Cognitive protection of critical industrial solutions using IoT sensor fusion

A technique for cognitive protection of a system can include digital and analog sensors to measure or calculate operational parameters of a system. Digital sensors may be used to determine measured or primary operational parameters. The analog sensors are used to measure analog sensor information related to operation of the system. Analog sensor information that is measured may be used to calculate secondary operational parameters that includes the same operating parameters as the primary operational parameters. Lockstep analysis may be used to compare the primary operational parameters with the secondary operational parameters so as to determine a discrepancy in the operational parameters in the system.

TECHNICAL FIELD

Embodiments described herein generally relate to security in networks, and more particularly to techniques for protecting Internet-of-Things (IoT) networks from advanced threats by performing sensor fusion of sensors data that is received over an independent channel.

BACKGROUND ART

The Internet of Things (IoT) is based on the idea that industrial and consumer solutions, not just computers and computer networks, can be readable, recognizable, locatable, addressable, and controllable via an IoT communications network (e.g., an ad-hoc system or the Internet). IoT devices in industrial and consumer systems, for example, IoT sensors and IoT components, are also accessible over the Internet and, therefore, vulnerable to malware. A cyberattack on an IoT critical infrastructure in an industrial system can cause significant physical damage and/or threaten human life if compromised. For example, a cyberattack on an IoT utility infrastructure that uses a nuclear power plant could target critical sub-systems and potentially cause blackouts, plant explosions, or even a nuclear meltdown in a centrifuge. Recently, the stuxnet virus, which is an example of an industrial malware, was used to target programmable logic controllers (PLCs) in a nuclear reactor and cause severe damage to property. The stuxnet virus targeted industrial software that controlled a nuclear reactor by exploiting a zero-day flaw or vulnerability to obtain control of the PLCs and cause a meltdown in the nuclear reactor. The stuxnet virus is just one example of an advanced malware threat where current antivirus solutions are not able to detect the malware. Similarly, in non-industrial systems such as those used in drive-by-wire vehicle systems onboard a vehicle, computer-controlled devices in vehicles, for example, brakes, engine, locks, or the like that are connected to an onboard network may be attacked by malware in order to gain access to the onboard network and compromise safe operation of the vehicle.

An advanced malware threat is capable of circumventing traditional prevention controls, for example, anti-virus software, host-based intrusion prevention systems, or the like, and modifying the behavior of industrial and non-industrial systems. Existing antivirus security software is unable to defend against these advanced malware threats since antivirus signatures are not yet available to antivirus software developers. All systems, industrial and non-industrial, are vulnerable to attacks if they are connected to a network—be it an internal network or an external network. Current security software does not adequately protect these systems from advanced malware threats, as illustrated above. Therefore, a way of protecting against advanced malware threats in critical industrial and non-industrial solutions would be desirable.

DESCRIPTION OF EMBODIMENTS

As used herein, the term “computer system” can refer to a single computer or a plurality of computers working together to perform the function described as being performed on or by a computer system.

As used herein, the term “cognitive analysis” can refer to using data from one or more sensors or data derived from the one or more sensors in order to represent sensors data into different meaningful and comparable parameters.

As used herein, the term “Internet of Things (IOT)” can refer to a network of physical objects or “things” embedded with electronics, software, sensors and connectivity to enable it to exchange data with other connected devices in the network. Each thing in the network is an IoT device that can be uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure.

As used herein, the term “trusted execution environment” can refer to a set of CPU instructions that provides secure execution services to an operating system. The trusted execution environment allows software to define a safe, isolated execution space within the hardware of a larger system. Controls on this execution space allow operations to be executed without being observed or influenced by unauthorized software such as malware. Multiple of these execution spaces may exist on the system at once, and each has dedicated resources that are managed by the processor, chipset and OS kernel.

A technique for cognitive protection of a system from malware attacks can include industrial and non-industrial systems that are connected to a network. The system may include digital and analog sensors that may be used to determine measured and/or calculated operational parameters of the system. The digital sensors are connected to a network and may communicate one or more primary operational parameters of the system over primary channels. The analog sensors are used to measure analog sensors information related to operation of the system. The analog information may be converted to digital information and communicated over an independent channel and isolated from the primary channels. Cognitive analysis may be used to determine secondary operational parameters of the system using the analog sensors information. The secondary operational parameters include the same operating parameters as those of the primary operational parameters but are determined using other means such as, for example, using analog sensors data. Further, lockstep analysis may be used to compare the primary operational parameters with the secondary operational parameters so as to determine discrepancies in the system.

