Abstract: This engine structure includes: a cylinder block in which a cylinder bore is formed; a piston provided in the cylinder bore; a cylinder head provided to an upper portion of the cylinder block in a manner to seal the cylinder bore; and a ring that is inserted in the upper end portion of the cylinder bore and has an inner diameter that is smaller than the diameter of the cylinder. A first abradable layer that is worn down by contact with an outer peripheral surface of the piston is formed on an inner peripheral surface of the ring.

Abstract: An internal combustion engine includes: a cylinder in which a piston reciprocates; a cylinder head positioned above the cylinder; a gasket disposed between the cylinder and the cylinder head; a stepped portion formed at an upper end portion of an inner peripheral surface of the cylinder; a ring member having a cylindrical shape provided to the stepped portion; and a seal member that is provided on an upper surface of the ring member and is sandwiched between the ring member and the cylinder head.

Abstract: An electric work vehicle includes a body 3 configured to travel; a first battery 20 accommodated in the body 3; a three-point link mechanism T connected to a rear section of the body 3 in such a manner as to be swingable up and down; a drive device configured to drive the three-point link mechanism T to swing; and a second battery 60 detachably attached to the three-point link mechanism T. The second battery 60 is switchable, by swinging of the three-point link mechanism T, between a lowered state, in which the second battery 60 is in contact with a ground surface, and a raised state, in which the battery 60 is raised above the ground surface.

Abstract: A customer environment log generation unit (130) acquires a simulated environment log (410) being a log that indicates a behavior estimated to occur in a simulated environment (200) when an attack is made on the simulated environment (200) being a system environment which simulates a customer environment (300) being an actual system environment and which has a difference from the customer environment (300). Further, the customer environment log generation unit (130) converts the simulated environment log (410) into a customer environment log (430) being a log that indicates a behavior estimated to occur in the customer environment (300) when an attack corresponding to the attack against the simulated environment (200) is made on the customer environment (300), by reflecting the difference between the simulated environment (200) and the customer environment (300).

Abstract: An attribute-value extraction unit (101) extracts as a plurality of model-generation attribute values, a plurality of attribute values belonging to an attribute associated with a monitoring subject for anomaly detection. A division-data generation unit (102) extracts for each model-generation attribute value, a normal event associated with the model-generation attribute value from normal data indicating a plurality of normal events each of which is found out to be normal, each of which is associated with one attribute value of the plurality of attribute values, and each of which includes a plurality of characteristics, and generates for each model-generation attribute value, division data indicating the extracted normal event. A characteristic selection unit (103) selects a combination of characteristics to be used for generation of a normal model to be used for anomaly detection, from a plurality of characteristics included in a plurality of normal events indicated in a plurality of pieces of division data.

Abstract: A security monitoring apparatus (100) includes a content category deducing unit (122), a category comparing unit (123), and an information assignment unit (130). The content category deducing unit (122) deduces a first deduced category that is a result of deducing a category of content that a target device that a monitoring target system (200) includes has, using a content category deducing model that is a learning model that deduces using content data that indicates content, a category of content indicated in the content data, and data that indicates content that the target device has. The category comparing unit (123) verifies whether or not the first deduced category and a category for comparison match. The information assignment unit (130) generates assignment information that is in accordance with whether or not the first deduced category and the category for comparison match.

Abstract: A power supply device for an electric work vehicle, including: a battery by which an electric work vehicle is drivable; a first connection unit for an internal inverter configured to convert DC power from the battery into AC power, and output the AC power to an internal electric device installed in the electric work vehicle; a second connection unit for an external inverter configured to convert DC power from the battery into AC power, and output the AC power to an external electric device; and a battery control unit connected to the first connection unit and the second connection unit, and configured to control charging of, and power supply from, the battery, wherein the battery control unit is configured to communicate with the external inverter side via the second connection unit to perform authentication regarding whether or not power supply to the external inverter is permitted.

Abstract: An acquisition unit (10) acquires normal sample data and non-normal sample data. A model generation unit (120) generates a normal model representing the normal sample data. A change unit (141) generates a non-normal feature vector of the non-normal sample data, and generates a non-normal changed vector obtained by changing an element of the non-normal feature vector. When the non-normal changed vector and the normal model are similar to each other, a verification unit (142) executes a process using sample data represented by the non-normal changed vector. The verification unit (142) verifies whether an anomalous event is detected by a detection device. Upon verification that an anomalous event is not detected, the verification unit (142) determines whether an anomalous event is present, independently of the detection device.

Abstract: A log generation apparatus (100) includes an object search unit (111), a user search unit (112), and a specific operation log generation unit. The object search unit (111) uses a target operation log, which is a log of operations actually performed on objects owned by a target system, to search for a target object from among the objects owned by the target system. The user search unit (112) uses the target operation log to search for, as a target user, a user who can operate the target object from among users of the target system. The specific operation log generation unit receives specific operation information indicating a specific operation that a specific user performs in the target system, and uses the specific operation information and the target operation log to generate a specific operation log, which is a virtual log indicating a specific operation that the target user has performed on the target object.

Abstract: A normal classification unit (101) extracts a true positive access that is known to be an access aimed to attack and that has been determined by a detection unit (102) to be an access aimed to attack. A modification unit (107) modifies a feature of the true positive access by using a feature of a true negative access that is known to be a normal access and that has been determined by the detection unit (102) to be a normal access.

