568 results about "Network intrusion detection" patented technology

An intrusion detection and analysis system and method are disclosed. The system includes a data monitoring device comprising a capture engine operable to capture data passing through the network and configured to monitor network traffic, decode protocols, and analyze received data. The system further includes an intrusion detection device comprising a detection engine operable to perform intrusion detection on data provided by the data monitoring device. Application program interfaces are provided and configured to allow the intrusion detection device access to applications of the data monitoring device to perform intrusion detection. The system also includes memory for storing reference network information used by the intrusion detection device to determine if an intrusion has occurred.

According to one embodiment of the invention, a method for automatically generating software code operable to detect a defined signature in network traffic comprises providing an inspector shell, generating a plurality of parameter name-value associations from provided configuration data, and automatically generating, by computer, an instance of the inspector shell having a signature defined by the parameter name-value associations.

In a method of determining whether a data stream includes unauthorized data, the data stream is analyzed using a hardware filter to detect a presence of one or more of a first set of patterns in the data stream. It is determined whether a packet in the data stream belongs to one of a plurality of data flows to be further inspected based on the analysis of the data stream by the hardware filter. A set of rules is applied to the packet to produce rule match status data if it is determined that the packet belongs to one of the plurality of data flows to be further inspected. The packet is analyzed to determine if the packet includes unauthorized data using software stored on a computer-readable medium and implemented on a processor if the rule match status data indicates that the packet potentially includes unauthorized data.

A system for providing intrusion detection in a network wherein data flows are exchanged using associated network ports and application layer protocols. The system includes a monitoring module configured for monitoring data flows in the network, a protocol identification engine configured for detecting information on the application layer protocols involved in the monitored data flows, and an intrusion detection module configured for operating based on the information on application layer protocols detected. Intrusion detection is thus provided independently of any predefined association between the network ports and the application layer protocols.

An intrusion detection system and method for a computer network includes a processor and one or more programs that run on the processor for application inspection of data packets traversing the computer network. The one or more programs also obtaining attribute information from the packets specific to a particular application and comparing the attribute information against a knowledge database that provides a baseline of normal network behavior. The processor raises an alarm whenever the attribute information exceeds a predetermined range of deviation from the baseline of normal network behavior. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

A method, system, and computer program product for detecting and mapping activity occurring at and between devices on a computer network for utilization within an intrusion detection mechanism. An enhanced graph matching intrusion detection system (eGMIDS) utility executing on a control server provides data collection functions and data fusion techniques. The eGMIDS comprises multiple sensors and associated unique adaptors that are located at different remote devices of the network and utilized to detect specific types of activity occurring at the respective devices relevant to eGMIDS processing. The sensors convert the data into eGMIDS format and encapsulate the data in a special transmission packet that is transmitted to the control server. The eGMIDS utility converts the activity data within these packets into eGMIDS-usable format and then processes the converted data via a data fusion technique to generate a graphical representation of the network (devices) and the activity occurring at / amongst the various devices.

A method and system are described for detecting unauthorized access to one or more of a plurality of networked victim computers in a victim cloud. The networked victim computers connect to one or more DNS servers. The system includes one or more decoy bot computers, which are operated as victim computers in the victim cloud. The system also includes one or more decoy control computers, which are operated as control computers that communicate with victim computers in the victim cloud. Threats are identified by analyzing data traffic communicated with the decoy bot computers and decoy control computers for information suspected of having being sent from a victim's computer without proper authorization, and by monitoring whether behavior of a DNS server deviates from expected behaviors.

The invention provides a method and apparatus for advanced network intrusion detection. The system uses deep packet inspection that can recognize languages described by context-free grammars. The system combines deep packet inspection with one or more grammar parsers (409A-409M). The invention can detect token streams (408) even when polymorphic. The system looks for tokens at multiple byte alignments and is capable of detecting multiple suspicious token streams (408). The invention is capable of detecting languages expressed in LL(I) or LR(I) grammar. The result is a system that can detect attacking code wherever it is located in the data stream (408).

Host computer systems automatically detect malicious code. The host computer systems automatically generate and send malicious code packets of the malicious code to a local analysis center (LAC) computer system. Based on the received malicious code packets, the LAC computer system provides a signature update to a network intrusion detection system. Further, the LAC computer system also automatically sends malicious code signatures of the malicious code to a global analysis center. In this manner, the spread of the malicious code is rapidly detected and prevented.

Systems and method of the present disclosure are directed to a network security tool. In some embodiments, the tool identifies a current vulnerability of a private network. The tool can determine a signature of an attack configured to exploit the current vulnerability. The tool can comparing the signature with active and inactive signatures stored in a signature repository. The tool can compare the signatures to identify an inactive signature corresponding to the signature of the attack configured to exploit the current vulnerability. The tool can automatically activate, responsive to the comparison, the identified inactive signature. The tool can use the activated signature to identify an exploit based on data packets received via the private network.

A network security system employing multiple levels of processing to identify security threats. Multiple host machines may each contain an agent that detects possibilities of security threats based on raw data sensed locally at that host. The hosts may share information obtained from local analysis and each host may use information generated at one or more other hosts, in combination with information generated locally, to identify a security concern, indicating with greater certainty that a security threat exists. Based on security concerns generated by multiple hosts, a security threat may be indicated and protective action may be taken.