FIG. 1illustrates a schematic dataflow diagram of a system100for implementing cognitive protection for detecting advance malware threats using fusion of IOT sensors, according to an embodiment of the invention. System100may include an IOT mechanical system102, analog monitoring system104, IOT digital sensors106, conversion module108, network110, emergency actuator system112and command and control (C&C) dashboard116. System100may also include an optional lockstep module114. The IOT mechanical system102, IOT digital sensors106, network110and C&C dashboards116may include traditional security software, for example, anti-virus software or anti-malware mechanisms to defend against malware and virus threats to system100that may target IOT digital sensors106over network110so as to, in one example, conceal actual operating conditions or parameters of IOT mechanical system102. Network110is not limited to a network of interconnected computer networks that use an internet protocol (TCP/IP) such as the Internet, and can also include other data networks and/or telecommunications networks that are configured to pass information back and forth to system100.

As shown inFIG. 1, IOT mechanical system102may represent a monitored system with a plurality of IOT devices that may be used in either an industrial system or a non-industrial system. IOT mechanical system102with its plurality of IOT devices may be addressable and accessible over network110. In an embodiment, IOT mechanical system102is connected to a plurality of IOT digital sensors106that may determine digital information for a set of primary or measured vital system health indicators (“VSHIs”) of IOT mechanical system102. IOT digital sensors106may also be addressable and accessible over network110. In an embodiment, IOT digital sensors106are the primary sensors that may determine one or more various operating parameters and/or operating variables. In an example, the IOT digital sensors106may provide the primary or measured VSHIs for IOT mechanical system102over network110(hereinafter “primary channels”). As used herein, VSHIs are a minimal set of monitored functionalities of IOT mechanical system that are related to health and operation of IOT mechanical system102whereby exertion may result in severe damage to IOT mechanical system102and/or potentially cause harm to users or the environment, in the vicinity of IOT mechanical system102. In an embodiment, IOT digital sensors106may determine the primary or measured VSHIs in digital form. The IOT digital sensors106may provide the primary or measured VSHIs to a command and control (C&C) dashboard116over one or more primary communication channels (hereinafter “primary channel”) via network110.

System100may also include an analog monitoring system104. Analog monitoring system104may include a plurality of analog sensors that are associated with monitored equipment102. Analog sensors may include audio sensors, video sensors, pressure sensors, ultrasound sensors or the like that are configured to sense operating parameters and/or variables of IOT mechanical system102and store the sensed parameters as analog sensor data. Analog monitoring system104is configured to communicate the analog sensor data over one or more independent channels (hereinafter “secondary channels”) to conversion module108. The secondary channels may also be connected to network110, however, may be isolated from the primary channels. In another embodiment, analog sensor data may communicate analog data over another network that is isolated from network110. In an embodiment, secondary independent channels may be hard-wired to communicate analog data or may wirelessly communicate analog data in network110. As the secondary channel is isolated from primary channel that communicates the primary or measured VSHIs, it, therefore, cannot be detected by advanced malware that may have gained access to network110.

Analog sensor data from analog monitoring system104may be transmitted to conversion module108for processing. Conversion module108uses cognitive analysis to create or derive a set of secondary or calculated VSHIs from the analog sensor data. The secondary or calculated VSHIs represents calculated VSHI information that is associated with operating parameters or variables of IOT mechanical system102. In an embodiment, conversion module108may send the secondary or calculated VSHIs to an optional lockstep module114for comparison. The secondary VSHIs include similar parameters, variables or measurements as those of the primary or measured VSHIs. In an embodiment, conversion module108may include logic to determine if the secondary or calculated VSHIs are erroneous and an alert may be provided. An alert that is provided may be indicative of a malware attack on IOT mechanical system102, which may be flagged and sent to an emergency actuator system112for shutting-down IOT mechanical system102. In another embodiment, an optional lockstep module may be used to compare secondary or calculated VSHIs against the primary or measured VSHIs in order to determine discrepancies within IOT mechanical system102.

