A multi-persistence cognitive embedding (MPCE) system for device authentication in Industrial Internet of Things (IIoT) networks

Abstract

A system for device authentication in Industrial Internet of Things (IIoT) networks, consisting of: a device behavior monitoring module configured to capture device communication patterns, including time intervals between packets, protocol usage, packet sizes, and communication frequency; a multi-persistence homology processor configured to extract topological features from the captured device communication patterns across multiple timescales and generate persistent features representing device behavior; a cognitive behavioral profiling engine configured to generate cognitive fingerprints based on the device's communication patterns and compute cognitive signatures as multi-persistence embeddings, with the developed cognitive behavioral signatures being stored in a database; an embedding generator configured to compress the topological features and cognitive signatures into low-dimensional behavioral signatures using at least one autoencoder and reservoir computing technique; an authentication module configured to compare current behavioral signatures with behavioral signatures stored in the database and to authenticate devices based on the signature match; and a reinforcement learning module configured to adaptively update authentication thresholds based on real-time device behavior and optimize decision parameters through feedback learning.

Description

FIELD OF THE INVENTIONThe present disclosure relates to a multi-persistence cognitive embedding (MPCE) system for device authentication in industrial Internet of Things (IIoT) networks. More particularly, the present invention relates to a multi-persistence cognitive embedding (MPCE)-based system configured for device authentication in the industrial Internet of Things (IIoT), the system incorporating topological data analysis (TDA), cognitive behavior profiling, and machine learning techniques to generate lightweight device authentication signatures.BACKGROUND OF THE INVENTIONIn recent years, the industrial Internet of Things (IIoT) has experienced rapid growth and networked many devices in industrial environments. However, securing these networked devices presents significant challenges, particularly in device authentication. Conventional authentication methods are predominantly based on resource-intensive cryptographic techniques such as ECC and RSA or complex challenge-response protocols. These approaches, while secure, do generate significant computational and power consumption on resource constrained IIoT devices and are therefore impractical for real-time authentication scenarios.Current authentication mechanisms do not address the unique limitations of IIoT environments where devices often operate with limited processing power, storage capacity, and power resources. The static nature of existing solutions further exacerbates this problem because they cannot adapt to emerging device behavior and newly arising safety threats. This rigidity makes IIoT networks vulnerable to sophisticated attacks that exploit the invariable nature of authentication protocols.Despite advances in authentication technologies, the application of topological data analysis (TDA) and behavioral biometrics in IIoT security remains largely unexplored. In particular, the potential of multi-persistence concepts in TDA for creating lightweight, adaptive authentication mechanisms has not yet been studied. This gap is significant because TDA could provide a more efficient approach to device authentication by analyzing behavior patterns without causing large computational loads.The integration of behavioral biometrics with multi-persistence cognitive embeds represents an unexplored possibility in IIoT authentication. While behavioral biometric authentication is promising in other areas, its potential for creating unique device signatures in IIoT networks, especially in combination with topological analyses, remains unused. This background emphasizes the need for innovative authentication solutions that conform security requirements to the resource constraints of IIoT devices while offering flexibility to changing network conditions.In view of the foregoing, the present invention provides a multi-persistence cognitive embedding (MPCE) system for device authentication in industrial Internet of Things (IIoT) networks, the system incorporating topological data analysis (TDA), cognitive behavior profiling, and machine learning techniques to generate lightweight device authentication signatures.SUMMARY OF THE INVENTIONThe present disclosure relates to a multi-persistence cognitive embedding (MPCE) system for device authentication in industrial internet of things (IIoT) networks. The Multi-Persistence Cognitive Embedding (MPCE) system integrates topological data analysis (TDA), cognitive behavior profiling, and machine learning to generate efficient device authentication signatures for Industrial Internet of Things (IIoT) devices. By utilizing multi-persistence homology, the system models the temporal and structural interaction patterns of devices and captures their unique behavior across multiple scales in a computationally easy manner.The present disclosure aims to provide an MPCE (Multi-Persistence Cognitive Embedding) system for device authentication in IIoT (Industrial Internet of Things) networks. The system comprises: a device behavior monitoring module configured to acquire device communication patterns including time intervals between packets, protocol usage, packet sizes, and communication frequency; a multi-persistence homology processor configured to extract topological features from the acquired device communication patterns across multiple time scales and to generate persistent features representing the device behavior; a cognitive behavior profiling engine configured to generate cognitive fingerprints based on the device communication