event count is greater than the previous profile count, a percentage increase can be calculated. An alert percentage based on the current event count can then be obtained from a table stored in a database. A comparison can then be made between the alert percentage and the percentage increase of event counts. Percentage increase for event counts greater than or equal to the alert percentage will result in an alert being triggered in the analysis system. 
     For a further aspect of the present invention, the analysis system can receive a current event count for a category of recordable events for a device in a computer network. Two or more previous profile counts for the device can be retrieved from a database. An average profile count can be determined for each category in the previous profile counts by summing the previous profile counts for a category and dividing the sum by the total number of profile counts retrieved. The difference between the current event count and the average profile count can be calculated for each category. If the current event count is greater than the average profile count, a percentage increase can be calculated. An alert percentage based on the current event count can then be obtained from a table stored in a database. A comparison can then be made between the alert percentage and the percentage increase of event counts. Percentage increase for event counts greater than or equal to the alert percentage will result in an alert being triggered in the analysis system. 
     For yet another aspect of the present invention, the analysis system can receive event data for a device from sensors and other devices in the computer network. The sensors typically review data packets for intrusion events or recordable events that may be an attack or a precursor to an attack on the device. Device data can be obtained from a database. The device data can include information related to the device&#39;s state, including the “normal” profiles of a given sensor at the device and any information about open alert tickets associated with the device. One or more rules can be retrieved from cache and applied by a rules engine to the event data to determine if there are any recordable events. Each recordable event can be placed into one or more categories and the current total event count for each category can be calculated by summing all of the recordable events in a category. A normal profile for the device can be retrieved from a database. The current total event count can then be compared to the normal profile count for the first category to determine if there is an increase in the event count over the normal profile count which may represent an attack on the device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of the exemplary embodiments of the present invention and advantages thereof, reference is now made to the following description in conjunction with the accompanying drawings in which: 
         FIG. 1  is a block diagram illustrating an exemplary operating environment for implementation of various embodiments of the present invention; 
         FIG. 2  is a flowchart illustrating a process for receiving a series of event data from multiple sensors in a network computing system and processing information for the event data in accordance with an exemplary embodiment of the present invention; 
         FIG. 3  is a flowchart illustrating a process for conducting the initial processing of event data in accordance with an exemplary embodiment of the present invention; 
         FIG. 4  is a flowchart illustrating a process for converting event data into discrete worker tasks in accordance with an exemplary embodiment of the present invention; 
         FIG. 5  is a flowchart illustrating a process for transmitting tasks to worker nodes in accordance with an exemplary embodiment of the present invention; 
         FIG. 6  is a flowchart illustrating a process for associating a worker with a particular network in accordance with an exemplary embodiment of the present invention; 
         FIGS. 7 and 7A  are flowcharts illustrating task processing on the event data in accordance with an exemplary embodiment of the present invention; 
         FIGS. 8 and 8A  are flowcharts illustrating a process for evaluating the event data based on a set of rules in accordance with an exemplary embodiment of the present invention; 
         FIG. 9  is a flowchart illustrating a process for comparing data obtained in the task processing to previous data obtained in regards to the device of the computing system in accordance with an exemplary embodiment of the present invention; 
         FIG. 10  is a flowchart illustrating a process for recalculating the “normal” profile for a device on the computing system in accordance with an exemplary embodiment of the present invention; and 
         FIG. 11  is block diagram of a matrix used in the comparison of data for a device in accordance with an exemplary embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS 
     The present invention supports a computer-implemented method and system for retrieval and analysis of event data in a networked computing system. Exemplary embodiments of the present invention can be more readily understood by reference to the accompanying Figures. Although exemplary embodiments of the present invention will be generally described in the context of a software module and operating system running on a network, those skilled in art will recognize that the present invention can also be implemented in conjunction with other program modules for other types of computers. Furthermore, those skilled in the art will recognize that the present invention may be implemented in a stand-alone or in a distributed computing environment. 
     In a distributed computing environment, program modules may be physically located in different local and remote memory storage devices. Execution of the program modules may occur locally in a stand-alone manner or remotely in a client/server manner. Examples of such distributed computing environments include local area networks, enterprise wide computer networks, and the global Internet. 
     The detailed description that follows is represented largely in terms of processes and symbolic representations of operations by conventional computing components, including processing units, memory storage devices, display devices, and input devices. These processes and operations may utilize conventional computer components in a distributed computing environment. 
     The processes and operations performed by the computer include the manipulation of signals by a processing unit or remote computer and the maintenance of these signals within data structures resident in one or more of the local or remote memory storage devices. Such data structures impose a physical organization upon the collection of data stored within a memory storage device and represent specific, electrical or magnetic elements. The symbolic representations are the means used by those skilled in the art of computer programming and computer construction to most effectively convey teachings and discoveries to others skilled in the art. 
