Patent Grant
7607169
The present invention relates to a computer-based system for capturing, normalizing, and cross-correlating security events from heterogeneous sources. More specifically, included in the system is a user interface for a network security console.
Computer networks and systems have become indispensable tools for modern business. Today terabits of information on virtually every subject imaginable are stored in and accessed across such networks by users throughout the world. Much of this information is, to some degree, confidential and its protection is required. Not surprisingly then, intrusion detection systems (IDS) have been developed to help uncover attempts by unauthorized persons and/or devices to gain access to computer networks and the information stored therein.
Intrusion detection may be regarded as the art of detecting inappropriate, incorrect or anomalous activity within or concerning a computer network or system. The most common approaches to intrusion detection are statistical anomaly detection and pattern-matching detection. IDS that operate on a host to detect malicious activity on that host are called host-based IDS (and may exist in the form of host wrappers/personal firewalls or agent-based software), and those that operate on network data flows are called network-based IDS. Host-based intrusion detection involves loading software on the system (the host) to be monitored and using log files and/or the host's auditing agents as sources of data. In contrast, a network-based intrusion detection system monitors the traffic on its network segment and uses that traffic as a data source. Packets captured by the network interface cards are considered to be of interest if they match a signature.
Regardless of the data source, there are two complementary approaches to detecting intrusions: knowledge-based approaches and behavior-based approaches. Almost all IDS tools in use today are knowledge-based. Knowledge-based intrusion detection techniques involve comparing the captured data to information regarding known techniques to exploit vulnerabilities. When a match is detected, an alarm is triggered. Behavior-based intrusion detection techniques, on the other hand, attempt to spot intrusions by observing deviations from normal or expected behaviors of the system or the users (models of which are extracted from reference information collected by various means). When a suspected deviation is observed, an alarm is generated.
Advantages of the knowledge-based approaches are that they have the potential for very low false alarm rates, and the contextual analysis proposed by the intrusion detection system is detailed, making it easier for a security officer using such an intrusion detection system to take preventive or corrective action. Drawbacks include the difficulty in gathering the required information on the known attacks and keeping it up to date with new vulnerabilities and environments.
Advantages of behavior-based approaches are that they can detect attempts to exploit new and unforeseen vulnerabilities. They are also less dependent on system specifics. However, the high false alarm rate is generally cited as a significant drawback of these techniques and because behaviors can change over time, the incidence of such false alarms can increase.
Regardless of whether a host-based or a network-based implementation is adopted and whether that implementation is knowledge-based or behavior-based, an intrusion detection system is only as useful as its ability to discriminate between normal system usage and true intrusions (accompanied by appropriate alerts). If intrusions can be detected and the appropriate personnel notified in a prompt fashion, measures can be taken to avoid compromises to the protected system. Otherwise such safeguarding cannot be provided. Accordingly, what is needed is a system that can provide accurate and timely intrusion detection and alert generation so as to effectively combat attempts to compromise a computer network or system.
In one embodiment, the present invention provides a user interface for a network security console associated with multiple network security devices. A graphical user interface (GUI) for use with an intrusion detection system, comprises a radar display that is configured to simulate a stream of time-based events chronologically. In addition, one or more playback controls are configured to control the stream during simulation. In further embodiments, the radar display includes a slider configured to allow a user to jump to events that occurred at a user-defined time.
The present invention is illustrated by way of example, and not limitation, in the figures of the accompanying drawings in which:
Described herein is a computer-based system for capturing security events from heterogeneous sources, normalizing such events to a common schema and cross-correlating such normalized events with rules to create meta-events. The system (one embodiment of which is manifest as computer software), enables aggregation, correlation, detection, and investigative tracking of suspicious network activities from multiple security devices. The present system also includes a user interface for a network security console associated with the heterogeneous sources. A graphical user interface (GUI) for use with an intrusion detection system includes a radar display that is configured to simulate a stream of time-based events chronologically. In addition, one or more playback controls are configured to control the stream during simulation. In further embodiments, the radar display includes a slider configured to allow a user to jump to events that occurred at a user-defined time.