patterns and to calculate cognitive signatures as multi-persistence embeddings, wherein developed cognitive behavior signatures are stored in a database; an embedding generator configured to compress the topological features and cognitive signatures into low-dimensional behavioral signatures using at least one autoencoder and reservoir computing technique; an authentication module configured to compare current behavioral signatures to behavioral signatures stored in the database and authenticate devices based on signature matches; and a reinforcement learning module configured to adaptively update authentication thresholds based on real-time device behavior and optimize decision parameters by feedback learning.An object of the present disclosure is to provide a multi-persistence cognitive embedding (MPCE) system for device authentication in industrial Internet of Things (IIoT) networks.Another object of the present disclosure is to develop a robust and efficient device authentication system tailored to IIoT environments.Another object of the present disclosure is to utilize multi-persistence homology to model and detect unique device interaction patterns over time.Another object of the present disclosure is to reduce computational effort while maintaining high accuracy in profile creation and authentication of device behavior.Another object of the present disclosure is to improve security in IIoT networks by identifying and embedding persistent device functions across different observation scales.In order to further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof illustrated in the accompanying drawings. It should be noted that these drawings illustrate only typical embodiments of the invention and are therefore not to be considered as limiting the scope thereof. The invention will be described and explained with additional details and details with reference to the accompanying drawings.BRIEF DESCRIPTION OF THE FIGURESThese and other features, aspects, and advantages of the present disclosure will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. The following applies here: FIG. 1 shows a block diagram of a multi-persistence cognitive embedding (MPCE) system for device authentication in industrial Internet of Things (IIoT) networks, according to an embodiment of the present disclosure. And, and FIG. 2 is a block diagram illustrating the operation of the proposed system according to an embodiment of the present disclosure.Moreover, those skilled in the art will appreciate that elements are shown in the drawings for simplicity and need not necessarily be drawn to scale. For example, the flowcharts illustrate the method using the major steps to improve understanding of aspects of the present disclosure. Moreover, with respect to the construction of the apparatus, one or more components of the apparatus may have been represented in the drawings by conventional symbols, and the drawings may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawings with details readily apparent to those skilled in the art having the benefit of the description herein.DETAILED DESCRIPTION:In order to aid in the understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation of the scope of the invention is intended, since such changes and further modifications to the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated, as would normally occur to one skilled in the art to which the invention pertains.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrase "in one embodiment," "in another embodiment," and similar language in this specification may or may not all refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps not only comprises those steps, but may also comprise other steps not expressly listed or inherent in such a process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" do not exclude, without further limitations, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The system, methods, and examples provided herein are illustrative only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.The functional units described in this specification have been referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may include executable code and may include, for example, one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construction. Nevertheless, the executable of an identified device need not be physically arranged together, but may include various instructions stored in various locations that, when logically interconnected, form the device and fulfil the stated purpose of the device.Indeed, executable code of a device or module may be a single instruction or multiple instructions, and may even be distributed across multiple different code segments, among different applications, and across multiple storage devices. Similarly, operational data may be identified and illustrated herein within the device and embodied in any suitable form and organized in any suitable type of data structure. The operational data may be acquired as a single dataset or distributed across different locations including different storage devices and may be present at least partially as electronic signals in a system or network.References throughout this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the appearances of the phrases "a selected embodiment," "in one embodiment," or "in one embodiment" in various places in this specification are not necessarily referring to the same embodiment.Moreover, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of the embodiments of the disclosed subject matter. However, one skilled in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail in order to avoid obscuring aspects of the disclosed subject matter.According to the example embodiments, the disclosed computer programs or modules may be executed in many example ways, such as an application stored in a device's memory or a hosted application executing on a server and communicating with the device application or