     Exemplary embodiments of the present invention include a computer program that embodies the functions described herein and illustrated in the appended flowcharts. However, it should be apparent that there could be many different ways of implementing the invention in computer programming, and the invention should not be construed as limited to any one set of the computer program instructions. Furthermore, a skilled programmer would be able to write such a computer program to implement a disclosed embodiment of the present invention without difficulty based, for example, on the flowcharts and associated description in the application text. Therefore, disclosure or a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use the present invention. The inventive functionality of the computer program will be explained in more detail in the following description and is disclosed in conjunction with the remaining Figures illustrated in the program below. 
     Referring now to the drawings, in which like numerals represent like elements throughout the several Figures, aspects of the present invention and an exemplary operating environment for the implementation of the present invention will be described.  FIG. 1  is a block diagram illustrating an event retrieval and analysis system  100  constructed in accordance with an exemplary embodiment of the present invention. The exemplary event analysis system  100  includes multiple sensors  105 , an aggregator  110 , a scheduler  115 , multiple scheduler wrappers  120 , an MSS database  135 , an information database  137 , an XPS database  140 , a scheduler database  145 , an information database  137  a rules engine  150 , and a trouble ticketing system  160 . 
     The sensors  105  are communicably attached via a distributed computer network to the aggregator  110 . In one exemplary embodiment, the sensors  105  receive event data from one or more devices in a networked computing system. The aggregator  110  is communicable attached via a distributed computer network to the scheduler  115 . The aggregator receives all the incoming event data from the various computer sensors  105 . The aggregator  110  typically arranges this data and forwards it to the scheduler  115  for processing. 
     The scheduler  115  is communicably attached via a distributed computer network to the aggregator  110  and to several schedule wrappers  120 . The scheduler  115  handles management of the event data being received from the sensors  105  through the aggregator  110 . In one exemplary embodiment, upon startup of this system  100 , the scheduler  115  registers as a client to the aggregator  110  to receive the event data from the aggregator  110 . The scheduler  115  then converts the event data stream into discrete work tasks, which can then be sent out to the scheduler wrappers  120  for processing. In one exemplary embodiment, the scheduler  115  communicates with the scheduler wrappers  120  via Java RMI. 
     The scheduler wrappers  120  are communicably attached via a distributed computer network to the scheduler  115 , the MSS database  135 , the information database  137 , the XPS database  140 , the scheduler database  145 , and the rules engine  150 . The scheduler wrapper  120  conducts the processing of the event data received from the scheduler  115 . When a scheduler wrapper  120  is initiated, it registers with the scheduler  115 , indicating that the scheduler wrapper  120  is ready to accept tasks or event data for processing. At that point, the scheduler  115  can begin dispatching tasks to the scheduler wrapper  120  for it to then dispatch to workers  125  and worker threads  130 . 
     Each scheduler wrapper  120  represents a distinct process running on the scheduler  115 . The scheduler wrapper  120  is responsible for starting up or initiating individual worker threads  130  and managing the worker thread&#39;s  130  life cycle. The scheduler wrapper  120  receives Java RMI calls from the scheduler  115  and communicates to the scheduler  115  on behalf of its workers  125 . In one exemplary embodiment, the scheduler wrapper  120  is a very light wrapper around a set of distinct worker threads  130  running within a single Java VM process. 
     A scheduler  115  may run any number of scheduler wrapper  120  processes, each in a distinct java VM instance. A given scheduler wrapper  120  may run any arbitrary number of worker threads  130 , although, in one exemplary embodiment, the processing degradation often occurs with approximately thirty distinct worker threads  130  in a single scheduler wrapper  120 . In one exemplary embodiment, each worker  125  within a scheduler wrapper  120  operates twenty worker threads  130 . 
     The worker  125  is a node in the scheduler wrapper  120  that processes analysis tasks on the event data. Each worker  125  operates as a thread within the scheduler wrapper  120 , essentially looping forever in a processing loop of receiving analysis tasks passed on from the scheduler wrapper  120 . The worker  125  will typically receive an analysis task containing new event data to process from the scheduler  115 . The worker  125  retrieves the last known device state for the device in question and runs the analysis task using the data received. The worker  125  then triggers any alerts to the trouble ticketing system  160 . 
     The MSS database  135  is communicably attached via a distributed computer network to the scheduler wrapper  120 . The MSS database  135  typically contains general research information relating to attack data and specific customer network data to give a richer set of data for the rules engine  150  to use in making decisions it interprets in the event data. The MSS database may further include firewall logs, the customer&#39;s security information, information about the customer&#39;s network topology, scanning information, and indications of which IP is on the customer&#39;s network protocol. The worker thread  130  can obtain the data in the MSS database  135  and use it to assist the worker thread in its decisional processes with regards to what particular event data may mean. 
     The information database  137  is communicably attached via a distributed computer network to the scheduler wrapper  120  and the scheduler  115 . The information database  137  typically contains device data not contained in the other databases of the event analysis system  100 . In addition, the information database  137  can contain information from the trouble ticketing system  160 , information related to the network topology and platform of customers, operating system information, known critical servers, customer specific information, and DMS lookup information. The worker thread  130  can obtain the data in the information database  137  and use it to assist the worker thread in its decisional processes with regards to what particular event data may mean. 