An optional lockstep module114may receive secondary or calculated VSHIs from conversion module108. Lockstep module114may include hardware and algorithms to process the primary and secondary VSHIs that are received over network110. Lockstep hardware includes one or more processors, memories and algorithms that process the VSHIs that are received and flag an error in the case of a discrepancy. In one embodiment, lockstep module114may compare the respective measured VSHIs against the calculated VSHIs that are received over the primary and secondary channels (“channel matching”) to determine a discrepancy. Lockstep module114may be configured to compare the primary VSHIs with the secondary VSHIs so as to determine if there is a discrepancy in VSHIs that may indicate a potential malware attack on IOT mechanical system102. In another embodiment, lockstep module114may compare the secondary VSHIs against predetermined VSHIs that are stored in one or more memories in order to determine a discrepancy in the system. A discrepancy in the system may be flagged and an alert may be communicated from lockstep module114to C&C dashboard116in response to a discrepancy, which may result in shut-down of IOT mechanical system102for further analysis. The alert may be indicative of a malware threat on IOT mechanical system102by concealing accurate values of primary or measured VSHIs. Thus, using the secondary VSHIs and primary VSHIs provides protection against any malware threat to network110. Any malware threats to network110may have to target all VSHI information that is calculated in system100, which may be very difficult to existing malware. Malware may have to target all VSHIs that are communicated over both the primary channels and the secondary channels. Malware attack on primary channels to compromise primary VSHIs may have to identically target the secondary channels in order to compromise any secondary VSHIs that are communicated. However, cognitive analysis is used to determine these secondary VSHIs using measured sensor data, any malware threats that attack measured sensor data may find it very difficult to generate similar secondary VSHIs that may be used to attack system100Therefore, values of secondary or calculated VSHIs may represent uncompromised operating parameters for IOT mechanical system102, which may be used to detect malware threats to network110.

Emergency actuator system112may include one or more actuators, for example, programmable logic controllers (PLCs) or programmable automation controllers (PACs) that may be actuated to shut-down IOT mechanical system102in response to a discrepancy in the secondary VSHIs received from IOT mechanical system102.

C&C dashboard116is configured to manage, command, direct or regulate the behavior of IOT mechanical system102from the VSHIs that are received by C&C dashboard116. In an embodiment, C&C dashboard116can include programmable automation controllers (PACs), programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), or other smaller control system configurations that use data that is received from lockstep module114to communicate supervisory commands to IOT mechanical system102.

FIG. 2illustrates a schematic block diagram for performing cognitive protection of critical equipment from malware threats to an industrial system200such as, for example, a centrifuge in a nuclear power plant according to an embodiment of the invention. While system200is illustrated below for use with critical equipment in an industrial system, the principles described below are equally applicable to a non-critical system in an industrial system or a non-industrial system such as, for example, an engine control unit (ECU) in a vehicle system. In an embodiment, system200may use a plurality of sensor modalities to detect operational parameters of monitored equipment202, for example, a centrifuge and may use cognitive analysis on the sensors data to protect against advanced malware threats.

System200is connected to a network216and may include monitored equipment202, comparator system208and control system210. In an embodiment, monitored equipment202may be a centrifuge in a nuclear reactor whose various safety-related parameters or variables are being monitored. Monitored equipment202can include sensors204that may sense one or more operational parameters of monitored equipment202, for example, sense pressure, revolutions per minute (RPM), temperature, or the like for use in monitoring of one or more parameters or variables of monitored equipment202.

In embodiments, sensors204may include digital sensors connected to monitored equipment202. The digital sensors may measure one or more parameters of monitored equipment202and provide the sensed digital information to a microcontroller or processor205for processing. Microcontroller205may be configured to process the measured sensor data and determine primary or measured VSHIs (also referred to a “primary or measured parameters”). Primary channel212may be associated with network216, such as a LAN, WAN or the like and may be used for communicating with and/or controlling monitored equipment202. Microcontroller205may include processing hardware and logic for processing the sensor information and transmitting the primary VSHIs to comparator system208and control system210over primary channel212. In an embodiment, primary channel212may be a wireless connection for transmitting the primary VSHIs to comparator system208and control system210.

