SYSTEMS AND METHODS FOR PROCESSING DATA FLOWS

• US 20080162390A1
• Filed: 10/24/2007
• Published: 07/03/2008
• Est. Priority Date: 09/25/2000
• Status: Active Grant

• Current Assignee: CA, Inc. (d/b/a CA Technologies) (Broadcom, Inc.)
• Original Assignee: Blue Coat Systems Incorporated (Gen Digital Inc.)
• Inventors: Justus, Stephen D., Ferguson, JC, Akerman, Moisey, Kapoor, Harsh, Martin, Timothy M., Fu, Chunsheng, Xu, Weidong, Korsunsky, Yevgeny, Gallo, Paul S., Lee, Charles Ching
• Granted Patent: US 8,402,540 B2
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06F 21/55 Detecting local intrusion or implementing counter-measures
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06F 9/505 considering the load
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: H04L 2463/141 Denial of service attacks against endpoints in a network
• CPC Class Codes: H04L 63/0227 Filtering policies mail message filtering H04L51/212
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1416 Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1441 Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/145 the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
• CPC Class Codes: H04L 63/1483 service impersonation, e.g. phishing, pharming or web spoofing detection of rogue wireless access points H04W12/12
• CPC Class Codes: H04L 63/164 at the network layer
• CPC Class Codes: H04L 63/166 at the transport layer
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/34 involving the movement of software or configuration parameters network booting or remote initial program loading [RIPL] G06F9/4416
• CPC Class Codes: H04L 67/62 Establishing a time schedule for servicing the requests
• CPC Class Codes: H04L 67/63 Routing a service request depending on the request content or context
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 : Network security protocols

Abstract

A flow processing facility, which uses a set of artificial neurons for pattern recognition, such as a self-organizing map, in order to provide security and protection to a computer or computer system supports unified threat management based at least in part on patterns relevant to a variety of types of threats that relate to computer systems, including computer networks. Flow processing for switching, security, and other network applications, including a facility that processes a data flow to address patterns relevant to a variety of conditions are directed at internal network security, virtualization, and web connection security. A flow processing facility for inspecting payloads of network traffic packets detects security threats and intrusions across accessible layers of the IP-stack by applying content matching and behavioral anomaly detection techniques based on regular expression matching and self-organizing maps. Exposing threats and intrusions within packet payload at or near real-time rates enhances network security from both external and internal sources while ensuring security policy is rigorously applied to data and system resources. Intrusion Detection and Protection (IDP) is provided by a flow processing facility that processes a data flow to address patterns relevant to a variety of types of network and data integrity threats that relate to computer systems, including computer networks.

Claims

1. A method in a flow processing facility for securing a computer resource, comprising:

receiving a data flow;

employing a set of artificial neurons to make a determination, the determination indicating which of a plurality of patterns is present in the data flow;

accessing a configuration, the configuration associating zero or more actions with each pattern of the plurality of patterns;

executing the actions that are associated with the patterns that the determination indicates, the actions modifying the data flow; and

transmitting the data flow.