The invention relates to multilayer honey-net data transmission method and system, and the gateway of honey-net receives the data flow of external network; the gateway of honey-net detects the network intrusion for the received data flow; the normal data flow can pass, which is sent to the target host computer; and the informal data flow is divide into high, middle and low according to dangerous level; and the data flow with high level is sent to physical honey-tank system, the data flow with middle level is sent to virtual machine honey-tank system, and the data floe with low level is sent to virtual honey-tank system. The invention can utilize the advantages of low interactive honey-tank system and high interactive honey-tank system, save system source, and improve the covering area of honey-tank system and the ability of obtaining the movable information of network attack and capturing the malice code; it can defeat the anti-honey tank technique. And it can be used in security field of computer network.

Disclosed is an in-line mode network intrusion detecting and preventing system coupled between a protection network and an external network, for detecting intrusion states between the networks and preventing the intrusion. The system comprises a first network processor unit for monitoring the packets communicated between the networks to collect various statistical data, and performing a packet filtering process according to a packet preventing rule and a packet sensing process according to a sensing rule; and a second network processor unit for checking payloads of the packets with reference to attack signatures to detect the attack states to one of the networks.

In a system and method for detecting network intrusion, the system comprises: a packet capturer which captures at least one packet on a network; a preprocessor which provides feature values dependent on features of each packet captured by the packet capturer; and a learning engine for classifying patterns dependent on the feature values provided by the preprocessor into two different pattern sets, and for selecting one pattern set having more elements from the pattern sets as a reference set so as to detect network intrusion. The network intrusion detection system and method do not depend on historical data according to known attack patterns, and thus not only detect a changed attack pattern but also efficiently detect network intrusion.

The present invention relates to a network intrusion detection and prevention system. The system includes: a signature based detecting device; an anomaly behavior based detecting device; and a new signature creating and verifying device disposed between the signature based detecting device and the anomaly behavior based detecting device, wherein if the anomaly behavior based detecting device detects network-attack-suspicious packets, the new signature creating and verifying device collects and searches the detected suspicious packets for common information, and then creates a new signature on the basis of the searched common information and at the same time, verifies whether or not the created new signature is applicable to the signature based detecting device, and then registers the created new signature to the signature based detecting device if it is determined that the created new signature is applicable.

A network intrusion detection system (NIDS) works in conjunction with a distributed virtual switch fabric to provide enhanced network intrusion detection in a way that does not require as much human intervention, autonomically adjusts to hardware changes in the network, and responds much more quickly than known network intrusion detection systems. The NIDS accesses network information from the distributed virtual switch fabric, which gives the NIDS access to a virtual view that includes hardware information for all networking devices in the network. This allows the NIDS to automatically determine network topology, update itself as hardware in the network is added or changed, and promptly take automated service actions in response to detected network intrusions. The result is a NIDS that is easier to configure, maintain, and use, and that provides enhanced network security.

The invention discloses a deep leaning-based network intrusion detection and vulnerability scanning method and devices. The method comprises the steps of collecting malicious sample files and buildinga malicious file database; performing training modeling according to behaviors of malicious files in the malicious file database by using a deep learning algorithm, performing real-time monitored model incremental training according to received new malicious sample files, so as to obtain classified models; simulating and running the malicious sample files in the malicious file database in different environments, and detecting an attack characteristic of the malicious sample files by using IDS; and analyzing the malicious file database by using a data mining algorithm, building a vulnerabilityattack manner characteristic library, generating a network attack package, and scanning network vulnerabilities.

The invention, which belongs to the technical field of network intrusion detection, discloses an intrusion detection method and intrusion detection system based on sustainable ensemble learning. A multi-class regression model is constructed by using a class probability output and a classification confidence product of an individual learner as training data, so that the decision-making process of the ensemble learning has high adaptability to the attack type to improve the detection accuracy. At the model updating stage, parameters and decision results of historical models are added into the training process of a new model, thereby completing incremental learning of the model. According to the invention, on the basis of the ensemble learning fusion plan of the multi-regression model, the decision-making weights of the individual learner during the detection processes for different attack types are allocated in a fine granularity manner; and the parameters and results of the historical models are used for training the new model, so that the stability of the model is improved and the sustainability of the learning process is ensured. Besides, the experiment result is compared with theexisting MV and WMV plans, the accuracy, stability and sustainability of the intrusion detection method and intrusion detection system are verified.

Disclosed is a method for configuring an intrusion detection system in a network which comprises determining a location in the network for a deployed intrusion detection sensor of the intrusion detection system, deploying the intrusion detection sensor in the determined location, enabling the intrusion detection sensor to monitor communication in a portion of the network, tuning the intrusion detection sensor to an appropriate level of awareness of the content in the communication in the network, prioritizing responses generated by the intrusion detection sensor to achieve an appropriate response to a detected intrusion in the network, configuring intrusion response mechanisms in the network to achieve an appropriate response by the mechanisms; and re-tuning the intrusion detection sensor in response to a prior intrusion detection.