Abstract: The present invention relates to a coating composition which is for a wiper blade and is for forming a coating layer for lowering the coefficient of friction of a wiper blade rubber base on glass, the coating composition including a lubricant additive and a solvent, wherein the lubricant additive is functionalized graphene capable of self-adhesion to the wiper blade rubber base.

Abstract: A skin care device according to an embodiment includes a main body and a head part connected to the main body, wherein the head part includes a housing in which an upper portion thereof is open, and including an accommodation space therein, an electrode part disposed in the housing, a plurality of light emitting elements disposed in the housing and spaced apart from the electrode part, and a cover part for shielding of the open upper region of the housing, wherein the cover part includes a hole corresponding to the electrode part, a part of the electrode part protrudes from an upper surface of the cover part via the hole, and an upper surface of the electrode part facing a skin of a user includes a convex curved surface toward the skin.

Abstract: A lens module includes a lens barrel in which a plurality of lenses are disposed, and a lens holder to which the lens barrel is coupled, wherein the lens holder includes a fastening portion fastening the lens barrel, the lens barrel includes a fastening blade coupled to the fastening portion of the lens holder, and the fastening portion includes a stepped portion preventing rotation in a direction, opposite to a direction in which the fastening blade rotates for fastening.

Abstract: A camera module includes: a magnet disposed on a lens module; a bearing member disposed in a ball guide portion formed between the lens module and a housing that accommodates the lens module; a coil disposed in the housing to face the magnet; and a yoke member disposed on the housing to interact with the magnet to generate a biased magnetic force.

Abstract: A camera module includes: a magnet disposed on a lens module; a bearing member disposed in a ball guide portion formed between the lens module and a housing that accommodates the lens module; a coil disposed in the housing to face the magnet; and a yoke member disposed on the housing to interact with the magnet to generate a biased magnetic force.

Abstract: A cosmetic device according to an embodiment of the present disclosure comprises: a main body; a head module that is detachable from the main body; a control unit for recognizing the head module mounted to the main body; and a communication unit for transmitting information about the recognized head module to a mobile terminal. The control unit can control the operation of the head module mounted to the main body differently according to the information received from the mobile terminal.

Abstract: The present disclosure is directed to providing a beauty device capable of caring various areas, and to effectively providing various functions during scalp care, and the beauty device may include a main body and a head module detachable from the main body, wherein the head module may include at least one of a plurality of protrusions, a light-emitting part, an electrode part, a sensor, and a motor, and the sensor may include a proximity sensor.

Abstract: A camera module includes: a lens module including a plurality of lenses; a housing accommodating the lens module; a reflection module disposed in front of the lens module; an image sensor module configured to receive light passing through the lens module; and a light blocking portion disposed in the housing and positioned in a space between the lens module and the image sensor module. The light blocking portion includes: a first light blocking plate including a first window having a first opening through which the light is allowed to pass; and a second light blocking plate including a second window having a second opening through which the light is allowed to pass. The first window includes a first inner wall including a first inclined surface, and the second window includes a second inner wall including a second inclined surface.

Abstract: Provided is a method performed by a computing device for classifying a type of exploit.

Abstract: Provided is a method performed by a computing device for monitoring an abnormal behavior of a plurality IoT devices. The method comprises determining abnormality of a behavior of each of the plurality of IoT devices based on traffic data representing the behavior of each of the plurality of IoT devices, clustering the behavior of each of the plurality of IoT devices based on the traffic data and a result of the determining the abnormality and generating data for representing a plurality of clusters formed as a result of the clustering such that a first cluster corresponding to a normal behavior cluster and a second cluster corresponding to an abnormal behavior cluster are displayed on different planes, the first cluster and the second cluster being divided based on the result of the determining the abnormality.

Abstract: Provided is a method performed by a computing device for identifying a device. The method include receiving a target packet from an identification target device, extracting a pattern of the target packet, the target packet being transmitted by the identification target device from the packet, matching the pattern of the target packet with at least one of packet patterns stored in an identification information DB, comparing a first model name with a second model name, the first model name being corresponding to the matched pattern stored in the identification information DB, the second model name being selected by a user of a user terminal, and transmitting a proposed model name to the user terminal based on determining that the first model name and the second model name are different, the proposed model name being used for connection between the user terminal and the identification target device.

