Patent Publication Number: US-2022234626-A1

Title: Trusted context self learning method for an in-vehicle network intrusion detection system developed to limit calibration proliferation and development costs

Description:
INTRODUCTION 
     The present disclosure generally relates to vehicles and security and more particularly relates to methods, systems, and apparatuses assessing the trust levels of a system topology of a vehicle for system configuration or calibration of control units (ECUs) in the vehicle. 
     In recent years there have seen significant advancements in autonomous and semi-autonomous driving features of inland driven vehicles with complex network and host systems. With complex networks and systems produced in mass production in which there is appreciable variation in the produced electrical content of a completed and manufactured vehicle that requires consideration in security assessments. In the vehicles with more advanced autonomous, semi-autonomous, and related feature options, the multiple system variations are coordinated by different ECUs (Electronic Control Units). While regulatory concerns are aligned with self-driving system classes, there is needed applicability to even a platform that does not support any class of autonomy and will be launched within vehicles that do not support autonomy as well. The autonomous and semi-autonomous vehicle ECUs are internally connected through multiple communication buses enabling the ECUs connected to the bus to read or send data to other ECUs. As a result, if an adversary can compromise one of the ECUs, the adversary can access and exploit the data of other ECUs. The function of an in-vehicle Network Intrusion Detection System (NIDS) is to monitor on-premise environments (in this case a network of ECUs) for suspicious events that could indicate a compromise and to prevent network compromises and intrusions. 
     To successfully operate a NIDS, the network signal monitoring software must be able to have a precise mapping of the network topology. The network topology mapping consists of details related to the type and number of networks (Controller Area Network, Ethernet, etc.) and details related to which ECUs are located on which networks. 
     It is desirable to depart from costly and time-consuming calibration processes that have been used to create vehicle specific configurations that contain a list of network topology and messages in use by each ECU that is each part of a unique platform build. 
     It is desirable to have a system design that is capable of securely self-learning vehicle network topology by monitoring certain ECU network security and vehicle state elements to identify a trusted window during which learning can occur to prevent unauthorized personnel from otherwise accessing and causing a misconfiguration of a vehicle system. 
     It is desirable to provide a capability to support a secure self-enable of the NIDS in select vehicle sales regions to ensure functionality that conforms with regional data privacy regulations in the region of sales that may differ from a vehicle&#39;s assembly locality. 
     Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. 
     SUMMARY 
     In at least one exemplary embodiment, a method for determining a trusted window by an in-vehicle Network Intrusion Detection System (NIDS) for the learning of a vehicle platform is provided. The method includes: executing, by a processor, the NIDS to monitor a network of Electronic Control Units (ECUs) and vehicle state elements by receiving a set of vehicle inputs about a vehicle&#39;s operating state; in response to a determination about the vehicle&#39;s operating state, identifying, by the processor, the trusted window during which learning about a network topology and whitelisted messages contained in a vehicle platform is allowable; creating, by the processor, a vehicle-specific configuration containing a list of networks of topologies and whitelisted messages in use on specific networks by the ECUs in the vehicle platform; and preventing, by the processor, misconfiguring of at least one network in the list of network topologies and whitelisted messages of the vehicle-specific configuration in the vehicle platform outside the trusted window based on results from an assessment of at least one security implication of a network topology and whitelist message contained in the list in use by the ECUs in the vehicle platform. 
     In at least one embodiment, the method includes applying, by the processor, to generate by the algorithm, the network topology, and the whitelist message with behavior associated with the vehicle platform. 
     In at least one embodiment, the method includes implementing, by the processor, prior available data of network topology generated during the manufacture of a vehicle. 
     In at least one embodiment, the method wherein the prior available data includes data of ECUs that are part of the vehicle&#39;s network topology. 
     In at least one embodiment, the method wherein the data vehicle&#39;s network topology includes ECUs, and controller area network (CAN) bus membership. 
     In at least one embodiment, the method includes implementing, by the processor, automated learning by the algorithm in the NIDS of a region of sale of a vehicle. 
