Method and system for detecting anomalies at edge locations in a network

Abstract

The present disclosure provides a method and a system to detecting anomalies at edge locations in a network based on an Artificial Intelligence (AI) model. The method includes receiving a structured data corresponding to an edge location. The method includes processing the structured data to extract a current set of features from the structured data The method includes comparing each of the current set of features with a corresponding training feature of a plurality of training features. The method includes identifying a deviation within one or more of the current set of features from the corresponding training feature in response to comparing. The method includes detecting one or more anomalies within the one or more of the current set of features based on the identified deviation and a pre-defined threshold.

Description

METHOD AND SYSTEM FOR DETECTING ANOMALIES AT EDGE LOCATIONS IN A NETWORK RESERVATION OF RIGHTS

[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and / or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (herein after referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner. TECHNICAL FIELD

[0002] The present disclosure relates to a wireless network, and specifically to a method and a system for detecting anomalies at edge locations in a network based on Artificial Intelligence (AI). DEFINITION

[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.

[0004] The term ‘structured data’ as used herein, refers to a clear code data that includes a clean, a valid, and an expected data that is transmitted from various data sources in a network.

[0005] The term ‘anomalies’ as used herein, refers to events, behaviours, or patterns that deviate from normal or expected operation.

[0006] The term ‘data sources’ as used herein includes network elements or user equipments associated with the network.

[0007] The term ‘coherent data’ as used herein, includes a part of data of the structure data that is within the pre-defined threshold. BACKGROUND

[0008] The following description of related art is intended to providebackground information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0009] Current anomaly detection processes used for detecting anomalies in a wireless network (e.g., a fourth generation (4G) network, a fifth generation (5G) network) face notable challenges, primarily centered around a centralization of a raw and unprocessed data for analysis. This is because these traditional processes necessitate gathering all data at a central location before initiating an anomaly detection process. However, this centralized approach, while widely adopted, presents significant challenges notably in terms of latency and high bandwidth utilization. Consequently, it proves to be inefficient, particularly in scenarios where real-time or near-real-time anomaly detection is imperative.

[0010] Furthermore, an absence of anomalies detection capabilities at edge locations represents an inefficiency of these traditional approaches. Without an ability to perform the anomalies detection at the edge locations, data gathered at the edge locations is needlessly relayed to a central system for further analysis and enrichment. This unnecessary forwarding of the data not only consumes valuable resources but also prolongs a time required to detect and respond to anomalies, potentially compromising effectiveness of the anomaly detection process.

[0011] Due to these above discussed limitations of the traditional processes used for detecting the anomalies, there is a clear need for a more efficient and a decentralized approach for overcoming deficiencies of the prior arts. OBJECTS OF THE PRESENT DISCLOSURE

[0012] It is an object of the present disclosure to provide a method and a system for detecting anomalies at an edge location in a network using an Artificial Intelligence (AI) model.

[0013] It is an object of the present disclosure to perform a real-time anomaly detection by supporting a real-time or a near-real-time anomaly detectionat the edge location. This ensures that anomalies are detected and addressed promptly, without a need for a data (i.e., a structured data) to be transmitted to a central location (i.e., a centralized database).

[0014] It is an object of the present disclosure to reduce a latency and a bandwidth consumption by performing anomaly detection at the edge location, hence minimizes the latency and the bandwidth requirements. This optimization allows for a faster response time and an efficient utilization of network resources in the network.

[0015] It is an object of the present disclosure to perform resource and time optimization by implementing anomaly detection at the edge location which prevents unnecessary data forwarding to subsequent layers in the network for further processing and enrichment. This optimization reduces the network resources consumption and a processing time, improving overall efficiency of the network. SUMMARY

[0016] In one embodiment, a method for detecting anomalies at edge locations in a network based on Artificial Intelligence (AI) is disclosed. The method includes receiving a structured data corresponding to an edge location. The method includes processing the structured data to extract a current set of features from the structured data. The method includes comparing each of the current set of features with a corresponding training feature of a plurality of training features. The method includes identifying a deviation within one or more of the current set of features from the corresponding training feature in response to comparing. The method includes detecting one or more anomalies within the one or more of the current set of features based on the identified deviation and a pre-defined threshold.

