Description:.

SYSTEM AND METHOD FOR ENHANCED ANOMALY DETECTION IN INDUSTRIAL IOT VIA MULTI-MODAL SENSOR FUSION

Field of the Invention

Generally, the present disclosure relates to data processing in Industrial Internet of Things (IIoT) systems. Particularly, the present disclosure relates to a method for Multi-Modal Sensor Fusion and Anomaly Detection.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
In recent years, the Industrial Internet of Things (IIoT) has emerged as a transformative force in various sectors, enhancing operational efficiency, reliability, and safety. IIoT systems leverage interconnected sensors and devices to monitor and manage industrial processes in real-time. The deployment of diverse sensors, including temperature control, pressure, vibration, and acoustic sensors, plays a critical role in capturing a wide range of data essential for operational oversight and decision-making. However, the sheer volume and variety of sensor data pose significant challenges in terms of data processing and analysis.
Data preprocessing is a crucial step in managing the data obtained from IIoT sensors. Techniques such as noise reduction, data normalization, outlier removal, and signal enhancement are employed to refine the sensor data, making it suitable for further analysis. Despite the application of these techniques, the complexity and heterogeneity of sensor data often necessitate more sophisticated approaches to ensure effective data integration and analysis.
The fusion of sensor data, wherein information from disparate sensors is integrated into a unified data format, represents a vital process in overcoming the challenges associated with data diversity. Traditional methods for sensor data fusion often lack the flexibility and scalability required to handle the dynamic nature of IIoT environments. Moreover, these methods may not adequately address the intricate relationships between different types of sensor data, limiting the potential for comprehensive insights.
Upon preprocessing and fusion of sensor data, the identification of potential security threats and equipment malfunctions becomes a paramount concern. Anomaly detection, particularly through the use of artificial intelligence techniques, has gained prominence as a means to discern patterns indicative of malfunctions or cyber threats. While AI-driven anomaly detection offers substantial advantages in terms of accuracy and efficiency, the development of models that can accurately mimic genuine services and assets for cybersecurity purposes remains a complex task. Furthermore, the effective storage and visualization of anomaly detection results for timely human review and action present additional challenges.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and techniques for enhancing security and monitoring in IIoT systems through multi-modal sensor fusion and AI-driven anomaly detection.
Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
In an aspect, the present disclosure provides a method and system for Multi-Modal Sensor Fusion and Anomaly Detection in Industrial IoT systems. Data is collected from a multitude of sensors, encompassing temperature control, pressure, vibration, and acoustic sensors. This collected data undergoes preprocessing to ensure normalization and cleansing, employing techniques such as noise reduction, data normalization, outlier removal, or signal enhancement. A data fusion module integrates various sensor data into a unified format, followed by the execution of an anomaly detection module on the processed data to identify potential security threats and equipment malfunctions. Anomalies are detected using an artificial intelligence technique designed to emulate genuine services and assets for enhanced cybersecurity. The detection results are stored in a database and anomaly records, with visualization tools provided for human review and immediate action. The anomaly detection module is designed to adapt its behavior in response to potential intrusions, enhancing its effectiveness. Additionally, real-time notifications containing anomaly details, recommended actions, and a priority level are sent to maintenance teams or cybersecurity personnel upon anomaly detection. The anomaly detection algorithm includes a machine learning model for establishing a baseline of normal operational parameters, real-time analysis for deviation identification, a feedback mechanism for continuous model improvement, an alert system for anomaly categorization and notification, and integration with decentralized ledger technology for data integrity and traceability. Sensor calibration based on historical data is also included to refine anomaly detection accuracy.
Furthermore, a system for implementing the aforementioned method is described. It comprises a plurality of sensors for data collection, a preprocessing module for data cleaning and normalization, a data fusion module for integrating and unifying sensor data, and an artificial intelligence-powered anomaly detection module. A database stores anomaly detection results, and a visualization interface displays detected anomalies. The anomaly detection module adapts its algorithms to respond to potential intrusions, and a notification unit alerts relevant personnel upon anomaly detection.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a method relates to a systematic procedure or technique for Multi-Modal Sensor Fusion and Anomaly Detection in IIoT systems, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a block diagram for a system (200) to perform Multi-Modal Sensor Fusion and Anomaly Detection within IIoT systems, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates an architectural overview of a multi-modal sensor fusion and anomaly detection system for the IIOT, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates a sequential flowchart for a multi-modal sensor fusion and anomaly detection process within an IIOT, in accordance with the embodiments of the present disclosure.

Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a method relates to a systematic procedure or technique for Multi-Modal Sensor Fusion and Anomaly Detection in Industrial Internet of Things (IIoT) systems, in accordance with the embodiments of the present disclosure. In step (102), the method (100) comprises collecting data from a plurality of sensors (202). The data collection process involves gathering information relevant to IIoT operations, employing sensors (202) selected based on the specific requirements of temperature control, pressure, vibration, and acoustic monitoring. Following the data collection, step (104) involves preprocessing the collected data to normalize and clean the data for further analysis. The preprocessing step is critical for ensuring the quality and reliability of sensor data, incorporating techniques such as noise reduction, data normalization, outlier removal, or signal enhancement. This preprocessing facilitates the preparation of sensor data for subsequent fusion and analysis processes. Subsequently, step (106) comprises fusing sensor data using a data fusion module (206). The data fusion module (206) is designed to integrate various types of sensor data into a unified data format. This integration is pivotal for creating a cohesive understanding of the monitored environment, enabling more effective and comprehensive analysis of the collected data. In step (108) further comprises executing an anomaly detection module (208) on the preprocessed and fused data. The anomaly detection module (208) is tasked with identifying potential security threats and equipment malfunctions. This execution involves analyzing the integrated sensor data to detect irregularities or patterns indicative of anomalous behavior, leveraging advanced analytical techniques to ensure the timely identification of potential issues. In step (110), anomalies are detected using an artificial intelligence technique designed to mimic genuine services and assets for cybersecurity purposes. This approach employs sophisticated AI algorithms to enhance the accuracy and efficiency of anomaly detection, contributing to the robustness of cybersecurity measures within IIoT systems. Additionally, step (112) comprises storing the results of the anomaly detection in a time service database (210) and anomaly records. This storage enables the organized management of detection outcomes, facilitating easy access and analysis of historical data for trend identification and security assessment. Further, the step (114) involves visualizing the stored data and detected anomalies for human review and immediate action. Visualization plays a crucial role in translating complex data into understandable and actionable insights, ensuring that operational personnel can effectively respond to identified anomalies and threats.
In an embodiment, the anomaly detection module (208) of the method (100) adapts its behavior in response to potential intrusions. This adaptive behavior is crucial for maintaining the efficacy of the anomaly detection process in dynamic IIoT environments where security threats evolve rapidly. The adaptation mechanism within the anomaly detection module (208) involves analyzing detected anomalies and adjusting detection parameters or algorithms based on the nature and characteristics of the potential intrusions. This ensures that the anomaly detection module (208) remains effective against a wide range of security threats, thereby enhancing the overall security posture of the Industrial Internet of Things (IIoT) systems. Such an adaptive approach not only improves the responsiveness of the anomaly detection module (208) to emerging threats but also reduces the likelihood of false positives and negatives, thereby increasing the reliability of the anomaly detection process.
In another embodiment, upon the detection of an anomaly, the method (100) further comprises notifying maintenance teams or cybersecurity personnel in real-time. This notification process is an integral part of the method (100), ensuring prompt and effective response to potential issues. The notifications generated include comprehensive details of the detected anomaly, recommended actions for addressing the issue, and a priority level that reflects the severity of the detected anomaly. Such detailed notifications enable maintenance teams and cybersecurity personnel to quickly assess the situation and take appropriate actions, minimizing potential disruptions or damages. The inclusion of a priority level in the notifications facilitates the efficient allocation of resources, ensuring that critical issues are addressed promptly while less severe anomalies are managed according to their impact on the system.
In a further embodiment, the anomaly detection algorithm module within the method (100) is described in detail, comprising several components that enhance its anomaly detection capabilities. A machine learning model, trained on historical sensor data, establishes a baseline for normal operational parameters, allowing for the identification of deviations indicative of an anomaly through real-time analysis. This baseline serves as a reference point against which current sensor data is compared, enabling the accurate detection of anomalies. Additionally, a feedback mechanism updates the machine learning model with new sensor data, continuously improving detection accuracy. An alert system categorizes detected anomalies based on their severity and routes notifications to the appropriate personnel, ensuring a timely and coordinated response. Furthermore, the integration of the anomaly detection algorithm with decentralized ledger technology enhances the integrity and traceability of sensor data and anomaly detection results, providing a secure and verifiable record of all activities.