     The XPS database  140  is communicably attached via a distributed computer network to the scheduler wrapper  120 . The XPS database  140  typically contains historical event data and summarized information generated by the event analysis system  100 . The worker thread  130  can obtain the data in the XPS database  140  and use it to assist the worker thread in its decisional processes with regards to what particular event data may mean. 
     The scheduler database  145  is communicably attached via a distributed computer network to the scheduler wrapper  120 . The scheduler database  145  includes the data processed by the worker threads  130 , a copy of the decisions made by the worker threads  130 , the “normal” profiles for the devices on each network being analyzed by the worker threads  130 , the device states for the devices on each network being analyzed, the state of the networks being analyzed, stored event data counts for each category of data for each of the devices on each network being analyzed, and a listing of rules to be applied to each device by the worker threads  130 . Those of ordinary skill in the art will recognize that the information described in the MSS database  135 , the information database  137 , the XPS database  140 , and the scheduler database  145  can be stored in one or several storage devices and that the particular storage device the data is stored and the specific number of storage devices used to store the data can be easily modified and adjusted based on the users specific needs. 
     The rules engine  150  is communicably attached via a distributed computer network to the scheduler wrapper  120  and the trouble ticketing system  160 . The rules engine  150  receives event data and processes one or more rules against that data. The rules analyzed against the event data can be the same for every network or every device on a particular network. On the other hand, the rules can be different for every device on the network. The rules engine  150  can generate alerts based on event data that triggers a rule. The alerts can be transmitted by the rules engine  150  to the trouble ticketing system  160  through the use of an incident report  155 . On the other hand, the rules engine  150  can generate and alert and transmit that alert to a notifier (not shown). The alert from the rules engine  150  can include instructions on the method of alert produced by the notifier. In one exemplary embodiment, alerts can include email notifications, text messages, pages to a cell phone or pager, audible messages delivered to a workstation, phone, or cell phone, an incident report  155 , textual messages sent to a workstation, visual or audible alarms sent to a workstation or other methods of alerting known to those of ordinary skill in the art. 
     The incident report  155  is typically generated by the rules engine  150  by a worker thread  130 . The incident report  155  can include information related to the fact that an alert has occurred, basic information about the customer associated with the device from which the event data was received, the device from which the event data was received, the type of alert, why the alert was triggered, and the percentage increase in current event counts for the category that triggered the alert. In one exemplary embodiment, the incident report  155  is a detailed breakdown of events leading up to the generation of an alert by the rules engine  150 . The data in the exemplary incident report  155  is sorted by each signature name, then by source IP, then by destination IP. In addition each IP address is examined based on the customer information to determine if the IP address is external to the customer, internal, or a critical system for the customer. The trouble ticketing system  160  is communicably attached via a distributed computer network to the scheduler wrapper  120  and the rules engine  150 . The trouble ticketing system  160  generates trouble tickets based on alerts received from the rules engine  150 . 
       FIGS. 2 through 10  are logical flowchart diagrams illustrating the computer-implemented processes completed by exemplary methods for receiving and analyzing event data by the event analysis system  100 .  FIG. 2  is a logical flowchart diagram presented to illustrate the general steps of an exemplary process  200  for receiving and analyzing event data from a device in a computer network within the operating environment of the exemplary event analysis system  100  of  FIG. 1 . 
     Now referring to  FIGS. 1 and 2 , the exemplary method  200  begins at the START step and proceeds to step  205 , where event data for a device on a network is received at a sensor  105 . In step  210 , the sensor  105  transmits the event data to the aggregator  110 . In step  215 , the aggregator  110  conducts initial processing of the event data received from the sensors  105 . In one exemplary embodiment, the initial processing of the event data includes determining the device the event data is associated with and the customer for whom the device is being monitored. The scheduler  115  registers itself as a client to the aggregator  110  in step  220 . 
     In step  225 , the aggregator  110  transmits the event data to the scheduler  115 . The scheduler  115  converts the event data into discrete work tasks in step  230 . In step  235 , the scheduler wrapper  120  registers itself with the scheduler  115 . The scheduler  115  transmits the discrete work tasks to the workers  125  in the scheduler wrapper  120  in step  240 . In step  245 , the rule engine  150  processes rules on the work tasks passed to it by the worker thread  130 . 
     In step  250 , an inquiry is conducted by the scheduler  115  to determine if a completion report was received from the worker thread  130  through the scheduler wrapper  120 . If a completion report was not received from the scheduler wrapper  120 , the “NO” branch is followed to step  255 , where the scheduler  115  retrieves the discrete work task from the worker thread  130 . The process then returns to step  240  for retransmission of the discrete work task to another worker thread  130  in the worker  125  for processing. Returning to step  250 , if a completion report was received from the scheduler wrapper  120  by the scheduler  115 , then the “YES” branch is followed to step  260 . In step  260 , the scheduler  115  removes the task from its pending queue list. In step  265 , the scheduler wrapper  120  transmits updated device state data to the scheduler database  145  for the discrete work task completed. The process then concludes at the END step. 