Sensors204may also include analog sensors that are associated with monitored equipment202. Analog sensors can include audio sensors, video sensors, pressure sensors, ultrasound sensors or the like that are configured to independently measure sensor parameters of monitored equipment202. In some non-limiting examples, an ultrasound sensor may measure sound vibrations of monitored equipment202, an analog infrared image sensor may obtain still images or video images of monitored equipment202, a directional microphone may measure an analog acoustic signal, or laser sensors may be used to trigger a photon counter to determine rotation of, for example, a centrifuge in monitored equipment202. The analog sensor data from sensors204may be communicated to a converter206over a secondary channel214. Secondary channel214may be a physical connection that is independent and isolated from primary channel212. Secondary channel214may provide analog sensor data that is not generally used for measuring primary VSHIs and, therefore, may not be a target of an attack by malware that may have gained access to network216. The analog sensed information may be provided to converter206for conversion of the analog data into secondary or calculated VSHIs. For example, laser sensor data may be used to determine RPMs of the centrifuge or still or video images may be processed by software to compute rotation speed and temperature of a centrifuge.

Converter206may be configured to process the analog sensor data that are received over secondary channel214from analog sensors through cognitive analysis so as to create secondary or calculated VSHIs (also referred to a “secondary or calculated parameters”). For example, analog data from an ultrasonic sensor may be digitized and converted to RPMs, which represents a secondary source of RPM data for monitored equipment202that are calculated using different means. Converter206may include hardware and logic to convert analog sensor information into secondary operational parameters or secondary VSHIs for monitored equipment202. The secondary operational parameters may be another source of VSHIs for monitored equipment202that include same operating parameters as those of the primary or measured VSHIs. The secondary VSHIs determine similar operational parameters of monitored equipment202that are communicated over the secondary channel214. As the secondary channel214is not associated with primary VSHI information, the secondary channel214may be insulated from any malware attacks.

Comparator system208may be configured to analyze and compare the primary and secondary VSHIs that are received over primary and secondary channels212,214, respectively, so as to determine a discrepancy in operation of monitored equipment202. Based on the comparison of the primary and secondary VSHIs, an alert218may be provided to control system210. The alert may be indicative of a malware threat to monitored equipment202which may conceal or amend actual values of primary or measured VSHIs over primary channel212.

In an embodiment, comparator system208may include similar lockstep hardware that was discussed above in relation toFIG. 1so as to process the VSHIs received over primary and secondary channels212,214and determine if a discrepancy exists in the received information. If secondary VSHI on secondary channel214is greater or lesser than an acceptable range of predetermined VSHIs stored in one or more memories, an alert may be provided to control system210. Alternatively, or in addition to determining the acceptable range of values, if the difference between primary VSHIs and secondary VSHIs are greater than a threshold, an alert218may also be provided. The alert may be indicative of a malware threat to monitored equipment202.

Control system210is configured to manage, command, direct or regulate the behavior of monitored equipment202. In an embodiment, control system210can include a supervisory control and data acquisition (SCADA) system that may control operation of monitored equipment202. Control system210may receive primary VSHIs on primary channel212during operation of monitored equipment202and may regulate the behavior of monitored equipment202in response to the primary VSHIs. Control system210may also receive alerts from comparator system208and may shut down or take other action on the monitored equipment202in response to the alerts that are received from comparator system208.

FIG. 3illustrates a conceptual system architecture300that may be used for cognitive protection of an industrial system such as, for example, protection of systems100and200depicted inFIGS. 1-2according to an embodiment. System architecture300may be used for monitoring a SCADA-based industrial system and may include a primary monitoring system (PMS)302, a secondary monitoring system (SMS)304, a central management unit (CMU)310and a backup management system (BMU)324.

PMS302may include a SCADA system for controlling, directing and regulating operation of an industrial system (not shown). PMS302may include primary sensors for measuring operational parameters of the industrial system and providing a set of primary or measured VSHIs306to CMU310. The measured VSHIs306may be transmitted over one or more primary channels. In an embodiment, the primary channels may be connected to a network (not shown).

SMS304may include analog sensors that are associated with monitoring one or more operational parameters of the industrial system (not shown). In embodiments, analog sensors can include audio sensors, video sensors, pressure sensors, ultrasound sensors or the like that are configured to measure operational parameters of the industrial system and communicate the sensor data as analog data308to a CMU310. Analog data308may be communicated to CMU310over one or more independent secondary channels that can be connected to a network. In embodiments, primary and secondary channels may be wired connections to CMU310or may be wireless.

