Denso Corporation

Nara Institute of Science and Technology

Denson Corporation

Masumi Egawa

Kazuhito Takenaka

Takashi Bando

Takeshi Sugashima

Hideaki Misawa

Masataka Mori

Reiichirou Imoto

Yuki Shinohara

Utsushi Sakai

Kentaro Hitomi

Kazuoki Matsugatani

Taiji Abe

Kenji Muto

Naoya Ishida

Keigo Nagara

Reiichiro Imoto

Seiichi Furuta

Takahiro Shidai

Jun Kosai

Hossein Tehrani Niknejad

Tokuya Inagaki

Kenji Mutou

Keita Hayakawa

Ryosuke Murakami

Hiromasa Hanai

Takatomi Kubo

Ryunosuke Hamada

Kazushi Ikeda

Toru Hasegawa

Katsuya Tanaka

A center device is provided that receives a log from vehicle-mounted equipment transmitting the log based on an external transmission rule and analyzes the log to detect a cyber attack. Based on a result of the detecting, the center device determines update of the external transmission rule. The center devices transmits an external transmission rule update instruction. As attack depth of the cyber attack is deeper, the center device sets an external transmission target to the log that is generated in a deeper layer among layers in which constituent elements of the vehicle-mounted equipment are defined.

Log management device and center device

A log management device includes a log collection unit configured to receive a log generated by a security sensor, a storage unit configured to store the log, a statistical analysis unit configured to obtain a statistical calculation result by performing statistical analysis on a plurality of the logs, a control unit configured to determine which of the log and the statistical calculation result is to be sent according to a predetermined condition, and a transmission unit configured to transmit at least one of the log or the statistical calculation result according to the predetermined condition.

Log management device, log management method, computer program product, and security attack detection and analyzing system

An attack analyzer includes: a security log acquisition unit acquiring a security log including an abnormality detection signal generated by a security sensor mounted on an electronic control device constituting part of an electronic control system and indicating that the security sensor has detected an abnormality; an alive signal acquisition unit acquiring an alive signal; a prediction table storage unit storing a prediction table showing a correspondence relationship between a predicted attack route in the electronic control system and a predicted abnormality detection signal predicted to be generated by the security sensor; an attack route estimation unit estimating, using the prediction table, the attack route of an attack received by the electronic control system from the abnormality detection signal and the alive signal included in the security log; and an attack information output unit outputting attack information indicating the attack route.

Attack analyzer, attack analysis method and attack analysis program

An attack analyzer includes: a security log acquisition unit acquiring a security log including an abnormality detection signal generated by a security sensor mounted on an electronic control device constituting an electronic control system and indicating that the security sensor has detected an abnormality; a failure information acquisition unit acquiring failure information indicating a failure occurred in the electronic control device; a prediction table storage unit storing a prediction table showing a correspondence relationship between a predicted attack route in the electronic control system and a predicted abnormality detection signal predicted to be generated by the security sensor; an attack route estimation unit; and an attack information output unit outputting attack information indicating the attack route.

Attack analyzer for accurate estimation of attack route

A message monitoring system includes a first electronic control unit and a second electronic control unit connected to the first electronic control unit via a communication network. In the first electronic control unit, a first message is generated; a first feature value representing a feature of the first message is calculated; a second message storing the first feature value is generated; and the first message is transmitted whereas the second message is transmitted periodically. In the second electronic control unit, the first message and the second message are received from the first electronic control unit; a second feature value representing a feature of the received first message is calculated; and a comparison is performed between (i) the first feature value stored in the received second message and (ii) the calculated second feature value; and whether the first message is normal is determined based on a result of the comparison.

Message monitoring system, message transmission electronic control unit, and monitoring electronic control unit

An unauthorized intrusion prevention device is provided that, in response to detection of abnormality in an electronic control device mounted in a movable body, gives an instruction for executing measures for the movable body. The unauthorized intrusion prevention device acquires first information related to a protection function of the electronic control device and second information related to a function of the electronic control device other than the protection function. The unauthorized intrusion prevention device, using the first information, performs a first determination of whether the protection function is normal. The unauthorized intrusion prevention device, using the second information, performs a second determination of whether the function other than the protection function is normal or abnormal. The unauthorized intrusion prevention device, using results of first and second determination, determines as the measures a blocking method of blocking unauthorized information in the movable object.

Unauthorized intrusion prevention device, unauthorized intrusion prevention method, and unauthorized intrusion prevention program

A log transmission controller includes a log acquirer, a priority storage, an update instruction acquirer, a priority updater and a transmitter. The log acquirer acquires a log indicating respective states of electronic control units connected to the log transmission controller, which is equipped in a moving object. The priority storage stores priority information indicating a priority for transmitting the log to a server, which is disposed at exterior of the moving object. The update instruction acquirer acquires an update instruction, which is generated by an update instructor equipped in the moving object, for instructing to update the priority information stored in the priority storage. The priority updater updates the priority information based on the update instruction. The transmitter transmits the log to the server based on the priority indicated by the updated priority information.