In another embodiment related to the method (100), a step of calibrating the sensors (202) based on historical data is included to improve the accuracy of anomaly detection. Sensor calibration is essential for ensuring that the data collected from the sensors (202) is accurate and reliable, which in turn, increases the effectiveness of the anomaly detection process. By adjusting the sensors' (202) operational parameters according to historical data, potential inaccuracies in sensor readings can be minimized, leading to more precise detection of anomalies. Calibration based on historical data allows for the adjustment of sensors (202) in anticipation of known issues or environmental conditions, further enhancing the system's ability to identify true anomalies. This calibration step is particularly important in complex IIoT environments where sensor performance may vary due to a range of factors, ensuring that the anomaly detection module (208) receives high-quality data for analysis.
The term "system" as used throughout the present disclosure relates to an integrated arrangement of hardware and software components designed to perform Multi-Modal Sensor Fusion and Anomaly Detection within Industrial Internet of Things (IIoT) systems.
The term "plurality of sensors" as used throughout the present disclosure pertains to various devices capable of detecting and measuring environmental parameters such as temperature, pressure, vibration, and acoustic levels. These sensors are fundamental to the operation of the system, facilitating the collection of diverse data necessary for comprehensive monitoring and analysis.
The term "preprocessing module" as used throughout the present disclosure refers to a software component tasked with preparing the data collected from sensors for further processing. This component is responsible for cleaning and normalizing the data by removing noise, standardizing data formats, and eliminating outliers to ensure the integrity and usability of the data for fusion and analysis.
The term "data fusion module" as used throughout the present disclosure denotes a software mechanism that amalgamates data from multiple sensors into a coherent and unified dataset, thereby facilitating a comprehensive analysis of the monitored environment. This module plays a critical role in synthesizing data from disparate sources to provide a holistic view of the operational state of the IIoT system.
The term "anomaly detection module" as used throughout the present disclosure describes a software system that employs artificial intelligence algorithms to analyze fused sensor data for the identification of deviations that may signify potential security threats or equipment malfunctions. This component leverages machine learning and pattern recognition techniques to mimic genuine services and assets, enhancing the system's capability to detect anomalies accurately.
The term "database" as used throughout the present disclosure relates to a storage system designed to securely hold the results of the anomaly detection process, ensuring data integrity and availability for future reference or analysis. This storage system is serves as a critical repository for information regarding detected anomalies, facilitating effective data management and retrieval.
The term "visualization interface" as used throughout the present disclosure refers to a user interface designed to display information about detected anomalies in a manner that is easily interpretable by human operators. This component enables the presentation of complex data in a visual format, aiding in the quick identification and resolution of issues highlighted by the anomaly detection module.
FIG. 2 illustrates a block diagram for a system (200) to perform Multi-Modal Sensor Fusion and Anomaly Detection within IIoT systems, in accordance with the embodiments of the present disclosure. Said system (200) includes a plurality of sensors (202), which are operative to collect various forms of data, including, but not limited to, temperature, pressure, vibration, and acoustic data. Adjacent to the plurality of sensors (202), a preprocessing module (204) is provided. Said preprocessing module (204) is adapted for cleaning and normalizing the data received from the sensors (202). The operations of preprocessing module (204) include noise reduction, data normalization, and outlier removal, which are essential for preparing the sensor data for further analysis. Data fusion module (206) is illustrated as coupled to said preprocessing module (204). Said data fusion module (206) is configured to integrate the preprocessed data into a unified format, enabling comprehensive analysis across sensor types. This integration is critical to the effective functioning of the anomaly detection process. Anomaly detection module (208) is employed within system (200) to analyze the fused data for potential security threats and equipment malfunctions using artificial intelligence algorithms. These algorithms are particularly crafted to replicate genuine services and assets for advanced cybersecurity applications. A database (210) is illustrated as part of system (200), which is responsible for storing the results of the anomaly detection conducted by anomaly detection module (208). Said database (210) allows for structured record maintenance and efficient data accessibility. Additionally, system (200) comprises a visualization interface (212), designed to display the stored data and any detected anomalies, thus enabling human operators to undertake review and corrective action expediently. This visualization ensures that the data processed is readily interpretable and actionable, providing an essential tool for system monitoring and management within IIoT environments.