       FIG. 3  is a logical flowchart diagram illustrating an exemplary computer-implemented method for the aggregator  110  to conduct initial processing of the event data received from the sensor  105 , as completed by step  215  of  FIG. 2 . Referencing  FIGS. 1, 2, and 3 , the exemplary method  215  begins by receiving the event data from the individual sensor  105  in step  305 . In step  310 , the aggregator  110  determines which device that the event data is associated with. In one exemplary embodiment, event data is associated with the device if the event data is based on data packets being sent to or from the device. 
     In step  315 , the aggregator  110  adds the received event data to a data queue for that device. In an alternative embodiment, the aggregator  110 , bypasses the queuing step and passes the event data directly to the scheduler  115 . In step  320 , an inquiry is conducted by the aggregator  110  to determine if the data queue for that device has reached its limit. In one exemplary embodiment, the data queue reaches its limit when it has stored 1000 logs of data. In an alternative embodiment, the data queue for a device reaches it limit when it can no longer hold additional event data in the queue or does not have sufficient room to receive and store an additional download of event data from the sensor  105 . In another alternative embodiment, the data queue reaches its limit when it has stored a certain amount of data, irrespective of whether there is additional room in the data queue for more event data. If the data queue has not reached its limit, the “NO” branch is followed to step  325 . 
     If the data queue has reached its limit, the “YES” branch is followed to step  330 , where the aggregator  110  summarizes the data by categories. Categories generally represent one or more groupings of security events in the event data that represent a type or severity of a potential attack on the device or network. In one exemplary embodiment, the categories being summarized include low priority event count, medium priority event count, high priority event count, total event count, unique signatures count, scanned event count, worm signature event count, sweeps signature event count, hot decodes signature event count, and staging signature event count. 
     The scanned signature event count typically represents known scanning attacks against the device or network. Worm signature event count typically represents known worm attacks against the device or network. Sweeps signature event count typically represents known network sweep attacks against the network. Hot decodes signature event count typically represents high priority signatures that are currently being tracked by a technician at a workstation. In one exemplary embodiment, the hot decodes signature event count category uses a very sensitive threshold for determining alerts, such that almost any deviation from normal is considered significant and thus, worthy of sending out an alert by the rules engine  150 . The staging signature event count typically represents signatures currently being tested for inclusion into other categories. Those of ordinary skill in the art will recognize that the categories that the security events in the event data are organized into can be modified and amended based on new attacks, changes to the devices being monitored, changes to the network topology for the networks being monitored, changes to existing attack methods, or other reasons known to those of ordinary skill in the art. 
     In step  325 , an inquiry is conducted to determine if a predetermined amount of time has passed since the last data dump from the data queue in the aggregator  110  to the scheduler  115 . In one exemplary embodiment, the predetermined amount of time has been set at ten minutes, such that, if the data queue does not reach a full limit in step  320  prior to the passing of ten minutes time since the last data dump from the queue, the aggregator  110  will automatically dump the event data from the queue to the scheduler  115 . Those of ordinary skill in the art will recognize that the predetermined amount of time is adjustable from instantaneous to an infinite amount of time based on the technicians preferences and needs. In an alternative embodiment, the data is passed directly from the aggregator  110  to the scheduler  115  without queuing the event data, such that the need to determine if a predetermined amount of time has passed is eliminated. If the predetermined amount of time has not passed, the “NO” branch is followed to step  305  where additional event data can be received from the sensor  105 . On the other hand, if the predetermined amount of time has passed since the last data dump from the data queue, the “YES” branch is followed to step  330 , where the event data is summarized into categories by the aggregator  110 . The process then continues to step  220  of  FIG. 2 . 
       FIG. 4  is a logical flowchart diagram illustrating an exemplary computer-implemented method for converting event data into discrete work tasks at the scheduler  115  as complete by step  230  of  FIG. 2 . Now referring to  FIGS. 1, 2 and 4 , the exemplary method  230  begins at step  405 , where scheduler  115  receives the queued event data from the aggregator  110 . In step  410 , the scheduler  115  generates an event list comprised of the queued event data and a header. The scheduler  115  retrieves the name of the customer associated with the event data from the scheduler database  145  in step  415 . In step  420 , the scheduler  115  retrieves the name of the device associated with the event data from the scheduler database  145 . The scheduler  115  inserts the customer name and the device name into the header of the event list in step  425 . The process continues to step  235  of  FIG. 2 . 