     In at least one embodiment, the method includes enabling, by the processor, the NIDS to use leverage pre-existing manufacturing data to extract the region of sale of the vehicle to enable operability of the NIDS in accordance with privacy regulations of a region that the vehicle will be offered for sale. 
     In at least one embodiment, the method includes extracting, by the processor, via a compact system network configuration file in conjunction with a topology that has been learned to generate at least one whitelist message set for a vehicle build in accordance with whitelist message usage while also making considerations for whitelisting message for OEM aftermarket ECUs to be added during a system life cycle. 
     In another exemplary embodiment, a system is provided. The system includes a processor configured to execute an in-vehicle Network Intrusion Detection System (NIDS) to monitor a set of Electronic Control Units (ECUs) and vehicle state elements by receiving a set of vehicle inputs about a vehicle&#39;s operating state; in response to a determination about the vehicle&#39;s operating state, the processor configured to identify the trusted window of allowability to learn about a network topology and whitelisted messages contained in a vehicle platform; the processor configured to create a vehicle-specific configuration containing a list of networks of topologies and whitelisted messages in use by the ECUs in the vehicle platform; and the processor configured to prevent misconfiguring of at least one network in the list of network topologies and whitelisted messages of the vehicle-specific configuration in the vehicle platform outside the trusted window based on results from an assessment of at least one security implication of a network topology and whitelist message contained in the list in use by the ECUs in the vehicle platform. 
     In at least one exemplary embodiment, the processor is configured to implement the algorithm to generate the network topology and the whitelist message with behavior associated with the vehicle platform. 
     In at least one exemplary embodiment, the processor is configured to implement prior available data of network topology that is generated during a manufacture of a vehicle. 
     In at least one exemplary embodiment, wherein the prior available data includes data of ECUs that are part of the vehicle&#39;s network topology. 
     In at least one exemplary embodiment, wherein the data vehicle&#39;s network topology includes, but is not limited to, controller area network (CAN) bus locations. 
     In at least one exemplary embodiment, the processor is configured to implement automated learning using the algorithm in the NIDS of a region of sale of a vehicle. 
     In at least one exemplary embodiment, the processor is configured to extract, via a compact system network configuration file in conjunction with a topology that has been learned, at least one whitelist message set for a vehicle build in accordance with whitelist message usage. 
     In yet another exemplary embodiment, a vehicle apparatus is provided. The vehicle apparatus includes: a vehicle controller which includes a processor wherein the processor implements an algorithm to determine a trusted window by an in-vehicle Network Intrusion Detection System (NIDS) to learn about a vehicle platform, including execution of the NIDS to monitor a set of Electronic Control Units (ECUs) and vehicle state elements by receipt of a set of vehicle inputs about a vehicle&#39;s operating state; in response to a determination about the vehicle&#39;s operating state, identify the trusted window during which to learn about a network topology and whitelisted messages contained in a vehicle platform is allowable; create a vehicle-specific configuration that contains a list of networks of topologies and whitelisted messages in use by the ECUs in the vehicle platform; and prevent misconfiguring of at least one network in the list of network topologies and whitelisted messages of the vehicle-specific configuration in the vehicle platform outside the trusted window based on results from an assessment of at least one security implication of a network topology and whitelist message contained in the list in use by the ECUs in the vehicle platform. 
     In at least one exemplary embodiment, the vehicle apparatus includes the processor configured to: generate via the algorithm the network topology and the whitelist message with behavior associated with the vehicle platform. 
     In at least one exemplary embodiment, the vehicle apparatus includes the processor configured to: implement prior available data of network topology generated during the manufacture of a vehicle. 