In an embodiment concerning the system (200), the anomaly detection module (208) is specifically configured to adapt its detection algorithms in response to the behavior of potential intrusions. This adaptation capability is a critical enhancement to the anomaly detection process, enabling the module (208) to dynamically adjust its analytical models based on the evolving nature of cybersecurity threats. Such adaptability ensures that the anomaly detection module (208) remains effective against a broad spectrum of security challenges, enhancing the system's ability to safeguard Industrial Internet of Things (IIoT) environments from novel and sophisticated intrusions. The adaptive behavior of the anomaly detection module (208) involves continuous learning and updating of detection algorithms, leveraging real-time data analysis to refine its detection capabilities. This not only improves the accuracy of anomaly detection but also significantly reduces the likelihood of false positives and negatives, thereby ensuring that security resources are optimally allocated. The ability of the anomaly detection module (208) to adapt its detection algorithms enhances the overall resilience of the IIoT system against cyber threats, contributing to the maintenance of operational integrity and the protection of critical industrial assets.
In another embodiment of the system (200), an enhancement includes the incorporation of a notification unit, an integral component designed to facilitate timely communication with maintenance teams or cybersecurity personnel upon the detection of an anomaly. This notification unit is crucial for ensuring that detected anomalies are promptly addressed, minimizing potential disruptions or damages to the IIoT environment. Upon identification of an anomaly by the anomaly detection module (208), the notification unit automatically generates alerts containing detailed information about the anomaly, including its nature, location, and recommended actions for mitigation. These alerts are then disseminated to relevant personnel in real-time, enabling swift assessment and response to the detected issue. The inclusion of a notification unit in the system (200) significantly improves the operational responsiveness to potential security threats and equipment malfunctions, ensuring that maintenance and cybersecurity teams are well-informed and prepared to take immediate action. Furthermore, the capability to prioritize notifications based on the severity of detected anomalies ensures that critical issues receive immediate attention, optimizing the allocation of response efforts and resources. This feature enhances the system's efficacy in managing and mitigating anomalies, thereby contributing to the reliability and security of IIoT operations.
FIG. 3 illustrates an architectural overview of a multi-modal sensor fusion and anomaly detection system for the IIOT, in accordance with the embodiments of the present disclosure. The process begins with the input from various sensors, which can be configured to capture different types of environmental and machinery-related data. Four specific types of sensors are depicted: temperature control, pressure, vibration, and acoustic sensors, each contributing a data stream that reflects a different aspect of the operational context. Once the data is collected, the system performs anomaly detection, and concurrently, the data passes through a data fusion module that consolidates and synergized data from the multiple sensors, creating a cohesive data stream of the sensor inputs. Anomaly detection is performed both prior to and after the fusion process, indicating that anomalies can be detected at the individual sensor level as well as in the integrated data set. After processing, the data is organized and logs into the database. Further, the processed and stored data is displayed on a computer interface that allows users to interact with and interpret the information. This visual representation aids in making informed decisions about the system’s performance and necessary interventions.
FIG. 4 illustrates a sequential flowchart for a multi-modal sensor fusion and anomaly detection process within an IIOT, in accordance with the embodiments of the present disclosure. This procedural flow enables maintenance of the integrity and operational efficiency of industrial systems by preemptively identifying potential malfunctions or deviations from expected patterns. Following initiation, the system engages data collection step, where multiple sensors gather varied operational data. Subsequently, the raw data acquired from the sensors undergoes initial processing, which may include noise reduction, normalization, or other forms of data cleansing. The processing refines the data for analysis, removing any irrelevant or extraneous information that could lead to inaccurate anomaly detection. After preprocessing, the preprocessed data from diverse sensors, synthesizing disparate data streams into a coherent data stream to exploit the complementary nature of different sensor inputs, enhancing the reliability and accuracy of the subsequent anomaly detection. Further, the processed and fused data is analyzed using machine learning techniques to detect any anomalies or patterns that deviate from the norm. Machine learning techniques can be based on statistical models, or other advanced data analysis techniques that can discern irregularities that might indicate equipment malfunctions, process deviations, or environmental factors that require attention. Upon the identification of an anomaly, the system triggers an alert to the relevant personnel, prompting them to take the necessary measures to address the issue. So that potential problems are addressed promptly, reducing downtime and preventing further complications.