CMU310includes hardware with processors, one or more memories and algorithms so as to process the analog data308and the measured VSHIs306that are received from SMS304and PMS302, respectively. CMU310may independently determine calculated VSHIs318that are received from SMS304as well as compare the measured and calculated VSHIs306,318to determine if there is a discrepancy. In an embodiment, CMU310includes an optional trusted execution environment (TEE)312that may communicate with comparison and alerting module320and programmable automation controllers (PACs)322. TEE312defines a safe, isolated execution space within CMU310that provides an added level of security against malware threats that may have attacked the network. TEE312may include analog data processing module314and cognitive analysis module316. Analog data processing module314may process the analog data308to convert the information into digital format. For example, analog data308from an ultrasonic sensor may be digitized in module314. Also, a cognitive analysis module316may receive the digital information from analog data processing module314and determine a set of calculated or secondary VSHIs318. The calculated VSHIs represent operational parameters of the industrial system (not shown) that include same operating parameters as those of the primary VSHIs but which are calculated using cognitive analysis. As discussed above with reference toFIG. 1, any malware threats may find it very difficult to target both these primary and secondary channels within system300and compromise the information, therein.

Comparison and alerting module320may be configured to analyze and compare the measured VSHIs and calculated VSHIs306,318so as to determine a discrepancy in operational parameters of industrial system. Based on the comparison of the measured and calculated VSHIs306,318, comparison and alerting module320may provide an alert328to BMU324. The alert328may be indicative of a malware threat to the industrial system by targeting measured VSHIs306. In an embodiment, comparison and alerting module320may determine if calculated VSHIs318are greater or lesser than measured VSHIs306or within a range of values or, alternatively, if calculated VSHIs318are greater or lesser than predetermined or stored historic measured VSHIs. In response to this determination, an alert328may be provided to BMU324.

PAC322includes processor and software that is configured to receive alerts326from comparison and alerting module320. In an embodiment, PAC322may include tolerance thresholds that control operation of industrial system including shut-down of industrial system in response to exceeding tolerance thresholds

BMU324is configured to manage, command, direct or regulate the behavior of industrial system in response to receiving alerts and/or calculated and measured VSHIs318,306from CMU310. In an embodiment, BMU324may communicate signals and/or data to PAC322and PMS302so as to communicate supervisory commands to industrial system.

FIG. 4is a flowchart illustrating a process400that may be used for cognitive protection of industrial and non-industrial systems100-300depicted inFIGS. 1-3, according to an embodiment of the invention.

Process400begins in step405. In410, digital sensors connected to the system may determine primary parameters or variables of the system. In an embodiment, system may be a monitored critical or non-critical system that is connected to a network and having a plurality of IOT devices associated with the digital sensors. The primary parameters may include primary VSHIs that are measured by the digital sensors and which are transmitted over one or more primary channels to a comparison module, for example, comparison and alerting module320(FIG. 3).

In415, analog sensors connected to the system may measure secondary sensor parameters and/or variables of the system. The sensor parameters are transmitted as analog sensor data over one or more secondary channels to cognitive analysis module, for example, cognitive analysis module316ofFIG. 3.

In420, cognitive analysis may be used on the analog sensor data to calculate or derive a set of secondary or calculated parameters of the system. The secondary parameters may include secondary VSHIs that represent same operating parameters as those of the primary VSHIs but which are calculated through the use of independent sensors and cognitive analysis. The secondary parameters thus calculated are sent to the comparison module.

In425, the comparison module, for example, comparison and alerting module320(FIG. 3) processes the primary and secondary parameters to determine if there is a discrepancy in the primary and secondary parameters that are received for the system. As any malware threat to the industrial or non-industrial system100-300may target the digital sensors and/or primary channels that are used to measure primary VSHIs of the system, the secondary channels are not used to communicate primary VSHI and are thus not able to be compromised by malware threats.

In430, if there is a discrepancy (i.e., step430=“Y”), then, in step440, the discrepancy is flagged and an alert may be sent to an actuator system, for example, to PAC322(FIG. 3) and/or to a backup management system such as, for example, BMU324(FIG. 3). In an embodiment, PAC322may shut-down the system in response to receiving the alert322. Other actions may be taken by the PAC322in addition to or instead of shutting down the system.

However, in430, if there is no discrepancy in the primary and secondary parameters (i.e., step430=“N”), then, in435, digital sensors and analog sensors may continue monitoring operation of the system. Step440ends in step445.

