Abstract: A system can monitor the server for indications of an attack and adjusts server settings accordingly. In response, the system can increase server tolerance in a systematic way to deal with DDoS by adjusting server settings appropriately. Conversely, when the server is not under attack, the settings can be adjusted to those for standard operations (e.g., adjusted downward), as they are more optimal for normal, non-attack operations.

Abstract: Embodiments are provided for an actionable blacklist of DDoS offenders and ISPs associated offenders. The system can collect real-time attack data and perform real-time analysis, which can be fed into a centralized database for intelligent analysis to identify offenders and report to interested subscribers. The system can receive an indication that network resources are being targeted as part of one or more DDoS attacks, and then obtain the malicious IP address of devices associated with those DDoS attacks. The system can determine the Internet Service Provider (ISP) associated with malicious IP addresses. A metric can be computed that is associated with an ISP involved in the one or more DDoS attacks. If the metric exceeds a threshold, then an alert message indicating that the first ISP is involved in the one or more DDoS attacks can be sent to a list of subscribers.

Abstract: Embodiments can identify requests that may be tied to a DDOS attack. For example, the primary identifiers (e.g., a source address) of requests for a network resource (e.g., an entire website or a particular element of the website) can be tracked. In one embodiment, a statistical analysis of how often a particular source address (or other primary identifier) normally makes a request can be used to identify source addresses that make substantially more requests. A normal amount can correspond to an average number of request that a source address makes. According to some embodiments, a system can use statistical analysis methods on various request data in web server logs to identify potential attacks and send data concerned potential attacks to an HBA system for further analysis.

Abstract: Embodiments are provided for managing routes of data traffic within a network. The management may be performed via a graphical user interface that interacts with a Web server to update a configuration file. The configuration file can be converted to router management commands by a network management device (e.g., a BGP speaker). The commands can then be sent to border routers for controlling network traffic. Embodiments are also provided for capturing and logging routing updates made in a network.

Abstract: A system can monitor the server for indications of an attack and adjusts server settings accordingly. In response, the system can increase server tolerance in a systematic way to deal with DDoS by adjusting server settings appropriately. Conversely, when the server is not under attack, the settings can be adjusted to those for standard operations (e.g., adjusted downward), as they are more optimal for normal, non-attack operations.

Abstract: Embodiments provide system and methods for a DDoS service using a mix of mitigation systems (also called scrubbing centers) and non-mitigation systems. The non-mitigation systems are less expensive and thus can be placed at or near a customer's network resource (e.g., a computer, cluster of computers, or entire network). Under normal conditions, traffic for a customer's resource can go through a mitigation system or a non-mitigation system. When an attack is detected, traffic that would have otherwise gone through a non-mitigation system is re-routed to a mitigation system. Thus, the non-mitigation systems can be used to reduce latency and provide more efficient access to the customer's network resource during normal conditions. Since the non-mitigation servers are not equipped to respond to an attack, the non-mitigation systems are not used during an attack, thereby still providing protection to the customer network resource using the mitigation systems.

Abstract: Embodiments are provided for an actionable blacklist of DDoS offenders and ISPs associated offenders. The system can collect real-time attack data and perform real-time analysis, which can be fed into a centralized database for intelligent analysis to identify offenders and report to interested subscribers. The system can receive an indication that network resources are being targeted as part of one or more DDoS attacks, and then obtain the malicious IP address of devices associated with those DDoS attacks. The system can determine the Internet Service Provider (ISP) associated with malicious IP addresses. A metric can be computed that is associated with an ISP involved in the one or more DDoS attacks. If the metric exceeds a threshold, then an alert message indicating that the first ISP is involved in the one or more DDoS attacks can be sent to a list of subscribers.

Abstract: Embodiments can identify requests that may be tied to a DDOS attack. For example, the primary identifiers (e.g., a source address) of requests for a network resource (e.g., an entire website or a particular element of the website) can be tracked. In one embodiment, a statistical analysis of how often a particular source address (or other primary identifier) normally makes a request can be used to identify source addresses that make substantially more requests. A normal amount can correspond to an average number of request that a source address makes. According to some embodiments, a system can use statistical analysis methods on various request data in web server logs to identify potential attacks and send data concerned potential attacks to an HBA system for further analysis.

Abstract: Embodiments are provided for managing routes of data traffic within a network. The management may be performed via a graphical user interface that interacts with a Web server to update a configuration file. The configuration file can be converted to router management commands by a network management device (e.g., a BGP speaker). The commands can then be sent to border routers for controlling network traffic. Embodiments are also provided for capturing and logging routing updates made in a network.

Abstract: Embodiments provide system and methods for a DDoS service using a mix of mitigation systems (also called scrubbing centers) and non-mitigation systems. The non-mitigation systems are less expensive and thus can be placed at or near a customer's network resource (e.g., a computer, cluster of computers, or entire network). Under normal conditions, traffic for a customer's resource can go through a mitigation system or a non-mitigation system. When an attack is detected, traffic that would have otherwise gone through a non-mitigation system is re-routed to a mitigation system. Thus, the non-mitigation systems can be used to reduce latency and provide more efficient access to the customer's network resource during normal conditions. Since the non-mitigation servers are not equipped to respond to an attack, the non-mitigation systems are not used during an attack, thereby still providing protection to the customer network resource using the mitigation systems.