     In at least one exemplary embodiment, the vehicle apparatus includes the processor configured to: implement automated learning by the algorithm in the NIDS of a region of sale of a vehicle. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
       The exemplary embodiments will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and wherein: 
         FIG. 1  is a functional block diagram illustrating an autonomous or semi-autonomous vehicle having a NIDS configured with an intelligent identification algorithm that enables control of vehicle ECU calibrations and other configurations based on using a set of parameters to assess a trusted context in which vehicle calibration may occur, in accordance with exemplary embodiments; 
         FIGS. 2A, 2B, and 2C  are flow diagrams illustrating steps of an autonomous or semi-autonomous vehicle having a NIDS configured with an intelligent identification algorithm that enables control of vehicle ECU calibrations and other configurations based on using a set of parameters to assess a trusted context in which vehicle calibration may occur, in accordance with exemplary embodiments; and 
         FIG. 3  is a block diagram illustrating a gateway in communication with a set of ECUs of an autonomous or semi-autonomous vehicle and external ECUs with a NIDS configured with an intelligent identification algorithm that enables gateway access and control of vehicle ECU calibrations and other configurations based on using a set of parameters to assess a trusted context in which vehicle calibration may occur, in accordance with exemplary embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description. As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
     Embodiments of the present disclosure may be described herein in terms of functional and/or logical block components and various processing steps. It should be appreciated that such block components may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, lookup tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems and that the systems described herein are merely exemplary embodiments of the present disclosure. 
     For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning, image analysis, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure. 
     The NIDS may host signal processing and identification algorithms to distinguish a baseline operation of network protocol and system behaviors from deviations so as to facilitate the identification of targeted system behavior violations. The intrusion detection can be implemented to prevent malicious network traffic activities such as the hacking of vehicle systems and manipulating of configuration data. 
     The present disclosure describes methods, systems, and apparatuses that provide constraints calibration proliferation and development work on a per-platform basis with respect to vehicle system information needed to support the Network intrusion detection system functionality. The present disclosure describes methods, systems, and apparatuses that provide a self-learning algorithm that generates configuration data needed for the proper operation of a NIDS while using OEM specific MFG processes and ECU states to generate a trusted context for learning. 
       FIG. 1  is a functional block diagram illustrating an autonomous or semi-autonomous vehicle having a NIDS configured with an (i.e. intelligent) identification algorithm that enables control of vehicle ECU calibrations and other configurations based on using a set of parameters to assess a trusted context in which vehicle calibration may occur, in accordance with exemplary embodiments. 
       FIG. 1  illustrates an embodiment of an autonomous type of vehicle  100  in which an identification algorithm assessing a set of parameters or factors to determine a trusted context for gateway access to ECU configurations of vehicle systems. While it is contemplated that the disclosed subject matter is implemented in systems of an autonomous type of vehicle  100  other ways of deploying the disclosed subject are also feasible. For example, it is entirely possible for the subject matter to be deployed in or integrated into systems or equipment of other types of vehicles that may or may not be autonomous or remote devices such as drones. 
     With a reference to  FIG. 1 , the vehicle  100  having a network  105  (i.e. an all in-vehicle network) self-configuration processor system (inputs, ECU flags, vehicle states, ECUs, Sensors and sensor data, calibration data, etc. . . . )  110  is shown in accordance with exemplary embodiments. Vehicle  100  includes a plurality of sensors  120 , sensor ECUs  131 , general ECUs  130 , and a trusted network self-configuration module  132  of the trusted network self-configuration processor system  110 . 
     The trusted network self-configuration processor system  110  for an in-vehicle NIDS avoids the need for costly calibration proliferation methods by enabling build processes and the ECU states unique to the vehicle manufacturing processes and new, compact network design artifacts hosting critical network information required by the system. The trust assessed for the context of the network self-configuration processor system  110  is critical in lieu of a proliferation of vehicle-specific, signed calibrations. 