Although the present system will be discussed with reference to various illustrated examples, these examples should not be read to limit the broader spirit and scope of the present invention. For example, the examples presented herein describe distributed agents, managers and consoles, which are but one embodiment of the present invention. The general concepts and reach of the present invention are much broader and may extend to any computer-based or network-based security system. Also, examples of the messages that may be passed to and from the components of the system and the data schemas that may be used by components of the system are given in an attempt to further describe the present invention, but are not meant to be all-inclusive examples and should not be regarded as such.
Some portions of the detailed description that follows are presented in terms of algorithms and symbolic representations of operations on data within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the computer science arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers or the like. It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, it will be appreciated that throughout the description of the present invention, use of terms such as “processing”, “computing”, “calculating”, “determining”, “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
As indicated above, one embodiment of the present invention is instantiated in computer software, that is, computer readable instructions, which, when executed by one or more computer processors/systems, instruct the processors/systems to perform the designated actions. Such computer software may be resident in one or more computer readable media, such as hard drives, CD-ROMs, DVD-ROMs, read-only memory, read-write memory and so on. Such software may be distributed on one or more of these media, or may be made available for download across one or more computer networks (e.g., the Internet). Regardless of the format, the computer programming, rendering and processing techniques discussed herein are simply examples of the types of programming, rendering and processing techniques that may be used to implement aspects of the present invention. These examples should in no way limit the present invention, which is best understood with reference to the claims that follow this description.
Referring now to
Agents 12 are software programs that provide efficient, real-time (or near real-time) local event data capture and filtering from a variety of network security devices and/or applications. The primary sources of security events are common network elements including firewalls, intrusion detection systems and operating system logs. Agents 12 can collect events from any source that produces event logs or messages and can operate at the native device, at consolidation points within the network, and/or through simple network management protocol (SNMP) traps.
Managers 14 are server-based components that further consolidate, filter and cross-correlate events received from the agents, employing a rules engine 18 and a centralized event database 20. One role of manager 14 is to capture and store all of the real-time and historic event data to construct (via database manager 22) a complete, enterprise-wide picture of security activity. The manager 14 also provides centralized administration, notification (through one or more notifiers 24), and reporting, as well as a knowledge base 28 and case management workflow. The manager 14 may be deployed on any computer hardware platform and one embodiment utilizes a relational database management system such as an Oracle™ database to implement the event data store component. Communications between manager 14 and agents 12 may be bi-directional (e.g., to allow manager 14 to transmit commands to the platforms hosting agents 12) and encrypted. In some installations, managers 14 may act as concentrators for multiple agents 12 and can forward information to other managers (e.g., deployed at a corporate headquarters).
Consoles 16 are computer- (e.g., workstation, handheld, laptop, etc.-) based applications that allow security professionals to perform day-to-day administrative and operation tasks such as event monitoring, rules authoring, incident investigation and reporting. Access control lists allow multiple security professionals to use the same system and event database, with each having their own views, correlation rules, alerts, reports and knowledge base appropriate to their responsibilities. A single manager 14 can support multiple consoles 16.
In some embodiments, a browser-based version of the console 16 may be used to provide access to security events, knowledge base articles, reports, notifications and cases. That is, the manager 14 may include a web server component accessible via a web browser hosted on a personal or handheld computer (which takes the place of console 16) to provide some or all of the functionality of a console 16. Browser access is particularly useful for security professionals that are away from the consoles 16 and for part-time users. Communication between consoles 16 and manager 14 is bi-directional and may be encrypted.
Through the above-described architecture the present invention can support a centralized or decentralized environment. This is useful because an organization may want to implement a single instance of system 10 and use an access control list to partition users. Alternatively, the organization may choose to deploy separate systems 10 for each of a number of groups and consolidate the results at a “master” level. Such a deployment can also achieve a “follow-the-sun” arrangement where geographically dispersed peer groups collaborate with each other by passing primary oversight responsibility to the group currently working standard business hours. Systems 10 can also be deployed in a corporate hierarchy where business divisions work separately and support a rollup to a centralized management function.