Referring now toFIG. 5, a block diagram illustrates a programmable device500that may be used within systems100-300ofFIGS. 1-3in accordance with one embodiment. The programmable device500illustrated inFIG. 5is a multiprocessor programmable device that includes a first processing element570and a second processing element580. While two processing elements570and580are shown, an embodiment of programmable device500may also include only one such processing element.

Programmable device500is illustrated as a point-to-point interconnect system, in which the first processing element570and second processing element580are coupled via a point-to-point interconnect550. Any or all of the interconnects illustrated inFIG. 5may be implemented as a multi-drop bus rather than point-to-point interconnects.

As illustrated inFIG. 5, each of processing elements570and580may be multicore processors, including first and second processor cores (i.e., processor cores574aand574band processor cores584aand584b). Such cores574a,574b,584a,584bmay be configured to execute instruction code in a manner similar to that discussed above in connection withFIGS. 1-4. However, other embodiments may use processing elements that are single core processors as desired. In embodiments with multiple processing elements570,580, each processing element may be implemented with different numbers of cores as desired.

Each processing element570,580may include at least one shared cache546. The shared cache546a,546bmay store data (e.g., instructions) that are utilized by one or more components of the processing element, such as the cores574a,574band584a,584b, respectively. For example, the shared cache may locally cache data stored in a memory532,534for faster access by components of the processing elements570,580. In one or more embodiments, the shared cache546a,546bmay include one or more mid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), or other levels of cache, a last level cache (LLC), or combinations thereof.

WhileFIG. 5illustrates a programmable device with two processing elements570,580for clarity of the drawing, the scope of the present invention is not so limited and any number of processing elements may be present. Alternatively, one or more of processing elements570,580may be an element other than a processor, such as an graphics processing unit (GPU), a digital signal processing (DSP) unit, a field programmable gate array, or any other programmable processing element. Processing element580may be heterogeneous or asymmetric to processing element570. There may be a variety of differences between processing elements570,580in terms of a spectrum of metrics of merit including architectural, microarchitectural, thermal, power consumption characteristics and the like. These differences may effectively manifest themselves as asymmetry and heterogeneity amongst processing elements570,580. In some embodiments, the various processing elements570,580may reside in the same die package.

First processing element570may further include memory controller logic (MC)572and point-to-point (P-P) interconnects576and578. Similarly, second processing element580may include a MC582and P-P interconnects586and588. As illustrated inFIG. 5, MCs572and582couple processing elements570,580to respective memories, namely a memory532and a memory534, which may be portions of main memory locally attached to the respective processors. While MC logic572and582is illustrated as integrated into processing elements570,580, in some embodiments the memory controller logic may be discrete logic outside processing elements570,580rather than integrated therein.

In turn, I/O subsystem590may be coupled to a first link516via an interface596. In one embodiment, first link516may be a Peripheral Component Interconnect (PCI) bus, or a bus such as a PCI Express bus or another I/O interconnect bus, although the scope of the present invention is not so limited.

As illustrated inFIG. 5, various I/O devices514,524may be coupled to first link516, along with a bridge518which may couple first link516to a second link520. In one embodiment, second link520may be a low pin count (LPC) bus. Various devices may be coupled to second link520including, for example, a keyboard/mouse512, communication device(s)526(which may in turn be in communication with the computer network503), and a data storage unit528such as a disk drive or other mass storage device which may include code530, in one embodiment. The code530may include instructions for performing embodiments of one or more of the techniques described above. Further, an audio I/O524may be coupled to second bus520.

Note that other embodiments are contemplated. For example, instead of the point-to-point architecture ofFIG. 5, a system may implement a multi-drop bus or another such communication topology. Although links516and520are illustrated as busses inFIG. 4, any desired type of link may be used. Also, the elements ofFIG. 5may alternatively be partitioned using more or fewer integrated chips than illustrated inFIG. 5.

Referring now toFIG. 6, a block diagram illustrates a programmable device600according to another embodiment. Certain aspects ofFIG. 5have been omitted fromFIG. 6in order to avoid obscuring other aspects ofFIG. 6.