     The level of trust for the trusted, network self-configuration processor system  110  is determined by a set of determinations, checks, or assessments of results of security assessments (or security implications) that include assessing parameters or attributes of the following: (1) ECU level flags, security tokens, or counters used to indicate production processes are possible, (2) ECU privileged access status such as Security Access level and (3) vehicle mileage to be ≤a critical threshold for a vehicle to be distinguished as new such that the system recognizes the value and can imply the vehicle exists in a trusted OEM manufacturing environment; and in the trusted environment the NIDS can (4) securely ingest network topology information generated during the vehicle manufacturing process to ID which ECUs exist and on which network(s) it can be determined to generate a (5) baseline white list of in-vehicle network platform-specific content using the learned topology information of (4) in conjunction with (6) a new compact system configuration ARXML file that identifies all possible ECUs and messages and associated performance behaviors. 
     In an exemplary embodiment, a trusted assessment to learn the saleable region of the system to only (or nearly only, or as much as possible) enable the system functionality in markets where data collection would be compliant with regional regulatory mandates. Also, other additional customer-specific enable criteria could also be used in conjunction with this enable flag. In that regard, this is an initial ‘enable’ flag that would need to be AND&#39;d with other information, say a customer allowing data to be collected as a choice. The trust assessment leverages the existing manufacturing process and trust model by appending a (7) a saleable region code to a canonical server name, for example, which is configured to support telematic communication ECU  136  and which can be programmed based on the intended region of sale. Whereas the canonical URL name may provide no parseable region of sale data without otherwise needing additional lookup tables and configurations, the use of (7) the saleable region code in conjunction with (8) telematics system provides user Terms and Conditions acceptance to permit the NIDS to maintain agility with respect to a system for the current dynamic global environment with respect to data privacy and offboarding vehicle data. 
     This assessment of the trusted context eliminates the dependency on using staff resources and time-consuming calibration methods to provide the information needed for the functionality of an in-vehicle NIDS to support creating a baseline definition of each unique vehicle build configuration. 
     In an exemplary embodiment, the network self-configuration processor system  110  does not require calibration NIDS configuration tables as the automatic learn algorithm that is implemented creates a trusted environment in lieu of dependence security provided by signed calibration files. 
     In an exemplary embodiment, the network self-configuration processor system  110  relies on prior or legacy security elements that are available but have been repurposed or designed to create the context that defines a trusted window required for learning system configurations. 
     In an exemplary embodiment, the network self-configuration processor system  110  implements a compact system network configuration file extract  151  that is enabled for use in conjunction with system topology learned information to generate vehicle build specific “whitelisted” message sets with associated message performance. System configuration files can be 100&#39;s of MBs in size and contain all possible messages that any vehicles manufactured or assembled may have for all protocols, the extract is on the order of 10&#39;s of kilobytes in size and can be used within the program memory  144  of a modern embedded system (e.g. microcontroller). 
     In an exemplary embodiment, the whitelisted messages are the only messages if they do transmit on a vehicle network within the trusted context window can be considered as normal or baseline occurrences. 
     In an exemplary embodiment, the messages that are not part of the whitelisted messages but are part of an overall system configuration ARXML and are considered abnormal and will be flagged by the NIDS as prima facie violations, without respect to any other performance considerations. 
     In an exemplary embodiment, the network self-configuration processor system  110  can enable processes to leverage the use of legacy or existing ECU designs and vehicle manufacturing processes to learn canonical telematic server URL names to securely and easily receive the explicit region of sale at build time without additional programming time or ECU calibration memory partitions required. 
     In an exemplary embodiment, the network self-configuration processor system  110  binds the NIDS operation to as-built data and enables the NIDS to determine if it is allowed to log and offload data in accordance with regional data privacy laws. 
     In an exemplary embodiment, the network self-configuration processor system  110  defines mechanisms by which the learned in-vehicle network configuration information and can be used to verify the legitimacy of certain approved aftermarket ECUs which can be added to the in-vehicle networks while also adapting the vehicle whitelist to add the new messages while also being able to detect tampering of previously learned configurations. 