browser via a number of standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in example programming languages that execute from memory on the device or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.Some of the disclosed embodiments include or otherwise include data transfer over a network, such as communication of various inputs or files over the network. The network may include, for example, one or more of the following: Internet, Wide Area Networks (WANs), Local Area Networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network and digital subscriber line (xDS)), radio, television, cable, satellite, and / or any other transmission or tunneling mechanism for transmitting data. The network may include multiple networks or sub-networks, each of which may include, for example, a wired or wireless data path. The network may comprise a circuit switched voice network, a packet switched data network or any other network capable of transmitting electronic communication. For example, the network may comprise networks based on Internet Protocol (IP) or Asynchronous Transfer Mode (ATM), and may support voice using, for example, VoIP, voice over ATM, or other comparable protocols used for voice data communication. In one implementation, the network comprises a mobile telephone network configured to exchange text or SMS messages.Examples of the network include, but are not limited to, a personal area network (PAN), storage area network (SAN), home area network (HAN), campus area network (CAN), local area network (LAN), wide area network (WAN), metropolitan area network (MAN), virtual private network (VPN), enterprise private network (EPN), the Internet, global area network (GAN), and so forth.FIG. 1 shows a block diagram of a multi-persistence cognitive embedding (MPCE) system (100) for device authentication in industrial Internet of Things (IIoT) networks according to an embodiment of the present disclosure.Referring to FIG. 1, the system (100) includes a device behavior monitoring module (102) configured to acquire device communication patterns including time intervals between packets, protocol usage, packet sizes, and communication frequency; a multi-persistence homology processor (104) configured to extract topological features from the acquired device communication patterns across multiple time scales and generate persistent features representing device behavior; a cognitive behavior profiling engine (106) configured to generate cognitive fingerprints based on the device communication patterns and calculate cognitive signatures as multi-persistence embeddings, wherein developed cognitive behavior signatures are stored in a database (108); an embedding generator (110) configured to compress the topological features and cognitive signatures into low-dimensional behavioral signatures using at least one autoencoder and reservoir computing technique; an authentication module (112) configured to compare current behavioral signatures to behavioral signatures stored in the database (108) and authenticate devices based on signature matching; and a reinforcement learning module (114) configured to adaptively update authentication thresholds based on real-time device behavior and optimize decision parameters by feedback learning.In one embodiment, the multi-persistence homology processor (104) is also configured to analyze temporal dependencies in device communication patterns, extract short-term behavior characteristics, and extract long-term persistent behavior characteristics that remain stable across network changes.In one embodiment, the cognitive behavior profiling engine (106) includes: an energy consumption monitor, a protocol usage analyzer, a communication interval tracker, and a packet size analyzer, each component contributing to the generation of the cognitive fingerprint.In one embodiment, the embedding generator (110) includes a reservoir computing module (110a) configured to model temporal dependencies in device behavior data and generate time-series embeddings; and an autoencoder module (110b) configured to reduce dimensionality of the topological features and generate compressed behavior signatures.In one embodiment, the system (100) further comprises an anomaly detection module (116) configured to monitor device behavior in real time, detect deviations from expected behavior patterns, and trigger adaptive safety mechanisms upon detecting anomalies.In one embodiment, the reinforcement learning module ( 114) implements a deep Q network (DQN) that enables optimization of the authentication parameters based on the success rates of authentication, the false positives rates, and the computational effort.In one embodiment, the authentication module (112) is further configured to manage a historical behavior signature database (108), calculate similarity values between current and stored signatures, apply adaptive thresholds for authentication decisions, and update stored signatures based on validated device behavior.In one embodiment, the system (100) further comprises a resource optimization module (118) configured to monitor computational effort, track power consumption, and adjust authentication parameters to maintain efficiency on devices with constrained resources.In one embodiment, the device behavior monitoring module (102) implements packet acquisition mechanisms, protocol identification, temporal pattern analysis, and power consumption monitoring to generate comprehensive device interaction profiles.In one embodiment, the system (100) is configured to operate distributed across multiple IIoT devices, synchronize behavioral signatures across network nodes, and maintain authentication capability during interruptions of network connectivity.The present invention relates to a multi-persistence cognitive embedding (MPCE) system for device authentication in industrial Internet of Things (IIoT) networks, the system configured to utilize topological data analysis (TDA), cognitive behavior