Examining each of the various components in further detail, we begin with the agents 12. Agents 12 are used to collect, reduce and normalize the enormous amount of data that is generated by a network's security devices before a manager 14 acts on the data. As will become evident, this process goes beyond simple log consolidation. Before presenting those details, however, and to understand why such measures are desirable, some background regarding how analysts currently cope with security event information generated by multiple network devices is useful.
Conventional intrusion detection systems can help an analyst detect an attack directed at a network resource such as a server. Usually, such investigations are launched in response to an alert generated by the IDS. As a first step after receiving such an alert, an analyst might review perimeter router logs to see if a router associated with the network passed a packet that triggered the alert. If such a packet were discovered, the analyst would likely then want to review one or more firewall logs to see if any existing filters blocked the suspect packet. Assume, for the sake of this example, the suspect packet got past any firewalls; further investigation would be necessary to determine whether the integrity of the server itself was compromised. Such an integrity check may be performed using a conventional software application such as Tripwire, which is a file integrity checker employing MD5 checksums, to see which files, if any, had been accessed or modified. Finally, the analyst may have to examine a Syslog or an EventLog from the subject server, as well as any tcpdump data collected by a dedicated tcpdump host, for the segment of time surrounding the attack to determine what actually happened.
By this time the analyst has accessed many different systems and looked at several different types of logs in an effort to distill a comprehensive view of the attack. This can be a significant amount of work, and time taken in such review and analysis is time lost from the vitally important tasks of securing the network and restoring the compromised server to make sure that no other systems will be affected. The present invention helps to minimize the time spent on such analysis by consolidating all the relevant information in a single logging facility, allowing the analyst to look at the data in whatever sequence or depth he or she requires.
More than just consolidation, though, the present agents 12 provide data normalization, which is of great benefit when an analyst must deal with security incidents in a heterogeneous network environment. To understand why normalization is helpful consider a typical enterprise environment, which consists of many different types of network devices ranging from border routers and VPN devices, to firewalls and authentication servers, and a wide range of application servers such as web servers, e-mail servers and database servers. Each of these devices generates logs that, as described above, are sources of data to a security analyst. However, it is seldom, if ever, the case that two manufactures will use the same event logging mechanism or format their event logs identically. For example a Cisco Systems PIX™ firewall will not report an accepted packet in the same way as a Check Point firewall or even in the same fashion as a Cisco Systems router.
An example of the types of various reports that might be generated by different network devices is presented below in Table 1, which shows examples of logs from different network devices, each reporting the same packet traveling across a network. In particular, these logs represent a remote printer buffer overflow that connects to IIS servers over port 80.
The Check Point record contains the following fields: event id, date, time, firewall interface, IP address of the firewall interface, logging facility, action, service, source IP, target IP, protocol, source port, some Check Point specific fields and then the size of the datagram. This report is, to say the least, difficult for a human analyst to read (especially with all the empty fields that are represented by double quotes). The Cisco router has a different format: date, time, logging facility, event name, source IP, source port, target address, target port, and number of packets. The Cisco PIX firewall, which is produced by the same manufacturer as the router, uses yet another format: date, time, event name, source IP, source port, translated address or target address, target port, local address, and local port.
The final record is a Snort alert that claims this traffic was malicious. Snort is a well-known IDS and the fields it populates are: exploit or event name, classification, priority, date, time, source IP, source port, target IP, target port, protocol, TTL (time to live), type of service, ID, IP length, datagram length, tcp flags, sequence number, acknowledgement number, window size, and tcp length. Snort also reports additional data such as references to investigate the exploit.
Agents 12 may be deployed in connection with some or all of these (and other) network components and applications. For example, in
Many normalized schemas can be used and, in general, choosing the fields of a common schema may be based on content rather than semantic differences between device logs and/or manufacturers. To accomplish this normalization, agents 12 are equipped with a parser configured to extract values from the events as reported by the individual network devices/applications and populate the corresponding fields in the normalized schema. Table 2 is an example of a normalized schema for the data reported by the devices in Table 1.