     The sensors sense observable conditions of the vehicle  100  and can include, but are not limited to, image, LIDAR, and radar sensors  120 . Generally, each sensor of the plurality of sensors is specifically is coupled to the trusted network self-configuration processor module (communication gateway controller)  132  of the vehicle  100  and configured to sense external surroundings of the vehicle  100 . The trusted network self-configuration processor module  132  receives sensor signals generated by the sensors  120  and provided by sensor ECUs  131 , processes the sensor signals to obtain sensor data. Although the depicted embodiment realizes a platform as a vehicle  100 , the concepts presented here can be deployed in other platforms, such as aircraft, spacecraft, watercraft, motorcycles, robots, robotic devices, and the like. Moreover, the concepts presented here may also be deployed in alternative mobile and non-mobile platform applications, if so desired. 
     As mentioned, the vehicle  100  generally includes a plurality of sensors  120 , sensor ECUs  131 , general ECU devices  130 , and software, sufficient for ingesting digital information and/or sensing information, converting the sensed information into digital information, and providing the digital information to the network self-configuration processor system  110 . Generally, each sensor of the plurality of sensors is configured to sense aspects of the surroundings of the vehicle  100 . 
     Outside of the trusted manufacturing context, the network self-configuration processor system  110  if configured, can allow data to be exfiltrated via  136  if in a designated region, and also incorporate additional consumer approval as an additional input to permit data collection. The transceiver  136  can be used to establish and maintain the communications links to onboard components and external communication sources to be used to provide additional data, such as customer terms and conditions acceptance need to complete the activation of the NIDS. The transceiver  136  can perform signal processing (e.g., digitizing, data encoding, modulation, etc.) as is known in the art and this instance for the temporal data received sent, and received. 
     With continued reference to  FIG. 1 , the components of the network self-configuration processor system  110  and their functions are described. In the depicted embodiment, the computer system of the network self-configuration processor system  110  includes networks  105 , ECUs  130 , an extra vehicle communication interface  146 , and a communication gateway controller  132  with a block data processor  142  communicatively coupled to a memory  144 , a storage device  148 , an interprocessor bus  150 , and an optional storage disk  158 . In various embodiments, the network self-configuration processor system  110  performs actions and other functions described further below in connection with  FIG. 2 . The block data processor  142  performs the computation and control functions attributed to the network self-configuration processor system  110  and may include any type of module or multiple modules, single integrated circuits such as a micro module, or any suitable number of integrated circuit devices and/or circuit boards working in cooperation to carry out the described operations, tasks, and functions by manipulating electrical signals representing data bits at memory locations in the system memory, as well as other processing of signals. 
     During operation, the block data processor  142  loads and executes one or more programs, algorithms, and rules embodied as instructions and applications (i.e. the learning algorithm)  152  contained within the memory  144  and, as such, controls the general operation of the control system of the communication gateway controller  132 . In executing the processes described herein, the block data processor  142  loads and executes at least a program  156 . 
     A computer-readable storage medium, such as a memory  144 , a storage device  148 , or an optional storage disk  158  may be utilized as both storage and a scratchpad. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to the data bits. The memory  144  can be any type of suitable computer-readable storage medium. For example, memory  144  may include various types of dynamic random access memory (DRAM) such as SDRAM, the various types of static RAM (SRAM), and the various types of non-volatile memory (PROM, EPROM, and flash). In certain examples, the memory  144  is located on and/or co-located on the same computer chip as the block data processor  142 . In the depicted embodiment, the memory  144  stores the above-referenced instructions and applications  152  along with one or more configurable variables in stored values  154 . 
     The storage device  148  is a computer-readable storage medium in the form of any suitable type of storage apparatus, including direct access storage devices such as hard disk drives, flash systems, floppy disk drives, and optical disk drives. In one exemplary embodiment, the storage device  148  includes a program product from which memory  144  can receive a program  156  that executes one or more embodiments of one or more processes of the present disclosure. In another exemplary embodiment, the program product may be directly stored in and/or otherwise accessed by the memory  144  and/or a disk (e.g., optional storage disk  158 ), such as that referenced below. 