profiling, and machine learning techniques to generate light device authentication signatures.FIG. 2 is a block diagram illustrating the operation of the proposed system according to an embodiment of the present disclosure.Referring to FIG. 2, the Multi-Persistence Cognitive Embedding (MPCE) system integrates topological data analysis (TDA), cognitive behavior profiling, and machine learning techniques to generate lightweight authentication signatures for devices. The present invention uses multi-persistence homology to effectively model the temporal and structural behavior of IIoT devices. The system is configured to capture unique device interaction patterns over time, present this behavior in a topological structure, and embed its persistent features across multiple observation scales. The invention ensures computing power and simultaneously creates an accurate profile of device behavior in network environments.The Multi-Persistence Cognitive Embedding (MPCE) system includes multiple key components to ensure efficient and secure device authentication in IIoT environments. In essence, multi-persistence homology is used to extract topological features from time-series data, such as communication patterns of devices, across multiple time scales. These persistent features capture unique and evolving behaviors of devices in their interaction within the network and identify long-term behaviors that remain stable over time and over network changes. Cognitive behavior profiles are another important element of the MPCE system, in which cognitive fingerprints are generated based on interaction patterns. These patterns include attributes such as packet size, communication intervals, protocol usage, and power consumption. Over time, these cognitive signatures are stored as multi-persistence embeds that summarize the different behavioral characteristics of each device. To make these features computationally efficient, embedding techniques such as autoencoder, reservoir computing, or deep learning based techniques are applied to the multi-persistence homology data. This process compresses the information into low-dimensional, robust signatures, focusing on the most important topological features while minimizing the energy and computational effort. These easy embeddings ensure that the system remains feasible for resource constrained IIoT devices. During authentication, a device presents its current behavioral signature, which is compared to previously stored signatures. This comparison determines whether the current cognitive behavioral fingerprint corresponds to the expected pattern. When significant deviations are detected, possibly due to intruders or anomalies, an alarm is triggered and adaptive safety mechanisms are activated to address the issue.The proposed system implements several advanced algorithms to create a lightweight and efficient authentication mechanism for IIoT devices. Multi-persistence homology, an extended extension of persistent homology, is used to analyze time-series data such as device communication patterns. This technique captures both short-term and long-term persistent features across multiple time scales. Reservoir computing, in particular echo state networks, models the behavior data of devices over time and effectively captures temporal dependencies that are indispensable for embedding device behavior in low-dimensional cognitive signatures. To ensure efficient processing, autoencoders are used to reduce dimensionality of topological features derived from multi-persistence homology, thereby enabling lightweight and yet robust behavioral analyses. Embeddings. In addition, reinforcement learning techniques such as Q-learning or deep Q-networks dynamically update the authentication thresholds and optimize decision making based on device behavior and feedback in real-time.For training and feature extraction, the system uses the XIIoT ID record of cars, which provides comprehensive data for network traffic of IIoT devices. This data set is of critical importance for identifying various communication patterns and behavioral characteristics to generate cognitive signatures. Features such as time intervals between communication packets, protocols used (e.g., TCP and UDP), packet sizes and distributions, and frequency of device communication are extracted. These features are processed to derive multi-persistence homology and cognitive behavioral signatures for authentication purposes.The system is evaluated using several criteria including authentication success rate, latency, CPU utilization, power consumption, and the ability to detect abnormal behavior. The expected result is a scalable and adaptive authentication framework that integrates topological data analysis and cognitive behavior profiling. The design provides for a lower computational effort and energy consumption and is therefore particularly suitable for resource-limited IIoT devices. Moreover, the system is adaptive and can cope with dynamic changes in network conditions and device behavior over time, thereby ensuring robust and reliable performance in different IIoT environments.To further increase security, reinforcement learning algorithms are employed that allow the authentication process to continually adapt and improve based on feedback. In addition, the system integrates anomaly detection to identify deviations from the expected behavior. This adds another level of protection against attacks and ensures the network's resistance.The use of multi-persistence homology in IIoT device authentication introduces a novel approach to detecting persistent topological features of device behavior. This method provides a more comprehensive and accurate representation of the device interactions and is thus significantly less susceptible to tampering by an opponent compared to conventional authentication techniques.By incorporating cognitive behavior profiles into topological data analysis, the system enables dynamic and adaptive