FIG. 6illustrates that processing elements670,680may include integrated memory and I/O control logic (“CL”)672and682, respectively. In some embodiments, the672,682may include memory control logic (MC) such as that described above in connection withFIG. 5. In addition, CL672,682may also include I/O control logic.FIG. 6illustrates that not only may the memories632,634be coupled to the672,682but also that I/O devices644may also be coupled to the control logic672,682. Legacy I/O devices615may be coupled to the I/O subsystem690by interface696. Each processing element670,680may include multiple processor cores, illustrated inFIG. 5as processor cores674A,674B,684A and684B. As illustrated inFIG. 6, I/O subsystem690includes P-P interconnects694and698that connect to P-P interconnects676and686of the processing elements670and680with links652and654. Processing elements670and680may also be interconnected by link650and interconnects678and688, respectively.

The programmable devices depicted inFIGS. 5 and 6are schematic illustrations of embodiments of programmable devices which may be utilized to implement various embodiments discussed herein. Various components of the programmable devices depicted inFIGS. 5 and 6may be combined in a system-on-a-chip (SoC) architecture.

Referring now toFIG. 7, an example infrastructure in which techniques described above may be implements is illustrated schematically. Infrastructure700contains computer networks702. Computer networks702may include many different types of computer networks available today, such as the Internet, a corporate network or a Local Area Network (LAN). Each of these networks can contain wired or wireless programmable devices and operate using any number of network protocols (e.g., TCP/IP). Networks702may be connected to gateways and routers (represented by708), end user computers706and computer servers704. Infrastructure700also includes cellular network703for use with mobile communication devices. Mobile cellular networks support mobile phones and many other types of mobile devices. Mobile devices in the infrastructure700are illustrated as mobile phones710, laptops712and tablets714. A mobile device such as mobile phone710may interact with one or more mobile provider networks as the mobile device moves, typically interacting with a plurality of mobile network towers720,730and740for connecting to the cellular network703. Although referred to as a cellular network inFIG. 7, a mobile device may interact with towers of more than one provider network, as well as with multiple non-cellular devices such as wireless access points and routers708. In addition, the mobile devices710,712and714may interact with non-mobile devices such as computers704and706for desired services, which may include cognitive analysis of analog sensor data described above. The functionality of the systems100-300(FIGS. 1-3) may be implemented in any device or combination of devices illustrated inFIG. 7; however, most commonly is implemented in a firewall or intrusion protection system in a gateway or router.

The following examples pertain to further embodiments.

Example 1 is a computer system for cognitive protection of a control system, comprising: one or more processors; one or more memories coupled to the one or more processors, on which are stored instructions, comprising instructions that when executed cause one or more of the processors to: receive first monitored information from digital sensors over a first channel for a control system, wherein the first monitored information is digital information and includes at least one primary parameter of the control system; receive second monitored information from analog sensors over a second channel for the control system, the second monitored information being analog information, and wherein the second channel is independent of the first channel; determine at least one secondary parameter for the control system responsive to receiving the second monitored information, wherein the at least one secondary parameter is digital information; determine a discrepancy in the at least one primary parameter; and provide an alert responsive to a positive determination of a discrepancy in the at least one primary parameter; wherein the digital sensors and the analog sensors are associated with the control system.

In Example 2, the subject matter of Example 1 can optionally include, wherein the instructions further comprise instructions that when executed cause the one or more of the processors to perform cognitive analysis on the second monitored information.

In Example 3, the subject matter of Example 1-2 can optionally include, wherein the instructions further comprise instructions that when executed cause the one or more of the processors to determine a difference between the at least one primary parameter and the at least one secondary parameter.

In Example 4, the subject matter of Example 1-3 can optionally include, wherein the instructions further comprise instructions that when executed cause the one or more of the processors to compare the at least one secondary parameter with one or more historical or predetermined operational parameters.

In Example 5, the subject matter of Example 1-4 can optionally include, wherein the at least one secondary parameter and the at least one primary parameter include the same operating parameter.

In Example 6, the subject matter of Example 1-5 can optionally include, wherein each of at least one primary parameter and the at least one secondary parameter comprises a vital system health indicator.

In Example 7, the subject matter of Example 1-6 can optionally include, wherein the instructions further comprise instructions that when executed cause the one or more of the processors to shut-down the control system responsive to receiving the notification.

Example 8 is a method for cognitive protection of a control system, comprising: receiving first monitored information from primary sensors over a first channel, wherein the first monitored information includes at least one primary parameter of the control system; receiving second monitored information from secondary sensors over a second channel for the control system, wherein the second channel is independent of the first channel; determining at least one secondary parameter for the control system responsive to receiving the second monitored information; determining a discrepancy in the at least one primary parameter; and providing an alert when there is a discrepancy in the at least one primary parameter; wherein the primary sensors and the secondary sensors are associated with the control system.