       FIG. 5  is a logical flowchart diagram illustrating an exemplary computer-implemented method for transmitting discrete work tasks from the scheduler  115  to the workers  125 , as complete by step  240  of  FIG. 2 . Now referring to  FIGS. 1, 2 , and  5 , the exemplary method  240  begins with counter variable X being set equal to one in step  505 . Counter variable X typically represents the discrete work task being handled by a worker  125  and received from the scheduler  115 . In step  510 , the scheduler  115  retrieves the first discrete work task from the event list. In step  515 , the scheduler  115  determines the device associated with the first discrete work task. In one exemplary embodiment, the device is associated with the first discrete work task if the first discrete task includes event data from data packets sent to or from the device and received, copied, or intercepted by the sensor  105 . In step  520 , the scheduler  115  determines the computing network of the device. The network information is typically retrieved from the information database  137 . 
     In step  525 , an inquiry is conducted by the scheduler  115  to determine if a worker  125  is associated with the network on which the device is associated. In one exemplary embodiment, scheduler  115  receives event data for a given device, it will assign the work task to be processed on the worker  125  that is “bound” to the customer who own that device or asks that the device be monitored. By binding a given device and/or customer to a particular worker  125 , the scheduler  115  assures that tasks for a device are processed sequentially. In one exemplary embodiment, once the scheduler  115  transmits the work task to the worker  125 , the worker  125  has the responsibility of ensuring a first-in-first-out processing of tasks for a given device. 
     If a worker  125  has not been associated with this network, device or customer, the “NO” branch is followed to step  530 , where the scheduler  115  associates a worker  125  with the particular network, device, or customer. Otherwise, the “YES” branch is followed to step  535 , where the scheduler  115  transmits the discrete work task to the worker  125  associated with the network, device, or customer. In step  540 , an inquiry is conducted by the scheduler  115  to determine if another discrete work task needs to be transmitted to one of the workers  125 . In one exemplary embodiment, additional discrete work tasks are transmitted to workers  125  if additional tasks remain in the scheduler  115 . If another discrete work task needs to be transmitted to a worker  125 , the “YES” branch is followed to step  545 , where the counter variable X is incremented by 1. The processing returns to step  510  for the retrieval of the next discrete work task from the scheduler  115 . On the other hand, if there are no additional work tasks needed to be transmitted to the worker  125 , then the “NO” branch is followed to step  245  of  FIG. 2 . 
       FIG. 6  is a logical flowchart diagram illustrating an exemplary computer-implemented method for associating a worker with a particular network as completed by step  530  of  FIG. 5 . Now referring to  FIGS. 1, 5, and 6 , the exemplary method  530  begins with the scheduler  115  retrieving a listing of workers  125  in step  605 . In one exemplary embodiment, the listing of workers  125  is based on information provided by the scheduler wrappers  120  when they register with the scheduler  115 . In step  610 , the scheduler  115  determines which worker  125  is processing tasks for the fewest devices or networks. The scheduler  115  selects the worker  125  processing tasks for the fewest devices in step  615 . In one exemplary embodiment, if one or more workers  125  are not currently processing any tasks, the scheduler  115  will select the first worker  125  that it determines in not processing any tasks. In step  620 , the scheduler  115  associates the worker  125  with the current network, device, or customer from which discrete event tasks are being processed. The process then continues to step  535  of  FIG. 5 . 
       FIGS. 7 and 7A  are logical flowchart diagrams illustrating an exemplary computer-implemented method for processing tasks based on a set of rules as completed by step  245   FIG. 2 . Referencing  FIGS. 1, 2, 7, and 7A , the exemplary method  245  begins with a worker thread  130  receiving a discrete task from the pending queue in step  702 . In step  704 , the worker thread  130  retrieves data for the device associated with the event data being evaluated from the scheduler database  145 . 
     In one exemplary embodiment, the information retrieved by the worker thread  130  includes the last known device state for the device associated with the event data. The device state is a measure of the knowledge the system  100  has about a given device. The device state incorporates the known “normal” profiles of a given sensor  105 , the remedy ID assigned to the device, and any information about open alert tickets associated with the device. In addition, the device state can include “rule state” information, that captures the current state of each persistent rule and any metadata associated with that state. In one exemplary embodiment, this information is maintained in a persistent data store in the scheduler database  145 , which enables any worker  125  to access the data when it gets the analysis task for a given device. 
     In step  706 , the worker thread retrieves rules to be applied to the event data associated with the device from the rules engine  150 . Counter variable X is set equal to one in step  708 . Counter variable X represents each discrete rule retrieved from the rules engine  150 . In step  710 , counter variable Y is set equal to one. Counter variable Y represents a log of discrete task data for a device. In step  712 , the worker thread  130  transmits log Y to be analyzed against rule X in the rules engine  150 . Log Y is evaluated based on rule X from the rules engine in step  714 . 
     In step  716 , an inquiry is conducted by the rules engine  150  to determine if log Y data triggers rule X. If log Y data does trigger rule X, the “YES” branch is followed to step  718  where the rules engine  150  transmits the trigger state of the rule to a notifier in trouble ticketing system  160 . If log Y data does not trigger rule X, then the “NO” branch is followed to  720 . In step  720 , an inquiry is conducted by the worker thread  130  to determine if there is another rule X to apply to log Y data. If there is another rule to apply to the log Y data, the “YES” branch is followed to step  722 , where the counter variable X is incremented by one. The process then returns to step  712  for the transmission of the log Y data to the next rule in the rules engine  150 . On the other hand, if there are no additional rules to apply the log Y data to, the “NO” branch is followed to step  724 . 