     The data records may be stored in the computer-readable storage medium, such as the memory  144 , the storage device  148 , or the optional storage disk  158 . The internal buses  150  of  132  serve to transmit programs, data, status, and other information or signals between the various components of the computer system of the network self-configuration processor of system  110 . The bus  150  can be any suitable physical or logical means of connecting computer systems and components. This includes, but is not limited to, direct hard-wired connections, fiber optics, infrared and wireless bus technologies. During operation, program  156 , stored in memory  144 , is loaded and executed by the block data processor  142 . 
     The interface  146  may also include one or more network interfaces to allow 110 to communicate with technicians and/or manufacturing systems to permit communication with and potential storage of manufacturing-specific state information which ultimately can be placed into storage apparatuses, such as the storage device  148   
     In various embodiments, vehicle  100  is autonomous or semi-autonomous, and the control system of the communication gateway controller  132 , and/or components thereof, are incorporated into the vehicle  100 . The vehicle  100  is, for example, a vehicle that is automatically controlled to carry passengers from one location to another. The vehicle  100  is depicted in the illustrated embodiment as a passenger car, but it should be appreciated that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, and the like, can also be used. 
     With reference now to  FIGS. 2A, 2B and 2C ,  FIGS. 2A-2C  are flow diagrams illustrating steps of an autonomous or semi-autonomous vehicle having a NIDS configured with an intelligent identification algorithm that enables control of vehicle ECU calibrations and other configurations based on using a set of parameters to assess a trusted context in which vehicle calibration may occur, in accordance with exemplary embodiments, in accordance with various embodiments. 
     In  FIG. 2A , at step  205 , the network self-configuration processor system  110  executes the NIDS algorithm to initiate the automatic vehicle  100  specific network message learning algorithm. When the NIDS configuration is implemented, the NIDS enables data collection of the various ECU associated systems of the vehicle  100 . In an eight-step (See description in  FIGS. 2A-2B ) self-configuration assessment process, at task  210 , the network self-configuration processor system  110  performs a check or assessment by the algorithm via the central communication gateway module using a manufacturing status indication to determine whether a configuration value is greater than &gt;0. If it is determined at task  215  that the value is &gt;0 or true then the security access is determined to be active. The security access sub-function status check of the vehicle mileage to a certain determined low number which is a critical threshold for a vehicle being considered used. The function check implements are ((security access=active sub-function, and odometer value ≤predetermined or a variable set low number of km)). This is associated with steps 2-3 of the eight-step self-configuration assessment. 
     Alternately, if the configuration value at task  210  is not &gt;0 (the check is false) then the flow proceeds to task  215  for a status check of the vehicle mileage is below a preset amount (i.e. 100 km, 75 km, 50 km, etc.) Again, this corresponds to steps 2-3 of the eight-step process flow. In an exemplary embodiment, at task  215 , if the check of the vehicle mileage is &lt;100 km, the flow proceeds to step 4 to at task  220  learn the network topology data in the trusted environment, and to steps 5-6 to initiate the vehicle NIDS network message whitelist learn sub-algorithm (step 5) based on the learned content (step 4) and the NIDS system configuration file extract (Step 6), a new compact sub-set of specific system configuration ARXML data used to support automatic learning of vehicle-specific network. Alternately, if the result of task  215  is false, the flow proceeds to task  222  and determines that the vehicle is not located in the manufacturing plant and is prevented from learning the topology information and to initiate the vehicle NIDS network message whitelist learned sub-algorithm (corresponding to steps 4-6 of the eight-step process flow). 
