Abstract:
Disclosed herein are an apparatus and method for detecting a malicious script. The apparatus includes one or more processors and executable memory for storing at least one program executed by the one or more processors. The at least one program is configured to extract token-type features, each of which corresponds to a lexical unit, and tree-node-type features of an abstract syntax tree from an input script, to train two learning models to respectively learn two pieces of learning data that are generated in consideration of features extracted respectively from the token-type features and the node-type features as having the highest frequency, and to detect whether the script is a malicious script based on the result of ensemble-based malicious script detection performed for the script, which is acquired using an ensemble detection model generated from the two learning models.
Abstract:
Disclosed herein are an apparatus and method for updating an Internet-based malware detection engine using virtual machine scaling. The method may include creating a scaling group and an update group set based on a first virtual machine image, creating a second virtual machine image for a running virtual machine in response to occurrence of a snapshot event in the virtual update group run based on the first virtual machine image, modifying the scale-out image of the scaling group to the second virtual machine image, updating the scaling group by triggering a scale-out event and a scale-in event in the scaling group in response to occurrence of an update event, and modifying the scale-in image of the scaling group to the second virtual machine image.
Abstract:
Disclosed herein are an apparatus for detecting unknown malware using a variable-length operation code (opcode) and a method using the apparatus. The method includes collecting opcode information from a detection target, generating a multi-pixel image having a variable length by performing feature engineering on the opcode information; and detecting unknown malware by inputting the multi-pixel image to a deep-learning model based on AI.
Abstract:
Disclosed herein is an apparatus and method that process knowledge, experience information, or the like possessed by group members via a dynamically created social group, in the form of collaborative storyboards, thus enabling the collaborative storyboards to be shared among a plurality of social groups, as well as the corresponding members. The presented apparatus includes a social group management unit for managing information about a social group and a user joining the social group as a member, and an information management unit for accepting information finally determined with respect to information of content desired to be shared, which is posted by the user on a storyboard of the social group, in collaboration with other users, as a post of the storyboard of the social group, and distributing the post to the social group.
Abstract:
Disclosed herein are a stepping-stone detection apparatus and method. The stepping-stone detection apparatus includes a target connection information reception unit for receiving information about a target connection from an intrusion detection system (IDS), a fingerprint generation unit for generating a target connection fingerprint based on the information about the target connection, and generating one or more candidate connection fingerprints using information about one or more candidate connections corresponding to one or more flow information collectors, and a stepping-stone detection unit for detecting a stepping stone by comparing the target connection fingerprint, in which a maximum allowable delay time is reflected, with the candidate connection fingerprints.
Abstract:
Disclosed herein are an integrated network data collection apparatus and method. The integrated network data collection apparatus includes a packet collection unit for collecting packets corresponding to one or more virtual machines included in a cloud server, a flow-processing unit for generating flow information based on the collected packets, a session-processing unit for generating session information based on the generated flow information, and a storage unit for storing network data including at least one of the generated flow information and the generated session information.
Abstract:
Disclosed herein are an apparatus and method for detecting a Distributed Reflection Denial of Service (DRDoS) attack. The DRDoS attack detection apparatus includes a network flow data reception unit for receiving network flow data from network equipment, a session type determination unit for determining a session type of the received network flow data, a host type determination unit for determining a type of host corresponding to the network flow data based on the session type, an attack method determination unit for determining an attack method corresponding to the network flow data, a protocol identification unit for identifying a protocol of the network flow data, and an attack detection unit for detecting a DRDoS attack based on the session type, the host type, the attack method, and the protocol.
Abstract:
An apparatus and a method for an attack source traceback capable of tracing back an attacker, that is, an attack source present behind a command and control (C&C) server in a cyber target attack having non-connectivity over a transmission control protocol (TCP) connection are disclosed. The apparatus for the attack source traceback includes: a server information extracting unit detecting an attack for a system, which is generated via a server to thereby extract information on the server; a traceback agent installing unit installing a traceback agent in the server based on the information on the server; and a traceback unit finding an attack source for the system by analyzing network information of the server obtained by the traceback agent.