The system (as disclosed in present disclosure to determine abnormality) can be used industrial IoT sphere. Primarily, system enhances anomaly detection by incorporating various sensor types and utilizing advanced machine learning techniques, including generative adversarial networks and attention-based autoencoders. This integration enables precise detection of irregularities in industrial processes, facilitating early intervention that prevents disruptions, reduces maintenance costs, and ensures seamless production. Further, system provides safeguards industrial environments by employing real-time anomaly detection and incident response protocols. The system may utilize the MQTT protocol to enable agile communication, which is vital for maintaining data integrity and thwarting cyber threats in smart manufacturing plants. For real-time monitoring, the invention leverages multi-modal sensor fusion techniques that enable immediate processing of data from multiple sensors, enhancing situational awareness and providing a comprehensive view of industrial operations. This capability is crucial for promptly identifying and addressing potential issues. Furthermore, utilizes decoy systems (that mimic genuine network assets) utilizes AI to adapt to intrusion attempts. This strategy is effective in misdirecting and impeding attackers, allowing for rapid analysis of telemetry data to spot anomalies indicative of a security breach. As a result, cybersecurity professionals can preempt significant cyberattacks. The predictive maintenance capabilities utilizes sensors to anticipate equipment failure, thus mitigating downtime and financial loss. System also enables real-time response by quickly analyzing sensor data, leading to immediate action and efficient operational decision-making.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claim:

A method (100) for Multi-Modal Sensor Fusion and Anomaly Detection in Industrial IoT systems, comprising:
a. collecting data from a plurality of sensors (202), selected from at least temperature control, pressure, vibration, and acoustic sensors;
b. preprocessing the collected data to normalize and clean the data for further analysis, wherein the preprocessing step includes at least one of: noise reduction, data normalization, outlier removal, or signal enhancement;
c. fusing sensor data using a data fusion module (206) that integrates various types of sensor data into a unified data format;
d. executing an anomaly detection module (208) on the preprocessed and fused data to identify potential security threats and equipment malfunctions;
e. detecting anomalies using artificial intelligence technique to mimic genuine services and assets for cybersecurity purposes;
f. storing the results of the anomaly detection in a time service database (210) and anomaly records;
g. visualizing the stored data and detected anomalies for human review and immediate action.
The method (100) of claim 1, wherein the anomaly detection module (208) adapts behavior in response to potential intrusions.
The method (100) of claim 1, further comprising notifying maintenance teams or cybersecurity personnel in real-time upon detection of anomaly, wherein the notification includes details of the anomaly, recommended actions, and a priority level based on the severity of the detected anomaly.
The method (100) of claim 1, wherein the anomaly detection algorithm module comprises:
a. A machine learning model trained on historical sensor data to establish a baseline for normal operational parameters;
b. A real-time analysis component that compares current sensor data against the baseline to identify deviations indicative of an anomaly;
c. A feedback mechanism that updates the machine learning model with new sensor data to continuously improve detection accuracy;
d. An alert system that categorizes detected anomalies based on severity and routes notifications to appropriate maintenance or cybersecurity personnel;
e. Integration with a decentralized ledger technology to ensure the integrity and traceability of sensor data and anomaly detection results.
The method (100) of claim 1, further comprising a step of calibrating the sensors based on historical data to improve accuracy in anomaly detection.
A system (200) for Multi-Modal Sensor Fusion and Anomaly Detection in Industrial IoT, comprising:
a. a plurality of sensors (202) for collecting data;
b. a preprocessing module (204) for cleaning and normalizing data from the sensors;
c. a data fusion module (206) configured to integrate and unify data from the various sensors;
d. an anomaly detection module (208) using artificial intelligence algorithms capable of mimicking genuine services and assets to identify potential security threats and equipment malfunctions;
e. a database (210) for storing the results of the anomaly detection;
f. a visualization interface (212) for displaying the detected anomalies to users.