     At task  217 , (steps 2-3) if the status check of the vehicle mileage is &lt;100 km and security access equals the manufacturing privilege level and this is deemed true then at task  219  (like task  217 ) learn the network topology data in the trusted environment, and to steps 5-6 to initiate the vehicle NIDS network message whitelist learn sub-algorithm (step 5) based on the learned content (step 4) and the NIDS system configuration file extract (Step 6), a new compact sub-set of specific system configuration ARXML data used to support automatic learning of vehicle-specific network. Alternately, if the result of task  217  is false, the flow proceeds to task  221  and determines that the vehicle is not located in the manufacturing plant and is prevented from learning the topology information and to initiate the vehicle NIDS network message whitelist learned sub-algorithm (corresponding to steps 4-6 of the eight-step process flow). The system is implied to exist in a trusted OEM manufacturing environment during which the NIDS can securely ingest network topology information generated during the vehicle manufacturing process to ID which ECUs exist and on which network(s) it is a member. Hence, the system generates at step 5, a baseline white list of in-vehicle network platform-specific content using the learned topology information from step 4 (based on the learned context) in conjunction with step 6 of a new compact system configuration ARXML file (e.g., ARXML (AUTOSAR XML) files are used for the designing system, its configuration, its transportation and storing the information. This facilitates the reusability of Software component (SWC) from one system to another) that identifies all possible ECUs and messages and associated performance behaviors. 
     With reference to  FIG. 2B , the flow proceeds to task  225  to receive signed calibration containing the region of sale code data. Corresponding step 7 of the eight-step process, at task  230 , a determination is made as to whether the region corresponding to the region code allows the collection of data. In other words, if the data collection proposed is allowed under privacy governance of the locality for the region. If it is deemed allowable, the flow proceeds to make another check or determination at task  235  (corresponding to the final step, step 8 of the eight-step process), to check whether the Terms Conditions and Privacy Statement (TCPS) is accepted. If it is deemed true, then the data collection is allowed, the NIDS is allowed to collect and offload data depending on operating conditions set in the NIDS state in a manner in which it is capable to send data to the back office. If at task  235 , the TCPS is not accepted, then the data collection is not allowed. The NIDS is set to a state to prohibit the data collection and to not collect data until conditions are changed. 
     With reference to  FIG. 2C , at task  240 , once the NIDS configuration is completed, the NIDS is allowed to collect data. At task  245  the CAN frame or message is received. At task  250 , the CAN frame or message is checked or evaluated as to whether it is included in the NIDS extracted list of frames or messages. If it is not found in the list, then at task  270 , it is deemed a violation. Alternately, if it is on the list, then flow at task  260  checks the CAN frame or message against a set of normal behavior. At task  265 , a determination is made whether the frame violates the normal behavior. If it is deemed that it does, then the flow proceeds to mark the traffic that consists of the CAN frame or message with a violation. If not, then the flow proceeds to task  275 , and the traffic is marked as normal. Hence, the NIDS through the set of tasks implements mechanisms by which it learns the in-vehicle network configuration information and verifies the legitimacy of certain approved aftermarket ECUs which can be added to the in-vehicle networks while also adapting the vehicle whitelist to add the new messages and being able to detect tampering of previously learned configurations. 
       FIG. 3  is a block diagram illustrating a central communication gateway module  300  in communication with a set of ECUs  305  of an autonomous or semi-autonomous vehicle and external manufacturing and diagnostic systems  310  with the NIDS of central communication gateway module of  300  configured with an intelligent identification algorithm that enables central gateway access (via buses and bus locations  325 ) and control of vehicle ECU calibrations of  300  and other configurations based on using a set of parameters to assess a trusted context in which vehicle secured, self-learning may occur, in accordance with exemplary embodiments. The central communication gateway module  300  handles direct and inter-ECU or routed communications, enabling data exchanges among all the in-vehicle ECUs in addition to enabling external communications of manufacturing and diagnostic networks of  310  to pass through the central communication gateway module  300  via sets of buses and bus locations  325 . The central communication gateway module  300  is a common gateway, which converts data from one bus format to another bus format (i.e. containing an architecture of different types of data buses). 