Abstract: Provided is a method performed by a computing device for identifying a device. The method include receiving a target packet from an identification target device, extracting a pattern of the target packet, the target packet being transmitted by the identification target device from the packet, matching the pattern of the target packet with at least one of packet patterns stored in an identification information DB, comparing a first model name with a second model name, the first model name being corresponding to the matched pattern stored in the identification information DB, the second model name being selected by a user of a user terminal, and transmitting a proposed model name to the user terminal based on determining that the first model name and the second model name are different, the proposed model name being used for connection between the user terminal and the identification target device.

Abstract: Provided are a method for detecting an anomaly in devices, the method being performed by a computing device and comprising: acquiring operation information on a first device connected to a security management unit (SMU) of a first domain, and operation information on a second device connected to a SMU of a second domain, and detecting an anomaly in the first device and/or the second device by comparing the operation information on the first device with the operation information on the second device, wherein the SMU of the first domain is not directly connected to the SMU of the second domain.

Abstract: There is provided a method of generating malicious traffic, the method being performed by a computing apparatus and comprising obtaining traffic data transmitted from a first device infected with first malicious code or received by the first device, generating a traffic template of the first device by analyzing the traffic data, and generating a malicious traffic template of a terminal group, wherein the malicious traffic template of the terminal group comprises the traffic template of the first device.

Abstract: Provided is a method performed by a computing device for detecting abnormal behavior in a network. The method comprises obtaining a plurality of individual rules, wherein an individual rule of the plurality of individual rules is for extracting first output data from at least one input data set among a plurality of input data sets, the first output data satisfying a first extraction condition, obtaining a plurality of association rules, wherein an association rule of the plurality of association rules is for extracting second output data from at least one of the plurality of input data sets and the first output data, the second output data satisfying a second extraction condition and detecting abnormal behavior in a network based on third output data, the third output data being extracted using one of the plurality of individual rules and the plurality of association rules.

Abstract: A camera module includes: a lens module including a plurality of lenses; a housing accommodating the lens module; a reflection module disposed in front of the lens module; an image sensor module configured to receive light passing through the lens module; and a light blocking portion disposed in the housing and positioned in a space between the lens module and the image sensor module. The light blocking portion includes: a first light blocking plate including a first window having a first opening through which the light is allowed to pass; and a second light blocking plate including a second window having a second opening through which the light is allowed to pass. The first window includes a first inner wall including a first inclined surface, and the second window includes a second inner wall including a second inclined surface.

Abstract: An apparatus comprising a processor to execute the rule optimizer to perform a number of operations. One operation comprises obtaining 5 log data including a result of detecting an exploit attack based on a rule. Another operation comprises time-series analyzing the obtained log data to update at least some of previously applied detection rules. There is provided an apparatus for automatically optimizing a rule to improve the detection accuracy for an exploit attack in a rule-based attack detection system, and a method performed on the apparatus.

Abstract: An apparatus comprising a processor to execute the rule optimizer to perform a number of operations. One operation comprises obtaining 5 log data including a result of detecting an exploit attack based on a rule. Another operation comprises time-series analyzing the obtained log data to update at least some of previously applied detection rules. There is provided an apparatus for automatically optimizing a rule to improve the detection accuracy for an exploit attack in a rule-based attack detection system, and a method performed on the apparatus.

Abstract: Provided are methods and systems for identifying an Internet of things (IoT) device. A method of clustering an Internet of things (IoT) device, the method comprises determining a device group to which the IoT device belongs by applying the network packet of the IoT device obtained from the device identification apparatus to the clustering model, transmitting device group information according to the determination to the device identification apparatus and obtaining detailed identification information of the IoT device from the device identification apparatus, wherein the detailed identification information comprises a detailed model of the IoT device analyzed using the device group information.

Abstract: Provided is a method for managing abnormal behavior of an IoT device performed at an IoT gateway connected to the IoT device. The method comprises collecting a transmission packet transmitted by of the IoT device, calculating historical time series metrics for the IoT device using the collected packet, setting normal ranges of the time series metrics using at least one of a maximum value, a minimum value, and an average value of a curvature of a curve generated based on mapping the calculated historical time series metrics onto a two-dimensional plane, and determining whether current time series metric calculated using a received packet from the IoT device are out of the normal ranges.