Abstract: A power supply device for an electric work vehicle, including: a battery by which an electric work vehicle is drivable; a first connection unit for an internal inverter configured to convert DC power from the battery into AC power, and output the AC power to an internal electric device installed in the electric work vehicle; a second connection unit for an external inverter configured to convert DC power from the battery into AC power, and output the AC power to an external electric device; and a battery control unit connected to the first connection unit and the second connection unit, and configured to control charging of, and power supply from, the battery, wherein the battery control unit is configured to communicate with the external inverter side via the second connection unit to perform authentication regarding whether or not power supply to the external inverter is permitted.

Abstract: An attribute-value extraction unit (101) extracts as a plurality of model-generation attribute values, a plurality of attribute values belonging to an attribute associated with a monitoring subject for anomaly detection. A division-data generation unit (102) extracts for each model-generation attribute value, a normal event associated with the model-generation attribute value from normal data indicating a plurality of normal events each of which is found out to be normal, each of which is associated with one attribute value of the plurality of attribute values, and each of which includes a plurality of characteristics, and generates for each model-generation attribute value, division data indicating the extracted normal event. A characteristic selection unit (103) selects a combination of characteristics to be used for generation of a normal model to be used for anomaly detection, from a plurality of characteristics included in a plurality of normal events indicated in a plurality of pieces of division data.

Abstract: An attribute-value acquisition unit (203) acquires an attribute value of an attribute associated with a monitoring subject for anomaly detection. A normal-model acquisition unit (204) acquires from among a plurality of normal models generated corresponding to a plurality of attribute values, a normal model generated corresponding to the attribute value acquired by the attribute-value acquisition unit (203). An anomaly detection unit (205) performs the anomaly detection, using the normal model acquired by the normal-model acquisition unit (204).

Abstract: A fraudulent email decision device (10) is provided with a consistency analysis unit (24). The consistency analysis unit (24) identifies an intention of a subject email by, for example, a method of, with respect to a newly received incoming email as a subject email, extracting a function term, being a word expressing a reason the subject email was sent, from a body of the subject email. The consistency analysis unit (24) decides whether or not the subject email is a fraudulent email, from a relationship between another incoming email received in the past from the same sender as the sender of the subject email, and the identified intention of the subject email.

Abstract: A piston for an internal combustion engine, said piston having an upper piston portion (1) in which a ring zone (2) is located, and a piston lower part (3) having two opposing, load-carrying skirt wall sections (4,5) on the pressure- and counter-pressure sides of the piston adjoins the upper piston portion (1), and the two load-carrying skirt wall sections (4,5) are interconnected via connecting walls (6,7) that are recessed relative to the outer diameter of the piston. Each connecting wall (6,7) includes a pin bore having a pin bore axis (18) for receiving a pin. The two opposing, load-carrying wall section (4,5) have different wall thicknesses and different transition regions at the transition of their lateral skirt connections (8,9 and 16,17) to the recessed connection walls (6,7).

Abstract: In an SNS server (103) corresponding to a false submission filter device, an event specifying unit (604) analyzes contents of a submission informing of an occurrence of an event and specifies a location (721) of occurrence of the event. A query destination specifying unit (605) searches a query destination database (613) and specifies a query destination corresponding to the location (721) specified by the event specifying unit (604). A query unit (606) transmits a request for checking the presence or absence of occurrence of the event from the observation result of one or more machines to the query destination specified by the query destination specifying unit (605). The query unit (606) receives a response to the request. A result reflecting unit (607) determines whether the contents of the submission are true or false from a check result indicated by the response received by the query unit (606).

Abstract: An evaluation tree generation unit (101) generates as an evaluation tree, an attack tree about an information system, which is based on inference using predicate logic. A gold tree generation unit (102) generates a gold tree which covers an intrusion route to the information system and reflects an intrusion procedure for the information system, by using network configuration information indicating a network configuration of the information system and intrusion procedure information indicating an intrusion procedure assumed in intrusion into the information system. A tree comparison unit (103) compares the evaluation tree with the gold tree.

Abstract: A disclosed feature generation unit (110) collects information related to an assessment target whose security risk is to be assessed, as disclosure target information from disclosed information that has been disclosed, and generates disclosed feature information (F1) expressing a feature of the disclosure target information. An email feature generation unit (120) generates email feature information F2 expressing a feature of an assessment target email contained in an email box of the assessment target. An assessment unit (130) calculates a similarity degree between the disclosed feature information (F1) and the email feature information (F2). The assessment unit (130) outputs an assessment result 31 being a result of assessment on the security risk of the assessment target, based on the similarity degree.

Abstract: An acquisition unit (10) acquires normal sample data and non-normal sample data. A model generation unit (120) generates a normal model representing the normal sample data. A change unit (141) generates a non-normal feature vector of the non-normal sample data, and generates a non-normal changed vector obtained by changing an element of the non-normal feature vector. When the non-normal changed vector and the normal model are similar to each other, a verification unit (142) executes a process using sample data represented by the non-normal changed vector. The verification unit (142) verifies whether an anomalous event is detected by a detection device. Upon verification that an anomalous event is not detected, the verification unit (142) determines whether an anomalous event is present, independently of the detection device.

Abstract: A state replication apparatus (200) generates communication, between a main apparatus (421) and each sub-apparatus (422, 423), to cause a state combination to transit in accordance with transition order specified in an acquisition scenario. The state replication apparatus records each of the communication generated between the main apparatus and the each sub-apparatus. The state replication apparatus acquires a snapshot combination at each of acquisition timings specified in the acquisition scenario. The state replication apparatus replicates each of the main apparatus and the each sub-apparatus in states of a replication state combination based on the acquired each snapshot combination and the recorded each communication.