     In an exemplary embodiment, the present disclosure describes an algorithm that is implemented by a processor to determine a trusted context of operation by an in-vehicle Network Intrusion Detection System (NIDS) for learning of a vehicle platform. The processer executes the NIDS using an algorithm to monitor a set of Electronic Control Units (ECUs) and vehicle state elements by receiving a set of vehicle inputs about a vehicle&#39;s operating state. In response to a determination about the vehicle&#39;s operating state, the processor can identify the trusted window during which learning about network topology and whitelisted messages contained in a vehicle platform build configuration is allowable. The processor can also create a vehicle build-specific configuration containing a list of topologies of networks and whitelisted messages in use by the set of ECUs in each vehicle platform build configuration. The processor can prevent an undocumented misconfiguring of at least one network in the list of networks of topologies and whitelisted messages of the vehicle-specific configuration in the vehicle platform outside the trusted window based on results from an assessment of at least one security implication of a network of a topology and whitelist message contained in a list in use by the set of ECUs in the vehicle platform. The processor implements the algorithm to associate a set of topologies of networks and the whitelisted messages with behavior associated with the vehicle platform-specific build or manufacture. The processor can also use elements of prior available data of network topology generated for a vehicle platform network design. 
     The prior or previously available data includes ECU data that includes network instances, network segmentation configurations, ECU network memberships, frame routing, network message address translation, and frame signal routing definitions that are part of the vehicle&#39;s network topology. The prior available data includes bus data. The implementing automated learning by the processor is determined by the algorithm in the NIDS based on a region of sale of a vehicle. 
     The processor implements the NIDS to use or make readily available existing calibration files otherwise it enables an unparseable canonical, region-specific URL to extract the region of sale of the vehicle to enable operability of the NIDS in accordance with privacy regulations of a region that the vehicle has been offered for sale. 
     The processor extracts via a compact system network configuration file in conjunction with a topology that has been learned to generate at least one whitelist message set for a vehicle build in accordance with whitelist message usage. 
     In various exemplary embodiments, the algorithm  152  of  FIG. 1  that is implemented can be created in offline training derived from supervised or unsupervised learning processes and can be enabled using neural networks. For example, the neural networks may include trained convolutional neural network (CNN) and/or recurrent neural network (RNN) in which a similar methodology can be applied and used in vehicle control operations. 
     In an exemplary embodiment, the algorithm may be implemented as a dynamic neural network that is trained during a training mode before use or provisioned in the vehicle (or other vehicles). Once the dynamic neural network is trained, it may be implemented in a vehicle (e.g., the vehicle  100  of  FIG. 1 ) in an operational mode, in which the vehicle is operated in an autonomous, semi-autonomous or manual manner. 
     In various alternative exemplary embodiments, it will be appreciated that the neural network may also be implemented in both the training mode and the operational mode in a vehicle and trained during an initial operation period in conjunction with operations of a time delay or like methodology for torque control predictions. Also, a vehicle may operate solely in the operating mode with neural networks that have already been trained via a training mode of the same vehicle and/or other vehicles in various embodiments. 
     As mentioned briefly, the various modules and systems described above may be implemented as one or more machine learning algorithms or models that undergo supervised, unsupervised, semi-supervised, or reinforcement learning. Examples of such algorithms include, without limitation, artificial neural networks (ANN) (such as a recurrent neural network (RNN) and convolutional neural network (CNN)), decision tree models (such as classification and regression trees (CART)), ensemble learning models (such as boosting, bootstrapped aggregation, gradient boosting machines, and random forests), Bayesian network models (e.g., naive Bayes), principal component analysis (PCA), support vector machines (SVM), clustering models (such as K-nearest-neighbor, K-means, expectation-maximization, hierarchical clustering, etc.), and linear discriminant analysis models. 
     It should be appreciated that process of  FIGS. 1-3  may include any number of additional or alternative tasks, the tasks shown in  FIGS. 1-3  need not be performed in the illustrated order and process of the  FIGS. 1-3  may be incorporated into a more comprehensive procedure or process having additional functionality not described in detail herein. Moreover, one or more of the tasks shown in  FIGS. 1-3  could be omitted from an embodiment of the process shown in  FIGS. 1-3  as long as the intended overall functionality remains intact. 
     The foregoing detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments. As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, or detailed description. 
     While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. 
     It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.