[0017] In an embodiment, each of the one or more anomalies is detected as an anomaly when the deviation within the one or more of the current set of features is determined to be above the pre-defined threshold.

[0018] In an embodiment, the method further includes discarding the one or more anomalies from the structured data. The method further includes generating a coherent data in response to discarding the one or more anomalies.

[0019] In an embodiment, the method further includes storing the coherent data in a centralized repository.

[0020] In an embodiment, the method further includes an AI model configured for detecting anomalies at the edge location in the network.

[0021] In an embodiment, the AI model is trained based on a training dataset associated with a plurality of edge locations, and the training dataset includes a plurality of anomalies and the plurality of training features corresponding to the plurality of edge locations.

[0022] In an embodiment, the method further includes re-training the AI model based on the structured data and the current set of features associated with the edge location.

[0023] In another embodiment, a system for detecting anomalies at edge locations in a network based on Artificial Intelligence (AI) is disclosed. The system includes a memory and a processing engine communicatively coupled to the memory. The processing engine is configured to receive a structured data corresponding to the edge location. The processing engine is configured to process the structured data to extract a current set of features from the structured data. The processing engine is configured to compare each of the current set of features with a corresponding training feature of a plurality of training features. The processing engine is configured to identify a deviation within one or more of the current set of features from the corresponding training feature in response to comparing. The processing engine is configured to detect one or more anomalies within the one or more of the current set of features based on the identified deviation and a pre- defined threshold.

[0024] In an embodiment, each of the one or more anomalies is detected as an anomaly when the deviation within the one or more of the current set of features is determined to be above the pre-defined threshold.

[0025] In an embodiment, the processing engine is further configured to discard the one or more anomalies from the structured data. The processing engine is further configured to generate a coherent data in response to discarding the one or more anomalies.

[0026] In an embodiment, the processing engine is further configured to store the coherent data in a centralized repository.

[0027] In an embodiment, the processing engine further includes an AI model configured for detecting anomalies at the edge location in the network.

[0028] In an embodiment, the AI model is trained based on a training dataset associated with a plurality of edge locations, and the training dataset includes a plurality of anomalies and the plurality of training features corresponding to the plurality of edge locations.

[0029] In an embodiment, the processing engine is further configured to re- training, by the processing engine, the AI model based on the structured data and the current set of features associated with the edge location.

[0030] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0032] FIG.1 illustrates an exemplary network architecture in which or with which embodiments of the present disclosure may be implemented.

[0033] FIG. 2 illustrates an exemplary block diagram of a system configured for detecting anomalies at edge locations in a network based on Artificial Intelligence (AI), in accordance with an embodiment of the presentdisclosure.

[0034] FIG. 3 illustrates an exemplary flow diagram of a method for detecting anomalies at edge locations in a network based on AI, in accordance with an embodiment of the present disclosure.

[0035] FIG.4 illustrates an exemplary process flow of detecting anomalies at edge locations in a network based on AI, in accordance with an embodiment of the present disclosure.

[0036] FIG.5 illustrates an exemplary control logic for detecting anomalies at edge locations in a network based on AI, in accordance with an embodiment of a present disclosure.

[0037] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented. LIST OF REFERENCE NUMERALS (100) - Network architecture (102-1, 102-2…102-N) - One or more users (104-1, 104-2…104-N) - One or more computing devices or user equipments (106) – Network (108) – System (200) – Exemplary block diagram (202) – Processor(s) (204) - Memory (206) - Interface(s) (208) – Processing engine(s) (210) – Database (212) – Artificial Intelligence (AI) Model (600) - Exemplary computer system (610) - External storage device (620) - Bus (630) - Main memory (640) - Read only memory(650) - Mass storage device (660) - Communication port(s) (670) - Processor DETAILED DESCRIPTION

[0038] In the following detailed description, a reference is made to the accompanying drawings that form a part hereof, and in which the specific embodiments that may be practiced is shown by way of illustration. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments and it is to be understood that other changes may be made without departing from the scope of the embodiments. The following detailed description is therefore not to be taken in a limiting sense.

[0039] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

[0040] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0041] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without thesespecific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0042] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0043] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.

[0044] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means 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 phrases “in one embodiment” or “in an embodiment” in various places throughoutthis specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0045] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any combinations of one or more of the associated listed items.

[0046] It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.