In Example 9, the subject matter of Example 8 can optionally include, further comprising performing cognitive analysis on the second monitored information.

In Example 10, the subject matter of Example 8-9 can optionally include, further comprising determining a difference between the at least one primary parameter and the at least one secondary parameter.

In Example 11, the subject matter of Example 8-10 can optionally include, further comprising comparing the at least one secondary parameter with one or more historical or predetermined operational parameters.

In Example 12, the subject matter of Example 8-11 can optionally include, further comprising determining the at least one secondary parameter includes the same operating parameter as the at least one primary parameter.

In Example 13, the subject matter of Example 8-12 can optionally include, wherein each of at least one primary parameter and the at least one secondary parameter comprises a vital system health indicator.

In Example 14, the subject matter of Example 8-13 can optionally include, further comprising shutting-down the control system responsive to providing the alert.

Example 15 is a method for cognitive protection of a control system, comprising: receiving first monitored information, wherein the first monitored information includes at least one primary parameter of the control system; determining second monitored information, wherein the second monitored information includes at least one secondary parameter for the control system; determining a discrepancy in the at least one primary parameter; and communicating an alert when there is a discrepancy in the at least one primary parameter; transmitting a command to shut-down the control system responsive to receiving the alert; wherein the primary sensors and the secondary sensors are associated with the control system.

In Example 16, the subject matter of Example 15 can optionally include, further comprising determining the second information by cognitive analysis of analog sensor data obtained for the control system.

In Example 17, the subject matter of Example 15-16 can optionally include, further comprising determining a value that is a difference between the at least one primary parameter and the at least one secondary parameter.

In Example 18, the subject matter of Example 15-17 can optionally include, further comprising determining the discrepancy based on a comparison of the at least one secondary parameter with one or more historical or predetermined operational parameters.

In Example 19, the subject matter of Example 15-18 can optionally include, wherein the at least one secondary parameter and the at least one primary parameter include the same operating parameter.

In Example 20, the subject matter of Example 15-19 can optionally include, wherein each of at least one primary parameter and the at least one secondary parameter comprises a vital system health indicator.

In Example 21, the subject matter of Example 15-20 can optionally include, wherein the at least one primary parameter and the at least one secondary parameter includes digital information.

Example 22 is one or more machine readable media, on which are stored instructions, comprising instructions that when executed by a processor cause a machine to: receive first monitored information from digital sensors over a first channel for a control system, wherein the first monitored information is digital information and includes at least one primary parameter of the control system; receive second monitored information from analog sensors over a second channel for the control system, the second monitored information being analog information, and wherein the second channel is independent of the first channel; determine at least one secondary parameter for the control system responsive to receiving the second monitored information, wherein the at least one secondary parameter is digital information; determine a discrepancy in the at least one primary parameter; and provide an alert responsive to a positive determination of a discrepancy in the at least one primary parameter; wherein the digital sensors and the analog sensors are associated with the control system.

In Example 23, the subject matter of Example 22 can optionally include, wherein the instructions to determine the at least one secondary parameter further comprises instructions that when executed cause the machine to perform cognitive analysis on the second monitored information.

In Example 24, the subject matter of Example 22-23 can optionally include, wherein the instructions to determine the discrepancy further comprises instructions that when executed cause the machine to determine a difference between the at least one primary parameter and the at least one secondary parameter.

In Example 25, the subject matter of Example 22-24 can optionally include, wherein the instructions to determine the discrepancy further comprises instructions that when executed cause the machine to compare the at least one secondary parameter with one or more historical or predetermined operational parameters.

In Example 26, the subject matter of Example 22-25 can optionally include, wherein the at least one secondary parameter and the at least one primary parameter include the same operating parameter.

In Example 27, the subject matter of Example 22-26 can optionally include, wherein each of at least one primary parameter and the at least one secondary parameter comprises a vital system health indicator.

In Example 28, the subject matter of Example 22-27 can optionally include, wherein the primary sensors are digital sensors and the secondary sensors are analog sensors.

In Example 29, the subject matter of Example 22-28 can optionally include, wherein the instructions further comprise instructions that when executed cause the machine to shut-down the control system responsive to receiving the notification.

It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention therefore should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.