Abstract:
The present invention relates to a system and method for interlocking intrusion information. An intrusion information interlocking system includes at least one interlocking client which is connected to a client system which collects session information of intrusion in different network domains to transmit the intrusion information collected by the client system to the control system and requests analysis information on the intrusion information in accordance with a request of the client system to provide the analysis information to the client system, and an interlocking server which is connected to a control system which analyzes intrusion information to transmit the intrusion information of different network domains provided from one or more interlocking clients to the control system, stores the intrusion analysis information from the control system, and shares the stored intrusion analysis information with the interlocking client in accordance with the request of the interlocking client.
Abstract:
A behavior-based malicious code detecting apparatus and method using multiple feature vectors is disclosed. A malicious code learning method may include collecting characteristic factor information when a training target process comprising a malicious code is executed, generating a feature vector for malicious code verification based on the collected characteristic factor information, learning the generated feature vector through a plurality of machine learning algorithms to generate a model of representing the malicious code and a model of representing a normal file, and storing the model of representing the malicious code and the model of representing the normal file generated through the learning.
Abstract:
Provided are abnormal behavior detecting apparatus and method and the abnormal behavior detecting apparatus, includes: a behavior analyzing unit which analyzes a behavior which occurs for resources of a system based on data collected from a process while the process is executed on the system; a behavior modeling unit which models a behavior analysis result for the resources of the system on a coordinate which is generated based on the behavior for the resources of the system to create a process behavior model corresponding to the resources of the system; a suspicious behavior determining unit which determines a suspicious behavior of the process in accordance with the type of the process behavior model which is implemented on the coordinate; and a process detecting unit which detects a process in which the suspicious behavior occurs as an abnormal behavior process.
Abstract:
An apparatus for detecting an abnormality sign in a control system, the control system comprising control equipments, network equipments, security equipments or server equipments, the apparatus includes an information collection module configured to collect system information, network information, security event information or transaction information in interworking with a control equipments, network equipments, security equipments or server equipments. The apparatus includes storage module that stores the information collected by the information collection module. The apparatus includes an abnormality detection module configured to analyze a correlation between the collected information and a prescribed security policy to detect whether there is an abnormality sign in the control system.
Abstract:
The present invention relates to an apparatus and a method for detecting a malware code by generating and analyzing behavior pattern. A malware code detecting apparatus includes a behavior pattern generating unit which defines a characteristic parameter which distinguishes and specifies behaviors of a malware code and normally executable programs, converts an API calling event corresponding to the defined characteristic parameter and generates a behavior pattern in accordance with a similarity for behaviors of converted API call sequences to store the behavior pattern in a behavior pattern DB; and a malware code detecting unit which converts the API calling event corresponding to the defined characteristic parameter when the target process is executed into the API call sequence and determines whether the behavior pattern is a malware code in accordance with a similarity for behaviors of the converted API call sequence and the sequence stored in the behavior pattern DB.
Abstract:
Disclosed is a method of generating secret information on the basis of a ring oscillator. According to an embodiment of the present disclosure, there is provided an apparatus for generating secret information on the basis of a ring oscillator, the apparatus including: multiple PUF information generation units each including at least one ring oscillator cell and generating physically unclonable function (PUF) information generated by the at least one ring oscillator cell; a phase checking unit cross-checking phases for the multiple pieces of the PUF information that are output from the multiple PUF information generation units, respectively; and a secret key generation unit outputting secret key information based on a result of comparing the multiple phases received from the phase checking unit.
Abstract:
A computing device configured to execute an instruction set is provided. The computing device includes a system call hooker for hooking system calls that occur by the instruction set while the instruction set is executed, a category extractor for extracting a category to which each of the hooked system calls belongs with reference to category information associated with a correspondence relationship between a system call and a category, a sequence extractor for extracting one or more behavior sequences expressed in an N-gram manner from a full sequence of the hooked system calls with reference to the extracted category, and a model generator for generating a behavior pattern model of the system calls that occur when the instruction set is executed, based on a number of times that each of the extracted behavior sequences occurs.
Abstract:
Disclosed are provided a method and a system for network connection chain traceback by using network flow data in order to trace an attack source site for cyber hacking attacks that goes by way of various sites without addition of new equipment of a network or modification a standard protocol when the cyber hacking attack occurs in the Internet and an internal network.
Abstract:
Disclosed herein a method and apparatus for detecting a malicious code based on an assembly language model. According to an embodiment of the present disclosure, there is provided a method for detecting a malicious code. The method comprising: generating an instruction code sequence by converting an input file, for which a malicious code is to be detected, into an assembly code; embedding the instruction code sequence by using a prelearned assembly language model for instruction code embedding and outputting an embedding result of the instruction code sequence; and detecting whether or not the input file is a malicious code, by using a prelearned malicious code classification model with the embedding result as an input.
Abstract:
Provided is a module and method for transmitting information using a wireless hidden signal, which is capable of transmitting important information data requiring extreme security using a wireless hidden signal, and allowing the important information to be detected and distinguished by only promised transmitting/receiving parties so that the possibility of the wireless hidden signal being discovered can be minimized and security can be enhanced. The module for transferring information using a wireless hidden signal includes: a hidden formatting unit configured to generate a transmission data frame structure based on data that needs to be wirelessly transmitted; a hidden encoding unit configured to encode the generated transmission data frame structure to generate and output a hidden encoded bit stream; and a hidden modulation unit configured to convert the output hidden encoded bit stream into a wireless signal in a wireless transmission format.