     In step  724 , an inquiry is conducted by the worker thread  130  to determine if there is another log Y of data in the discrete task data. If there is another log Y of data in the task data, the “YES” branch is followed step  726 , where the counter variable Y is incremented by one. The process then returns to step  712  for the transmission of the next log Y of data to rule X in the rules engine  150 . On the other hand, if there is not another log Y of data, then no branch is followed to step  728 , where the worker thread  130  transmits a notification to the scheduler  115  and the rules engine  150  that processing task is complete. 
     In step  730 , an inquiry is conducted to determine if any rules need to perform final processing steps. In one exemplary embodiment, some rules include multiple steps that cannot be completed in a single analysis. For example, a rule may state that once a detection event is determined, the rule should wait for a predetermined amount of time to see if there is a corresponding state change. If the state change does not occur, then the rule can ignore the initial detection event. These rules are given the opportunity to conduct their final processing steps before the rules engine  150  completes the task processing step. If some rules need to perform final processing steps, the “YES” branch is followed to step  732 , where each rule conducts its final processing steps. On the other hand, if there are not any rules that need to perform final processing steps, then the “NO” branch is followed step  734 , where counter variable X is set equal to one. 
     In step  736 , the worker threads  130  transmit request notifications, or alerts, for the first rule to the notifier class. The trouble ticketing system  160  takes actions based on the notifications in steps  738 . In one exemplary embodiment, the actions taken by the trouble ticketing system  160  may include generating an incident report  155 , setting off an audible or visual alarm, sending textual, audio, or video messages to electronic devices including, but not limited to telephones, pagers, cell phones, email systems, voice mail systems, and PDA&#39;s that describe the alert, the device or customer associated with the event data that triggered the alert, a summary of the reason for the alert and a description of the device. 
     In step  740 , an inquiry is conducted by the worker thread  130  to determine if there is another rule for which request must be transmitted. If so, the “YES” branch is followed to step  742 , where the counter variable X is incremented by one. The process returns to step  736  to transmit a request for the next rule to the notifier class. On the other hand, if there is not another rule, then the “NO” branch is followed to step  744 , where clean-up task are conducted on the rules processed. In one exemplary embodiment, clean-up tasks include requesting that the rule release any state that is not significant, if the rule is holding open connections to data sources, such as the MSS database  135  or the XPS database  140 , it will close them, and conduct any other action necessary to bring each rule back to a zero state. 
     In step  746 , the rules are transformed in to a serialized form. Serializing the rules typically includes taking the state of the rule in memory and reducing it to a form that can be inserted into the scheduler database  145 , so that the rule can be recreated exactly in the same form as it was previously. In one exemplary embodiment, serialization includes converting the rule into a byte stream that can be reloaded into memory. In step  748 , the worker thread  130  through the schedule wrapper  120  transmits the serial rules and other data related to the analysis to the scheduler database  145 . The worker thread  130  transmits notification to the worker  125  that the task processing is complete in step  750 . In step  752 , the worker  125  through the scheduler wrapper  120  transmits notification to the scheduler  115  that the task processing is complete. The process then returns to step  250  of  FIG. 2 . 
       FIGS. 8 and 8A  are logical flowchart diagrams illustrating an exemplary computer-implemented method for evaluating log data based on rule X as completed by step  714  of  FIG. 7 . Now referring to  FIGS. 1, 7, 8, and 8A , the exemplary method  714  begins with the scheduler  115  summarizing the log data into categories in step  802 . In step  804 , the total event counts for each category are calculated. In one exemplary embodiment, the calculation of total event counts is achieved by summing the total number of events placed sorted into the category. The overall event count data is calculated in  806 . The overall event count represents the sum of the total event counts for all of the categories for a device or network. 
     In step  808 , metadata for the device being analyzed is retrieved from the information database  137 . In step  810 , information regarding the state of the device is retrieved. As discussed in greater detail above, the device state is a measure of the knowledge the system  100  has about a given device. The worker thread  130  retrieves the “normal” profile for the device being analyzed from the scheduler database  145  in step  812 . In one exemplary embodiment, each device has multiple “normal” profiles, each profile calculated for a specific hour of the day and a specific day of the week. For example, a device may have a “normal” profile designated “Tuesday—11 a.m.” and another designated Thursday—4 p.m.” When event data is retrieved from the sensor  105  for the device during the 11 a.m. hour on a Tuesday, the worker thread  130  will retrieve the “Tuesday—11 a.m. normal profile” from the scheduler database  145  for comparison analysis. 