[0047] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical, and computing device. The user device is capable of receiving and / or transmitting one or parameters, performing function / s, communicating with other user devices, and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery, and an input-means such as a hard keypad and / or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee,Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.

[0048] Further, the user device may also comprise a “processor” or “processing engine” including a processing unit. The processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.

[0049] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace older generations of wireless technologies. In a field of wireless data communications, a dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in an order of a second generation (2G), a third generation (3G), a fourth generation (4G), and now a fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0050] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from thedisclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

[0051] Embodiments herein relate to a method and a system for detecting anomalies at edge locations in a network (i.e., a wireless network, such as a Fourth Generation (4G) network, a Fifth Generation (5G) network, etc.) based on Artificial Intelligence (AI). In particular, the system includes an AI model that is configured for detecting the anomalies at the edge locations in the network. The AI model may be deployed at the edge locations for detecting the anomalies. For this, the AI model is configured to receive a structured data corresponding to an edge location in the network. Further, the AI model processes this structured data to detect one or more anomalies at the edge location.

[0052] The various embodiments of the present disclosure will be explained in detail with reference to FIGS.1 to 6.

[0053] FIG.1 illustrates an exemplary network architecture (100) in which or with which a system (108) for detecting anomalies at edge locations in a network based on an Artificial Intelligence (AI) is implemented, in accordance with embodiments of the present disclosure.

[0054] Referring to FIG.1, the network architecture (100) may include one or more computing devices or user equipments (104-1, 104-2…104-N) associated with one or more users (102-1, 102-2…102-N) in an environment. A person of ordinary skill in the art will understand that the one or more users (102-1, 102- 2…102-N) may be individually referred to as a user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more user equipments (104-1, 104-2…104-N) may be individually referred to as a user equipment (104) and collectively referred to as the user equipment (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three user equipments (104) are depicted in FIG.1, however any number of the user equipments (104) may be included without departing from the scope of the ongoing description.

[0055] In an embodiment, the user equipment (104) may include smart devices operating in a smart environment, for example, an Internet of Things (IoT) system. In such an embodiment, the user equipment (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, a networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, a smart security system, a smart home system, other devices for monitoring or interacting with or for the user (102) and / or an entity (110), or any combination thereof. A person of ordinary skill in the art will appreciate that the user equipment (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and / or with a central server or a cloud-computing system or any other device that is network-connected.

[0056] In an embodiment, the user equipment (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device(e.g., a head- mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of a computer device with wireless communication capabilities, and the like. In an embodiment, the user equipment (104) may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any other computing device, the user equipment (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102) or the entity (110) such as a touch pad, a touch enabled screen, an electronic pen,and the like. A person of ordinary skill in the art will appreciate that the user equipment (104) may not be restricted to the mentioned devices and various other devices may be used.

[0057] In an embodiment, the user equipment (104) is configured to communicate with the system (108) through a network (106). The system (108) may deploy an AI model at the edge locations for detecting the anomalies at the edge locations in the network (e.g., the network (106)). The network, for example may be a wireless network, such as a Fouth Generation (4G) network, a Fifth Generation (5G) network, a Sixth Generation (6G) network, and the like. The network (106) may enable the user equipment (104) to communicate with other devices in the network architecture (100) and / or with the system (108). The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), an Internet, a Public Switched Telephone Network (PSTN), or the like.

[0058] In another exemplary embodiment, a centralized server (112) may include or comprise, by way of example but not limitation, one or more of: a stand- alone server, a server blade, a server rack, a bank of servers, a server farm, a hardware supporting a part of a cloud service or the system (108), a home server, a hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof.

[0059] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0060] FIG. 2 illustrates an exemplary block diagram (200) of the system (108) configured for detecting the anomalies at the edge location in the network based on the AI, in accordance with an embodiment of the present disclosure. The network may correspond to the wireless network, such as, the 4G network, the 5G network, the 6G network, and the like. FIG.2 is explained in conjunction with FIG. 1.

[0061] In an embodiment, in order to detect the anomalies at the edge location, the system (108) may be deployed at the edge location. In an aspect, the system (108) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (108). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed detect the anomalies at the edge locations in the network. The memory (204) may comprise any non-transitory storage device including, for example, a volatile memory such as a Random-Access Memory (RAM), or a non-volatile memory such as an Erasable Programmable Read-Only Memory (EPROM), a flash memory, and the like.