The system (200) of claim 4, wherein the anomaly detection module (208) is configured to adapt its detection algorithms based on the behavior of potential intrusions.
The system (200) of claim 4, further comprising a notification unit for alerting maintenance teams or cybersecurity personnel upon the detection of an anomaly.

SYSTEM AND METHOD FOR ENHANCED ANOMALY DETECTION IN INDUSTRIAL IOT VIA MULTI-MODAL SENSOR FUSION

The present disclosure provides a method for Multi-Modal Sensor Fusion and Anomaly Detection in Industrial IoT systems. The method comprises collecting data from a plurality of sensors selected from at least temperature control, pressure, vibration, and acoustic sensors. Preprocessing the collected data to normalize and clean the data for further analysis is included, wherein the preprocessing step includes at least one of noise reduction, data normalization, outlier removal, or signal enhancement. A data fusion module that integrates various types of sensor data into a unified data format is used for fusing sensor data. An anomaly detection module is executed on the preprocessed and fused data to identify potential security threats and equipment malfunctions. Anomalies are detected using an artificial intelligence technique to mimic genuine services and assets for cybersecurity purposes. The results of the anomaly detection are stored in a time service database and anomaly records. Additionally, the stored data and detected anomalies are visualized for human review and immediate action.

Drawings
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FIG. 1
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FIG. 2
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FIG. 3
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FIG. 4
, Claims:I/We claim:

A method (100) for Multi-Modal Sensor Fusion and Anomaly Detection in Industrial IoT systems, comprising:
a. collecting data from a plurality of sensors (202), selected from at least temperature control, pressure, vibration, and acoustic sensors;
b. preprocessing the collected data to normalize and clean the data for further analysis, wherein the preprocessing step includes at least one of: noise reduction, data normalization, outlier removal, or signal enhancement;
c. fusing sensor data using a data fusion module (206) that integrates various types of sensor data into a unified data format;
d. executing an anomaly detection module (208) on the preprocessed and fused data to identify potential security threats and equipment malfunctions;
e. detecting anomalies using artificial intelligence technique to mimic genuine services and assets for cybersecurity purposes;
f. storing the results of the anomaly detection in a time service database (210) and anomaly records;
g. visualizing the stored data and detected anomalies for human review and immediate action.
The method (100) of claim 1, wherein the anomaly detection module (208) adapts behavior in response to potential intrusions.
The method (100) of claim 1, further comprising notifying maintenance teams or cybersecurity personnel in real-time upon detection of anomaly, wherein the notification includes details of the anomaly, recommended actions, and a priority level based on the severity of the detected anomaly.
The method (100) of claim 1, wherein the anomaly detection algorithm module comprises:
a. A machine learning model trained on historical sensor data to establish a baseline for normal operational parameters;
b. A real-time analysis component that compares current sensor data against the baseline to identify deviations indicative of an anomaly;
c. A feedback mechanism that updates the machine learning model with new sensor data to continuously improve detection accuracy;
d. An alert system that categorizes detected anomalies based on severity and routes notifications to appropriate maintenance or cybersecurity personnel;
e. Integration with a decentralized ledger technology to ensure the integrity and traceability of sensor data and anomaly detection results.
The method (100) of claim 1, further comprising a step of calibrating the sensors based on historical data to improve accuracy in anomaly detection.
A system (200) for Multi-Modal Sensor Fusion and Anomaly Detection in Industrial IoT, comprising:
a. a plurality of sensors (202) for collecting data;
b. a preprocessing module (204) for cleaning and normalizing data from the sensors;
c. a data fusion module (206) configured to integrate and unify data from the various sensors;
d. an anomaly detection module (208) using artificial intelligence algorithms capable of mimicking genuine services and assets to identify potential security threats and equipment malfunctions;
e. a database (210) for storing the results of the anomaly detection;
f. a visualization interface (212) for displaying the detected anomalies to users.
The system (200) of claim 4, wherein the anomaly detection module (208) is configured to adapt its detection algorithms based on the behavior of potential intrusions.
The system (200) of claim 4, further comprising a notification unit for alerting maintenance teams or cybersecurity personnel upon the detection of an anomaly.

SYSTEM AND METHOD FOR ENHANCED ANOMALY DETECTION IN INDUSTRIAL IOT VIA MULTI-MODAL SENSOR FUSION