     In step  814 , the worker thread  130  compares the current event count data to the “normal” profile using the threshold matrix. The threshold matrix will be described in more detail in  FIGS. 9 and 11 . In step  816 , an inquiry is conducted by the worker thread  130  to determine if any alerts have been triggered based on the comparison of the current event count data and the “normal” profile for the device. If alerts are triggered, the “YES” branch is followed to step  818 , where the triggered alerts are saved for later processing. On the other hand, if no alerts are triggered, then the “NO” branch is followed to step  820 . In step  820 , the worker thread  130  retrieves the previous hour profile from the scheduler database  145 . 
     In one exemplary embodiment, the previous hour profile is the event count data for each category and the overall event count for all categories for the device that was analyzed by the system during the hour prior to the time the current event data was obtained. One reason the event count data for the prior hour is analyzed, is to determine if there has been a sudden spike in event counts for one or more categories. Those of ordinary skill in the art will recognize that the prior event count retrieve could be composed of the prior hour&#39;s data, a single data count taken over multiple hours or a portion of a single hour, any other temporal division, or the most recently completed analysis of event counts for the device, irrespective of time. 
     In step  822 , the worker thread  130  compares the current event count data to the “previous hour” profile using the threshold matrix. In step  824 , an inquiry is conducted to determine if there are any alerts triggered based on the comparison of the “previous hour” profile in the current event count data. If alerts are triggered, the “YES” branch is followed to step  826 , where the triggered alerts are saved for processing at a later time. On the other hand, if no alerts are triggered, then the “NO” branch is followed to step  828 . 
     In step  828 , the worker thread  130  retrieves the event count data for the device for the previous four hours from the scheduler database  145 . While the exemplary embodiment describes the selection of the previous four hours of event count data, those of ordinary skill in the art will recognize that greater or fewer than the prior four hours of event count data may be retrieved. Retrieval may also be made irrespective of a particular amount of time, such that, for example, the prior four completed analyses of the event count data for the device may be retrieved irrespective of the time that each analysis was conducted. The worker thread  130  calculates the average counts for each category and the overall event count during the previous four hours data in step  830 . In step  832 , the worker thread  130  compares the current event count data to the average counts for the previous four hours using the threshold matrix. 
     In step  834 , an inquiry is conducted by the worker thread  130  to determine if any alerts are triggered based on the comparison of the current event count data and the average of the previous four hours event counts for each category and the overall event counts. If alerts are triggered, the “YES” branch is followed to step  842 . Otherwise, if there were not any alerts triggered, the “NO” branch is followed to step  836 . In step  836 , an inquiry is conducted by the worker thread  130  to determine if a trouble ticket was previously opened for this alert in this category of the device. If a trouble ticket was previously opened, the “YES” branch is followed to step  840 , where the trouble ticket is closed. On the other hand, if a trouble ticket was not previously opened, the “NO” branch is followed to step  844 . In step  842 , the triggered alerts are saved in the scheduler database  145 . 
     In step  846 , the worker thread  130  generates a trouble ticket at the trouble ticketing system  160  in one exemplary embodiment the trouble ticket may include information such as an incident report  155  transmitted to the trouble ticketing system  160 . In step  848 , an incident report in generated by the trouble ticketing system  160 . In step  850 , the worker thread  130  saves the trouble ticket and incident report in the scheduler database  145 . In step  852 , the worker thread  130  transmits the trouble ticket and incident report to the worker  125 . The worker  125  transmits the trouble ticket an incident report  155  to the scheduler  115  in step  854 . In step  856 , the scheduler  115  transmit the trouble ticket an incident report  155  to an evaluator for evaluation. 
     In step  858 , in an inquiry is conducted by the worker threads  130  to determine if any alerts were saved for later processing. If there were no alerts were saved for processing, then the “NO” branch is followed to step  860 . Otherwise the “YES” branch is followed to step  864 . In step  860 , the worker thread  130  conducts an inquiry to determine if any alerts have been saved for a significant amount of time. In one exemplary embodiment, the worker thread  130  conducts this inquiry in an effort to determine if a sensor  105  for a device is inoperable or not working properly. In one exemplary embodiment, if alerts have not been saved for two consecutive hours, that would be considered a significant amount of time. If no alerts have been saved for a significant amount of time, the “YES” branch is followed to step  862  where the worker thread  130  transmits an alert to the trouble ticketing system  160  that the sensor  105  associated with the device or network may have a problem. On the other hand, if alerts have been saved or a significant time has not been reached, then the “NO” branch is followed to step  864 . In step  864 , the worker thread  130  through the schedule wrapper  120  saves the event count data for the current hour for that device or network in the scheduler database  145 . In step  866 , the worker thread  130  recalculates the “normal” profile for the device or network for which the event data was received. The process then continues to step  716  of  FIG. 7 . 
       FIG. 9  is a logical flowchart diagram illustrating an exemplary computer-implemented method for comparing current event count data to other data using the threshold matrix as completed by steps  814 ,  822  and  832  of  FIG. 8 . Referencing  FIGS. 1, 8, and 9 , the exemplary method  814 ,  832 , and  832  begins with counter variable X being set equal to one in step  905 . Counter variable X represents a category of data as previously described above. In step  910 , the worker thread  130  retrieves the event count for the first category of data from the current event data. 