Table 2 reports the same four events described earlier, this time in a normalized fashion. Each of the agents 12 is configured to extract the relevant data from events reported by its associated network device/application and map that data to the corresponding common schema representation. For instance the Check Point firewall reports a target port as www-http, not as port 80 as is the case for most other network devices. Therefore an agent 12 associated with the Check Point firewall is configured with an appropriate lookup mechanism (e.g., a table) to ensure that “www-http” as reported by the firewall gets translated into “port 80” when the agent 12 reports the event to the manager 14.
Similarly, the agents 12 may need to be configured to convert the date/time stamp formats used by the various network devices/applications into a common date/time representation. That is, because the different network devices/applications all use different date/time formats, the agents cannot simply report the date/time stamps reported by the device/application. Instead, the agents 12 may be configured to convert local date/time stamps to a universal date/time notation, such as Greenwich Mean Time.
In addition to normalizing event data by fields, agents 12 can parse the event data stream and set field values based on conventions and practices of the organization. For example, the variety of event severity levels that devices produce can all be normalized at the agent level into a single, consistent hierarchy.
Thus, agents 12 collect and process events generated by heterogeneous network devices/applications throughout an enterprise. Alerts can come from routers, e-mail logs, anti-virus products, firewalls, intrusion detection systems, access control servers, VPN systems, NT Event Logs, Syslogs, and other sources where security threat information is detected and reported. In some embodiments, each event generator has an agent 12 assigned to collect all relevant security information, while in other embodiments agents are shared among two or more event generators. Thus, depending on the device/application to be monitored and the in-place infrastructure, a choice is provided for simple log parsing and loading, network listening (e.g., through SNMP traps), installation on aggregation points (Syslog servers and concentrators) and full distribution to all security-relevant devices.
In addition to collecting and normalizing data from security devices, the agents 12 intelligently manage the data with:
Thereafter, the event data is normalized at step 34. As indicated above, the normalization may occur at the field and/or the field value level. Further, the normalization may involve translation of the field values into nomenclatures/formats used across an enterprise.
Following normalization, the event data may, optionally, be aggregated (step 36) before being transmitted to the manager 14 (step 38). The transmissions may occur as the events are captured or may be made on a batched basis. In either case, the messages used to transmit the event data preferably include all of the source fields of an event. By delivering the entire event data set (i.e., all of the source fields) organized in a consistent format (i.e., the common schema), powerful upstream data management, cross-correlation, display and reporting is available to the security team. In some embodiments the event data is discarded after successful transmission to the manager 14, but in other cases the data may be cached for a time at the agent 12 to permit later replay of the data.
Referring again to
Once the event data messages have been received (and if necessary decrypted), the event data is passed to the rules engine 18. Rules engine 18 is at the heart of the manager 14 and is used to cross-correlate the event data with security rules in order to generate meta-events. Meta-events, in the context of the present invention, are instances of (usually) multiple individual event data elements (gathered from heterogeneous sources) that collectively satisfy one or more rule conditions such that an action is triggered. Stated differently, the meta-events represent information gathered from different sensors and presented as correlated results (i.e., the decision output of the rules engine 18 indicating that different events from different sources are associated with a common incident as defined by one or more rules).
The actions triggered by the rules may include notifications transmitted (e.g., via notifier 24) to designated destinations (e.g., security analysts may be notified via the consoles 16, e-mail messages, a call to a telephone, cellular telephone, voicemail box and/or pager number or address, or by way of a message to another communication device and/or address such as a facsimile machine, etc.) and/or instructions to network devices (e.g., via agents 12 or via external scripts or programs to which the notifier 24 may pass arguments) to take action to thwart a suspected attack (e.g., by reconfiguring one or more of the network devices, and/or modifying or updating access lists, etc.). The information sent with the notification can be configured to include the most relevant data based on the event that occurred and the requirements of the analyst.
As discussed below, when meta-events are generated by the rules engine 18, on-screen notifications may be provided to consoles 16 to prompt users to open cases for investigation of the events which led to the notification. This may include accessing knowledge base 28 to gather information regarding similar attack profiles and/or to take action in accordance with specified procedures. The knowledge base 28 contains reference documents (e.g., in the form of web pages and/or downloadable documents) that provide a description of the threat, recommended solutions, reference information, company procedures and/or links to additional resources. Indeed, any information can be provided through the knowledge base 28. By way of example, these pages/documents can have as their source: user-authored articles, third-party articles, and/or security vendors' reference material.