Log transmission controller

In a driving-state data storage apparatus, a collector collects, from each of vehicles on a target travelling road, a value of data indicative of a driving state of the corresponding vehicle to correspondingly obtain driving-state data values for the target road. A data allocator divides, based on similarity among the driving-state data values, the target traveling road into a plurality of traveling segments, and extracts, from the driving-state data values, data values for each of the divided travelling segments. The data values extracted for each of the travelling segments are similar to each other. The data allocator allocates a distribution of the extracted data values for each of the divided travelling segments to the corresponding one of the divided travelling segments as a feature distribution. A storage unit stores the feature distribution allocated for each of the travelling segments.

Driving-state data storage apparatus

An anomaly estimation apparatus includes a collection section that collects vehicle data, a feature amount calculation section that calculates a feature amount from the vehicle data and stores the feature amount and a place corresponding thereto, an anomaly determination section that determines whether an anomaly occurrence point is present based on the feature amount, an accumulation section that, if the anomaly occurrence point is present, uses the vehicle data at the anomaly occurrence point and an anomaly periphery point to generate estimation data, an information generation section that uses the estimation data to generate causality information representing causality between an anomaly caused at the anomaly occurrence point and an anomaly caused at the anomaly periphery point, and an estimation section that, if the anomaly occurrence point is present, uses the causality information to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point.

Anomaly estimation apparatus and display apparatus

In a driving assist system, a driving evaluator compares a driving feature data item sampled at each predetermined sampling point and obtained from a target vehicle with historical driving data items for the corresponding sampling point. The driving evaluator obtains, based on a result of the comparison, an evaluation value of the driving feature data item for the target vehicle at each predetermined sampling point. An unusual driving determiner obtains a cumulative sum of selected values in the evaluation values of the driving feature data items for the target vehicle. The unusual driving determiner determines whether the cumulative sum is larger than a predetermined threshold, and determines that driving of a driver of the target vehicle is unusual upon determining that the cumulative sum is larger than the predetermined threshold.

Driving assist system

In an anomaly detection apparatus mounted in a vehicle, a model storage stores a driving model which is set corresponding to a travel location of the vehicle traveling on a roadway and represents a normal driving state as the vehicle is traveling at the travel location. A data acquirer acquires driving performance data representing the driving state of the vehicle. A degree-of-anomaly calculator calculates a degree of anomaly in the driving state based on the driving model stored in the model storage and the driving performance data acquired by the data acquirer. A vehicle-mounted transmitter, if the degree of anomaly calculated by the degree-of-anomaly calculator exceeds an anomaly determination value for determining the presence of an anomaly in the driving state, transmits at least the degree of anomaly and the travel location corresponding to the degree of anomaly to a monitoring center located external to the vehicle and detects an anomaly in the road condition based on the degree of anomaly.

Road condition monitoring system

A drive data collection system includes an in-vehicle system and an information center. The in-vehicle system includes a data collector that repeatedly collects measurement values indicative of various indexes regarding a state of a subject vehicle, a model memory that memorizes model information regarding discretization rules that are shared by each of participating vehicles in the system, and a data discretizer that discretizes drive data, which includes time-series measurement values collected by the data collector, into multiple data parts according to the model information. The information center includes a data accumulator that accumulates the discretized drive data in a server. The in-vehicle system sends the discretized drive data to the information center through a communicator. Therefore, the drive data collection system efficiently collects the drive data in a versatilely-utilizable manner for the analysis of general driving practices/behaviors.

Drive data collection system

In an object identification device, each score calculator extracts a feature quantity from the image, and calculates a score using the extracted feature quantity and a model of the specified object. The score represents a reliability that the specified object is displayed in the image. A score-vector generator generates a score vector having the scores as elements thereof. A cluster determiner determines, based on previously determined clusters in which the score vector is classifiable, one of the clusters to which the score vector belongs as a target cluster. An object identifier identifies whether the specified object is displayed in the image based on one of the identification conditions. The one of the identification conditions is previously determined for the target cluster determined by the cluster determiner.

Object identification device

In a security system, a first collecting module cyclically collects target-object information indicative of environments of a target object and a monitored area in which the target object exists. An alarm module determines whether there is an abnormal situation associated with at least one of the target object and the monitored area based on the target-object information, and sets off an alarm indicative of a result of the determination that there is an abnormal situation. A second collecting module cyclically collects circumstance information indicative of a situation of an area surrounding the monitored area. An abnormal situation determining module calculates, based on the circumstance information, a probability that an abnormal situation associated with at least one of the target object and the monitored area will occur, and determines whether the probability is equal to or higher than a threshold level.

Security system, program product therefor, and surveillance method

In an abnormal driving behavior detection system for a vehicle, an obtainer repeatedly obtains an observed value indicative of at least one of a running condition of the vehicle and a driver's driving operation of the vehicle. A mode-probability calculator calculates, each time an observed value is obtained at a given obtaining timing as a target obtained value, a mode probability for each of driving modes as a function of one or more previous observed values. A deviation calculator obtains a predicted observed value for each driving mode using a driver's normal behavior model defined therefor, and calculates a deviation of the target observed value from the predicted observed value for each driving mode. An abnormality determiner determines whether there is at least one driver's abnormal behavior based on the mode probability for each driving mode and the deviation calculated for each driving mode.

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Kariya, Japan

Masumi Egawa