authentication mechanisms. These mechanisms continue to develop parallel to the device interactions and ensure that the authentication process remains slim and responds to changes in behavior over time.Unlike cryptographic techniques that require significant computational resources, this approach focuses on the use of topological features. As a result, it is excellently suited for resource-constrained IIoT devices, since it reduces computational complexity and energy consumption and simultaneously maintains safety and efficiency.Moreover, the integration of reinforcement learning allows the authentication system to dynamically adapt to evolving device behavior and network conditions. This flexibility ensures scalability and robustness so that the system can overcome the challenges of multiple and ever changing IIoT environments.The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow chart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimension, and material usage, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages, and solutions to problems have been described above with respect to certain embodiments. However, the advantages, benefits, solutions to problems, and any components that may result in an advantage, solution occurring or becoming clearer are not to be construed as a critical, required, or essential feature or component of any or all claims.REFERENCES100 A multi-persistence cognitive embedding (MPCE) system for device authentication in industrial internet of things (IIoT) networks 102 device behavior monitoring module 104 multi-persistence homology processor 106 cognitive behavior profile creation engine 108 database 110 embedding generator 110 a reservoir computing module 110 bautoencoder module 112 authentication module 114 reinforcement learning module 116 anomaly detection module 118 resource optimization module 202 topological features extraction 204 cognitive behavior profiles 206 autoencoder 208 lightweight behavior embedding 210 authentication 212 store behavior signature 214 persistence behavior 216 Cognitive Fingerprint 218 Behavior Signature

Claims

A system for device authentication in Industrial Internet of Things (IIoT) networks, comprising: a device behavior monitoring module configured to detect device communication patterns including time intervals between packets, protocol usage, packet sizes, and communication frequency; a multi-persistence homology processor configured to extract topological features from the detected device communication patterns over multiple time scales and generate persistent features representing the device behavior; a cognitive behavior profiling engine configured to generate cognitive fingerprints based on communication patterns of the device and compute cognitive signatures as multi-persistence embeddings, wherein the developed cognitive behavior signatures are stored in a database; an embedding generator configured to compress the topological features and cognitive signatures into low-dimensional behavior signatures using at least one autoencoder and reservoir computing technique; an authentication module configured to compare current behavior signatures to behavior signatures stored in the database and authenticate devices based on the signature match; and a reinforcement learning module configured to adaptively update authentication thresholds based on real-time device behavior and optimize decision parameters by feedback learning.The system of claim 1, wherein the multi-persistence homology processor is further configured to analyze temporal dependencies in device communication patterns, extract short-term behavior characteristics, and extract long-term persistent behavior characteristics that remain stable across network changes.The system of claim 1, wherein the cognitive behavior profiling engine comprises: an energy consumption monitor, a protocol usage analyzer, a communication interval tracker, and a packet size analyzer, each component contributing to the generation of the cognitive fingerprint.The system of claim 1, wherein the embedding generator comprises a reservoir computing module configured to model temporal dependencies in device behavior data and to generate time-series embeddings; and an autoencoder module configured to reduce dimensionality of the topological features and to generate compressed behavior signatures.The system of claim 1, further comprising an anomaly detection module configured to monitor device behavior in real time, detect deviations from expected behavior patterns, and trigger adaptive safety mechanisms upon detecting anomalies.The system of claim 1, wherein the reinforcement learning module implements deep Q network (DQN) that enables optimization of the authentication parameters based on the following factors: authentication success rates, false positive rates, and computational cost.The system of claim 1, wherein the authentication module is further configured to manage a database of historical behavior signatures, calculate similarity values between current and stored signatures, apply adaptive thresholds for authentication decisions, and update stored signatures based on validated device behavior.The system of claim 1 further comprising a resource optimization module configured to monitor computational effort, track power consumption, and adjust authentication parameters to maintain efficiency on devices with constrained resources.The system of claim 1, wherein the device behavior monitoring module implements: packet acquisition mechanisms, protocol identification, temporal pattern analysis, and energy consumption monitoring to generate comprehensive device interaction profiles.The system of claim 1, wherein the system is configured to: distributed operation across multiple IIoT devices, synchronization of behavioral signatures across network nodes, and maintenance of authentication capability during interruptions of network connectivity.

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