[0062] In an embodiment, the system (108) may include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I / O devices, storage devices, and the like. The interface(s) (206) may facilitate communication of the system (108). The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, a processing unit / engine (208) and a database (210).

[0063] The processing engine (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) toimplement one or more functionalities of the processing engine (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine (208) may be processor-executable instructions stored on a non- transitory machine-readable storage medium and the hardware for the processing engine (208) may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine- readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine (208). In such examples, the system (108) may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine- readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the processing engine (208) may be implemented by an electronic circuitry.

[0064] In an embodiment, the processing engine (208) may include an anomaly detection module (214) and an AI model (212) for detecting the anomalies at the edge location in the network. As already known to the person skilled in art, the edge location refers to a point within a network infrastructure that is physically close to end-users having the user equipments (104) accessing the network.

[0065] The anomaly detection module (214) is configured to detect the anomalies (i.e., the one or more anomalies) within the one or more relevant features using the pre-defined threshold (e.g., 50%). The anomaly detection module (214) includes the AI model (212) to perform anomaly detection. The AI model (212) is configured to receive the structured data from an edge location. In an embodiment, the structured data corresponds to a clear code data. The clear code data refers to a clean, valid, and expected data that is transmitted from various data sources in the network. In one embodiment, the data sources correspond to network elements such as, Internet of Things (IoT) gateways, routers, switches, firewalls, Access Points (APs), modems, Network Interface Cards (NICs), and the like. In another embodiments, the data sources correspond to user equipments (i.e., the user equipments (104)). Further, examples of the clear code data include, but are notlimited to, readings of temperature sensors, readings of humidity sensors, readings of pressure sensors, a status information of the network elements of the user equipments (104), a network traffic, an application data associated with the user equipments (104), and the like. Upon receiving the structured data, the AI model (212) is configured to process the structured data to detect one or more anomalies at the edge location in the network. A method of processing the structured data is further explained in detail in conjunction with FIG.3.

[0066] Once the one or more anomalies are detected, the AI model (212) is configured to discard the one or more anomalies from the structured data to generate a coherent data. The coherent data may correspond to clean data that excludes anomalous or irrelevant observations and represents expected patterns or behaviours of the data sources in the network. The AI model (212) further transmits and store the coherent data in a centralized repository for further analysis. The centralized repository may correspond to a database present within the centralized server (112).

[0067] It should be noted that, the AI model (212) is trained based on a training dataset associated with a plurality of edge locations to detect the anomalies at the edge location in the network. The training dataset includes a plurality of anomalies and the plurality of training features corresponding to the plurality of edge locations. Examples of the plurality of anomalies includes, but are not limited to, a spike in a network traffic, unusual data patterns, unusual access patterns associated with the user equipments (104), an abnormal behaviour of network elements, and the like. Examples of the plurality of training features includes, but are not limited to, numerical values indicating the readings of the temperature sensors, the pressure sensors, the humidity sensors, voltage sensors, or any other similar measurable quantity, errors codes or status indicators corresponding to events such as network connectivity issues, data transmission errors, resource exhaustions, configuration errors, security breaches, hardware failures, etc., a frequency of the events, a rate of change of the numerical values over a period of time (e.g., in 15 days), and the like. It should be noted that, the AI model (212) may continuously evolve based on the structured data and a current set of featuresassociated with the edge location. In other words, the AI model (212) may perform its retraining based on the structured data received from the edge location and the current set of features identified within the structured data.

[0068] In an embodiment, the database (210) may include data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor(s) (202), the processing engine (208), or the system (108).

[0069] Although FIG. 2 shows the exemplary block diagram (200) of the system (108), in other embodiments, the system (108) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG.2. Additionally, or alternatively, one or more components of the system (108) may perform functions described as being performed by one or more other components of the system (108).

[0070] FIG. 3 illustrate an exemplary flow diagram of a method (300) for detecting the anomalies at the edge locations in the network based on the AI, in accordance with an embodiment of the present disclosure. The network may correspond to the wireless network, such as, the 4G network, the 5G network, the 6G network, and the like. FIG.3 is explained in conjunction with FIGS.1 – 2. Each step of the method (300) may be executed by the processing engine (208) using the AI model (212). The AI model (212) is trained for detecting the anomalies at the edge location based on the training dataset associated with the plurality of edge locations. The training dataset includes the plurality of anomalies and the plurality of training features corresponding to the plurality of edge locations.