The rules engine 18 is based on a RETE engine configured to pyeserve event information state over configurable time windows so as to provide correlation of the event data according to specified rules. Correlation is generally regarded as a process of bringing information items into mutual relation. In the context of the present invention, correlation through rules engine 18 provides the ability to access, analyze, and relate different attributes of events from multiple sources to bring something to the attention of an analyst that might (or likely would) have otherwise gone unnoticed. In other words, the rules engine 18 provides the ability to determine what type of incident is represented by a collection of events reported by a number of heterogeneous network devices and/or applications. Because the collected event data is normalized into a common event schema, correlation can be performed utilizing any field including, but not limited to, geography, device type, source, target, time thresholds, and/or event type. Based on alerts generated by the rules engine 18, operators are provided with a workflow for investigating these incidents.
Turning to
Rules may be created at the manager 14 and/or at the consoles 16 using a flexible scripting language. An example of a rule might be:
In general, the rules may be designed to capture threats and attacks that are typical in large, diverse networks and may be organized to provide multiple lines of defense by detecting specific activities and grouping them according to level of threat:
After the events are processed by rules engine 18, the raw event data as well as any meta-events that were generated are stored in database 20 (step 48). In some embodiments, the raw event data may be stored prior to or concurrently with processing of the data by rules engine 18. Regardless of the sequence, such storage of the event data (and the meta-events generated by the rules engine 18) preserves a historical record of the event traffic and allows for replaying of the events through an existing or a new rule set (either at the manager 14 or the consoles 16) in order to assess the efficacy of new rules, for training purposes, and/or for case investigation.
Correlation via the rules ensures that credible threats and attacks come to the attention of the security staff on a high-priority basis. Hence once an alert is received, the operator can perform in-depth analysis and take aggressive action secure in the knowledge that the effort is well spent. When a rule match is reported to a console 16, the analyst can quickly drill down (through an associated graphical user interface) to see all of the individual events that caused the rule to fire. If necessary, the analyst can investigate even further to see all of the individual data elements captured for those events.
When action is required, the present invention provides a full set of tools and services for the operator. Resources such as the rule definition, a knowledge base article containing company policies and recommended actions, and the development of a complete case docket describing the problem assist the operator in responding immediately to critical security threats. If necessary, the operator can proactively deal with an attack by launching specific applications or scripts from the console 16 to reconfigure device settings or change access privileges.
The console 16 provides a centralized view into the security status of an enterprise and gives administrators, analysts, and operators an interface to perform security management tasks.
As discussed above, Toolbar 1 includes playback controls.
After selecting the time period the user wishes to playback, using VCR controls 510, the user can play, stop, pause, rewind, fast forward, jump to the beginning of the stream and jump to the end of a playback stream. These functions are extended to a real-time (i.e., live) event stream as well, with the limitation that a user cannot forward the stream beyond the current time into the future. In addition to analyzing events in real-time and replay, a user has the option to monitor events in a query mode and replay with rules mode. These additional modes are used to test the security of the system and test the efficiency of user-defined rules.
Additionally, playback control 500 includes radar 520. Radar 520 is an event distribution graph for a user-defined time period. Radar 520 includes vertical bars that indicate a volume of events and is color-coded to indicate the severity of the events described by the graph, as described above. In alternate embodiments, radar 520 can be color coded by other attributes such as target port, event category, or similar attributes. Specific bars can be selected and blown up to show greater detail of those events. In order to show the time at which the events occurred, radar 520 includes slider 521. With slider 521, a user can go to any point in time represented on the radar by sliding the slider 521 over the point in time on the graph. Users can virtually detect time-based event patterns.
As the slider 521 moves across the radar display 520, events are populated in the views panel 4 in correlation to the slider's 521 movement. The slider can be moved, expanded, or narrowed to select which events are populated in the views panel 4, which will be described now.
Grid display 600 is one view that a user can select to be presented with events 650. Console 16 is architected so that developers can easily build views that allow for different visualizations of data. For example, three-dimensional views can be developed and plugged into console 16.