     In step  915 , the worker thread  130  calculates the difference between the current data count for the first category of data and the retrieved profile of count data for the first category of data. As discussed in  FIG. 8 , the retrieved profile may include the “normal” profile, the “previous hour” profile, and/or the “previous four hour average” profile. In step  920 , an inquiry is conducted to determine if the current event data count for the first category of data is greater than the count for that category of data in the retrieved profile. If the current event data count is greater, the “YES” branch is followed to step  925 , where the worker thread  130  calculates the percentage increase in the data count for the first category. 
     The worker thread  130  retrieves the threshold matrix associated with the first category in step  930 . In one exemplary embodiment, the system  100  may retrieve a single threshold matrix for every device of every customer, a different threshold matrix for each customer, a different threshold matrix for each device of each customer, a particular threshold matrix for a particular type of device, or any other combination known to those of ordinary skill in the art. In step  935 , the worker thread  130  determines that the percentage increase triggers an alert based on the current event data count for the first category of data. 
       FIG. 11  provides and exemplary block diagram of a threshold matrix. As shown in  FIG. 11 , the exemplary threshold matrix  1100  includes a table  1102 . The threshold matrix table  1102  includes rows for “minimum count”  1105 , “maximum count”  1110 , and “percentage change to trigger an alert”  1115 . The threshold matrix table  1102  also includes one or more columns of count rages  1115  that provide the range of event count and the percentage change needed at that event count level to trigger an alert. 
     An example of incorporating the exemplary threshold matrix  1100  may be helpful. Using the example of a first category and a comparison of the current event count data for the first category having a count of 508 and the “normal” profile for the first category having an event count of 250. Because the current event count for the first category is 508 the percentage change needed to trigger an alert is selected from column 2 of the matrix  1100 , based on the fact that 508 lies in between 501 and 1500. Thus, only if the percentage increase in the current event count over the “normal” profile for the first category is greater than 100% will the alert be triggered. In this example, the current event count is greater than the “normal” profile count and the difference is calculated as 258. When 258 is dived by 250, the “normal” profile count, the percentage increase is determined to be 103.2% and the alert is triggered. 
     Returning to  FIG. 9 , in step  940 , an inquiry is conducted to determine if there is another category of event count data that exists in the current event data. Returning to step  920 , if the current event data count for the first category of data is not higher than the count for the retrieved profile, then the “NO” branch is followed to step  940 . In step  940 , if another category of event count data exists, the “YES” branch is followed to step  945 . In step  945 , the counter variable X is incremented by one. The process then returns to step  910  for the retrieval of the next event count for the particular category of data. On the other hand, if there are no additional categories of event count data, then the “NO” branch is followed to step  816 ,  824 , or  834  of  FIG. 8 . 
       FIG. 10  is a logical flowchart diagram illustrating an exemplary computer-implemented method for recalculating the normal profile for a device associated with the event data retrieved by the sensor  105  and analyzed by the worker thread  130  as completed by step  866  of  FIG. 8 . Now referring to  FIGS. 1, 8A, and 10 , the exemplary method  866  begins with the worker thread  130  determining the hour of the day of the current log data that was retrieved by the sensor  105  in step  1005 . In step  1010 , the worker thread  130  determines the day of the week the current log data was retrieved by the sensor  105 . The worker thread  130  retrieves data points for the same hour of the day and the same day of the week as the retrieved log data in step  1015 . 
     In step  1020 , the worker thread calculates the trimmed mean of the current logged data and the retrieved data points. In one exemplary embodiment, the trimmed mean is calculated by evaluating all of the data points, event counts, including the current event count for the first each category, removing the minimum and maximum event count and calculating the average as the “normal” profile for that category. In an alternative embodiment, the trimmed mean is calculated by determining the standard deviation of all of the data points for the category, removing the data points that are outside a certain number of standard deviations, and calculating the average count based on the remaining data points. 
     In another alternative embodiment, the trimmed mean is calculated by generating a weighted average by giving greater preference, and thus, greater weight, to data points obtained more recently as compared to older data points. The worker thread  130  saves the trimmed mean as the “normal” profile in the scheduler database  145  for the particular hour of the day and day of the week that the event data was collected by the sensors  105  in step  1025 . The process then continues of  848  of  FIG. 8A . 
     In conclusion, the present invention supports a computer-implemented method for retrieving event data from a device on a network, categorizing recordable events in the event data, and comparing the categorized counts to stored profiles of data for that device against a threshold matrix to determine if alerts should be triggered for the device. It will be appreciated that the present invention fulfills the needs of the prior art described herein and meets the above-stated objectives. While there have been shown and described several exemplary embodiments of the present invention, it will be evident to those skilled in the art that the various modifications and changes may be made thereto without departing from the spirit and the scope of the present invention as set forth in the appended claims and equivalence thereof.