Abstract: Embodiments can identify requests that may be tied to a DDOS attack. For example, the primary identifiers (e.g., a source address) of requests for a network resource (e.g., an entire website or a particular element of the website) can be tracked. In one embodiment, a statistical analysis of how often a particular source address (or other primary identifier) normally makes a request can be used to identify source addresses that make substantially more requests. A normal amount can correspond to an average number of request that a source address makes. According to some embodiments, a system can use statistical analysis methods on various request data in web server logs to identify potential attacks and send data concerned potential attacks to an HBA system for further analysis.

Abstract: Embodiments can identify requests that may be tied to a DDOS attack. For example, the primary identifiers (e.g., a source address) of requests for a network resource (e.g., an entire website or a particular element of the website) can be tracked. In one embodiment, a statistical analysis of how often a particular source address (or other primary identifier) normally makes a request can be used to identify source addresses that make substantially more requests. A normal amount can correspond to an average number of request that a source address makes. According to some embodiments, a system can use statistical analysis methods on various request data in web server logs to identify potential attacks and send data concerned potential attacks to an HBA system for further analysis.

Abstract: Embodiments provide system and methods for a DDoS service using a mix of mitigation systems (also called scrubbing centers) and non-mitigation systems. The non-mitigation systems are less expensive and thus can be placed at or near a customer's network resource (e.g., a computer, cluster of computers, or entire network). Under normal conditions, traffic for a customer's resource can go through a mitigation system or a non-mitigation system. When an attack is detected, traffic that would have otherwise gone through a non-mitigation system is re-routed to a mitigation system. Thus, the non-mitigation systems can be used to reduce latency and provide more efficient access to the customer's network resource during normal conditions. Since the non-mitigation servers are not equipped to respond to an attack, the non-mitigation systems are not used during an attack, thereby still providing protection to the customer network resource using the mitigation systems.

Abstract: A system can monitor the server for indications of an attack and adjusts server settings accordingly. In response, the system can increase server tolerance in a systematic way to deal with DDoS by adjusting server settings appropriately. Conversely, when the server is not under attack, the settings can be adjusted to those for standard operations (e.g., adjusted downward), as they are more optimal for normal, non-attack operations.

Abstract: Embodiments provide system and methods for a DDoS service using a mix of mitigation systems (also called scrubbing centers) and non-mitigation systems. The non-mitigation systems are less expensive and thus can be placed at or near a customer's network resource (e.g., a computer, cluster of computers, or entire network). Under normal conditions, traffic for a customer's resource can go through a mitigation system or a non-mitigation system. When an attack is detected, traffic that would have otherwise gone through a non-mitigation system is re-routed to a mitigation system. Thus, the non-mitigation systems can be used to reduce latency and provide more efficient access to the customer's network resource during normal conditions. Since the non-mitigation servers are not equipped to respond to an attack, the non-mitigation systems are not used during an attack, thereby still providing protection to the customer network resource using the mitigation systems.

Abstract: A system can monitor the server for indications of an attack and adjusts server settings accordingly. In response, the system can increase server tolerance in a systematic way to deal with DDoS by adjusting server settings appropriately. Conversely, when the server is not under attack, the settings can be adjusted to those for standard operations (e.g., adjusted downward), as they are more optimal for normal, non-attack operations.

Abstract: Embodiments are provided for managing routes of data traffic within a network. The management may be performed via a graphical user interface that interacts with a Web server to update a configuration file. The configuration file can be converted to router management commands by a network management device (e.g., a BGP speaker). The commands can then be sent to border routers for controlling network traffic. Embodiments are also provided for capturing and logging routing updates made in a network.

Abstract: Embodiments are provided to allow institutions and individuals the ability to subscribe to notifications regarding DDoS attacks on certain types of institutions, e.g., institutions that fall into certain market verticals or categories. Subscriber data can be integrated with an attack monitoring platform (AMP), which can push out certain information to a system, which tabulates and analyzes the information regarding attacks. If an attack is detected based on predefined thresholds and conditions, a database lookup is performed and a notification that contains certain attack attributes can be sent out to the appropriate subscribers. Subscribers can also be provided with a mechanism to modify their level of DDoS attack notification threshold.

Abstract: Embodiments are provided for an actionable blacklist of DDoS offenders and ISPs associated offenders. The system can collect real-time attack data and perform real-time analysis, which can be fed into a centralized database for intelligent analysis to identify offenders and report to interested subscribers. The system can receive an indication that network resources are being targeted as part of one or more DDoS attacks, and then obtain the malicious IP address of devices associated with those DDoS attacks. The system can determine the Internet Service Provider (ISP) associated with malicious IP addresses. A metric can be computed that is associated with an ISP involved in the one or more DDoS attacks. If the metric exceeds a threshold, then an alert message indicating that the first ISP is involved in the one or more DDoS attacks can be sent to a list of subscribers.

Abstract: Embodiments can identify requests that may be tied to a DDOS attack. For example, the primary identifiers (e.g., a source address) of requests for a network resource (e.g., an entire website or a particular element of the website) can be tracked. In one embodiment, a statistical analysis of how often a particular source address (or other primary identifier) normally makes a request can be used to identify source addresses that make substantially more requests. A normal amount can correspond to an average number of request that a source address makes. According to some embodiments, a system can use statistical analysis methods on various request data in web server logs to identify potential attacks and send data concerned potential attacks to an HBA system for further analysis.