Though there are varying information types and degrees of information gathered from devices, the information is normalized, contained, and stored as data fields, for consistent investigative and analytical comparison of all events, regardless of the device.
Once security personnel have been notified of an event, they can utilize the knowledge base to determine the appropriate actions. In addition, security analysts may undertake investigations of events and/or meta-events. In general, such matters can be assigned to so-called cases. Stated differently, cases create a workflow and oversight environment for situations where there are suspicious events requiring further investigation. Once a case is created, it can be assigned to an operator, investigated, and resolved based on the business policies and practices of the enterprise (e.g., as documented in knowledge base 28). The security staff can also add narration and event information to a case, or view open cases to determine their status and any required next steps.
Consoles 16 also provide a front-end for the administration of the entire system 10. This may include system configuration such as setting up operators, notification, agent behavior, etc. User management (such as creating and modifying users, access, roles, and responsibilities), rules management (e.g., authoring, viewing, and updating rules), and workflow management (e.g., setting up the flow of actions taken when an event is received) may also be handled through the consoles 16. Finally, the consoles 16 allow for remote access, thus supporting divisional responsibility and “follow-the-sun” management.
Thus, a computer-based system for capturing, correlating and reporting security events from heterogeneous sources has been described. More specifically, included in the system is a user interface for a network security console that is associated with the heterogeneous sources. In the foregoing description, the various examples and embodiments were meant to be illustrative of the present invention and not restrictive in terms of their scope. Accordingly, the invention should be measured only in terms of the claims, which follow.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4513743 | Van Arragon et al. | Apr 1985 | A |
| 5431691 | Snell et al. | Jul 1995 | A |
| 5557742 | Smaha et al. | Sep 1996 | A |
| 5717919 | Kodavalla et al. | Feb 1998 | A |
| 5781108 | Jacob et al. | Jul 1998 | A |
| 5850516 | Schneier | Dec 1998 | A |
| 5956404 | Schneier et al. | Sep 1999 | A |
| 5974341 | Er et al. | Oct 1999 | A |
| 5977913 | Christ | Nov 1999 | A |
| 5978475 | Schneier et al. | Nov 1999 | A |
| 6069655 | Seeley et al. | May 2000 | A |
| 6070244 | Orchier et al. | May 2000 | A |
| 6134664 | Walker | Oct 2000 | A |
| 6192034 | Hsieh et al. | Feb 2001 | B1 |
| 6275942 | Bernhard et al. | Aug 2001 | B1 |
| 6321338 | Porras et al. | Nov 2001 | B1 |
| 6400307 | Fullerton et al. | Jun 2002 | B2 |
| 6405133 | Schwinn et al. | Jun 2002 | B1 |
| 6408391 | Huff et al. | Jun 2002 | B1 |
| 6408404 | Ladwig | Jun 2002 | B1 |
| 6473104 | Harris | Oct 2002 | B1 |
| 6473794 | Guheen et al. | Oct 2002 | B1 |
| 6484203 | Porras et al. | Nov 2002 | B1 |
| 6542075 | Barker et al. | Apr 2003 | B2 |
| 6549208 | Maloney et al. | Apr 2003 | B2 |
| 6567808 | Eschelbeck et al. | May 2003 | B1 |
| 6574737 | Kingsford et al. | Jun 2003 | B1 |
| 6661340 | Saylor et al. | Dec 2003 | B1 |
| 6694362 | Secor et al. | Feb 2004 | B1 |
| 6704874 | Porras et al. | Mar 2004 | B1 |
| 6708212 | Porras et al. | Mar 2004 | B2 |
| 6711615 | Porras et al. | Mar 2004 | B2 |
| 6756998 | Bilger | Jun 2004 | B1 |
| 6839850 | Campbell et al. | Jan 2005 | B1 |
| 6871284 | Cooper et al. | Mar 2005 | B2 |
| 6928556 | Black et al. | Aug 2005 | B2 |
| 6952779 | Cohen et al. | Oct 2005 | B1 |
| 6965313 | Saylor et al. | Nov 2005 | B1 |
| 6966015 | Steinberg et al. | Nov 2005 | B2 |
| 6971026 | Fujiyama et al. | Nov 2005 | B1 |
| 6988208 | Hrabik et al. | Jan 2006 | B2 |
| 6993790 | Godwin et al. | Jan 2006 | B2 |
| 7007104 | Lewis et al. | Feb 2006 | B1 |
| 7039953 | Black et al. | May 2006 | B2 |
| 7043727 | Bennett et al. | May 2006 | B2 |
| 7089428 | Farley et al. | Aug 2006 | B2 |
| 7113090 | Saylor et al. | Sep 2006 | B1 |
| 7127743 | Khanolkar et al. | Oct 2006 | B1 |
| 7159237 | Schneier et al. | Jan 2007 | B2 |
| 7171689 | Beavers | Jan 2007 | B2 |
| 7219239 | Njemanze et al. | May 2007 | B1 |
| 7260844 | Tidwell et al. | Aug 2007 | B1 |
| 7278160 | Black et al. | Oct 2007 | B2 |
| 7308689 | Black et al. | Dec 2007 | B2 |
| 7333999 | Njemanze | Feb 2008 | B1 |
| 7340776 | Zobel et al. | Mar 2008 | B2 |
| 7376969 | Njemanze et al. | May 2008 | B1 |
| 20020019945 | Houston et al. | Feb 2002 | A1 |
| 20020066034 | Schlossberg et al. | May 2002 | A1 |
| 20020075170 | Schwinn et al. | Jun 2002 | A1 |
| 20020099958 | Hrabik et al. | Jul 2002 | A1 |
| 20020147803 | Dodd et al. | Oct 2002 | A1 |
| 20020184532 | Hackenberger et al. | Dec 2002 | A1 |
| 20030084349 | Friedrichs et al. | May 2003 | A1 |
| 20030093514 | Valdes et al. | May 2003 | A1 |
| 20030093692 | Porras | May 2003 | A1 |
| 20030101358 | Porras et al. | May 2003 | A1 |
| 20030188189 | Desai et al. | Oct 2003 | A1 |
| 20030221123 | Beavers | Nov 2003 | A1 |
| 20040010718 | Porras et al. | Jan 2004 | A1 |
| 20040024864 | Porras et al. | Feb 2004 | A1 |
| 20040044912 | Connary et al. | Mar 2004 | A1 |
| 20040049577 | Imhof | Mar 2004 | A1 |
| 20040137915 | Diener et al. | Jul 2004 | A1 |
| 20040221191 | Porras et al. | Nov 2004 | A1 |
| 20050027845 | Secor et al. | Feb 2005 | A1 |
| 20050204404 | Hrabik et al. | Sep 2005 | A1 |
| 20050251860 | Saurabh et al. | Nov 2005 | A1 |
| 20060069956 | Steinberg et al. | Mar 2006 | A1 |
| 20060212932 | Patrick et al. | Sep 2006 | A1 |
| 20070118905 | Morin et al. | May 2007 | A1 |
| 20070136437 | Shankar et al. | Jun 2007 | A1 |
| 20070150579 | Morin et al. | Jun 2007 | A1 |
| 20070162973 | Schneier et al. | Jul 2007 | A1 |
| 20070169038 | Shankar et al. | Jul 2007 | A1 |
| 20070234426 | Khanolkar et al. | Oct 2007 | A1 |
| 20070260931 | Aguilar-Macias et al. | Nov 2007 | A1 |
| 20080104046 | Singla et al. | May 2008 | A1 |
| 20080104276 | Lahoti et al. | May 2008 | A1 |
| 20080162592 | Huang et al. | Jul 2008 | A1 |
| 20080165000 | Morin et al. | Jul 2008 | A1 |
| Number | Date | Country |
|---|---|---|
| WO 0245315 | Jun 2002 | WO |
| WO 02060117 | Aug 2002 | WO |
| WO 02078262 | Oct 2002 | WO |
| WO 02101988 | Dec 2002 | WO |
| WO 2003009531 | Jan 2003 | WO |
| WO 2004019186 | Mar 2004 | WO |