[0071] In order to detect the anomalies at the edge location in the network, initially at step 302, the structured data corresponding to the edge location is received. In particular, the AI model (212) is configured for receiving the structured data from the edge location. In an embodiment, the structured data corresponds to a clear code data. The clear code data refers to a clean, a valid, and an expected data that is transmitted from various data sources in the network. In one embodiment, the data sources correspond to network elements such as, Internet of Things (IoT) gateways, routers, switches, firewalls, Access Points (APs), modems, NetworkInterface Cards (NICs), and the like. In another embodiments, the data sources correspond to user equipments (i.e., the user equipments (104)). Examples of the clear code data include, but are not limited to, readings of temperature sensors, readings of humidity sensors, readings of pressure sensors, a status information of the network elements of the user equipments (104), a network traffic, an application data associated with the user equipments (104), and the like. Examples of the anomalies includes, but are not limited to, a spike in a network traffic, an unusual data pattern, an unusual access pattern associated with the user equipment (104), an abnormal behaviour of a network element (e.g., a router), and the like.

[0072] Upon receiving the structured data, at step (304) the structured data is processed to extract a current set of features from the structured data. In particular, the AI model (212) is configured for process the structured data to extract the current set of features. The current set of features, for example, includes a sudden spike in the network traffic (for example: a value of the network traffic with a bandwidth utilization of 100 Mega bits per second (Mbps)) due to an event, e.g., a black Friday sale, and an unusual access pattern, e.g., multiple login attempts (4 login attempts) involving a specific user account associated with the user equipment (104) corresponding to the edge location in the network.

[0073] Once the current set of features are extracted, at step (306), each of the current set of features is compared with a corresponding training feature of the plurality of training features. In particular, the AI model (212) is configured to compare the current set of features with the corresponding training features. By way of an example, the bandwidth utilization of 100 Mbps spike in the network traffic may be compared with a corresponding training feature that includes a bandwidth utilization of 20 Mbps. Similarly, the unusual access pattern that includes 4 login attempts is compared with a corresponding training feature that includes 3 login attempts.

[0074] Further, based on the comparison, at step (308), a deviation within one or more of the current set of features from the corresponding training feature may be identified. In particular, the AI model (212) is configured to identify the deviation. In continuation to the above example, the deviation, for example, adeviation of the bandwidth utilization of 100 Mbps from the bandwidth utilization of 10 Mbps and the 4 login attempts with the 3 login attempts is identified based on the comparison.

[0075] Upon identifying the deviation, at step (310), one or more anomalies are detected within the one or more of the current set of features based on the identified deviation and a pre-defined threshold. For example, the pre-defined threshold is set for 50%. In this case, any feature in the current set of features that deviates beyond 50% may be detected as an anomaly. In particular, the AI model (212) is configured to detect the one or more anomalies. In an embodiment, each of the one or more anomalies is detected as an anomaly when the deviation within the one or more of the current set of features is determined to be above the pre-defined threshold. In continuation to above example, in first case, suppose the bandwidth utilization of 100 Mbps deviates for 90% from the bandwidth utilization of 10 Mbps which is more than the pre-defined threshold, i.e., 50%, in this case, the spike in the network traffic is considered as the anomaly. In second case, suppose the 4 login attempts deviates 10% with the 3 login attempts which is less than the pre-defined threshold, i.e., 50%, in this case, the unusual access patterns may not be detected as the anomaly.

[0076] Once the one or more anomalies are detected, the AI model (212) is configured to discard the one or more anomalies from the structured data. Further, the AI model (212) is configured to generate the coherent data in response to discarding the one or more anomalies. In an embodiment, the coherent data corresponds to clean data that excludes anomalous or irrelevant observations and represents expected patterns or behaviours of the data sources in the network. In particular, the coherent data includes data of the structured data that is within the pre-defined threshold. In continuation to the above example, the AI model (212) may discard data associated with the sudden spike in the network traffic from the structured data to generate the coherent data. Furthermore, once the coherent data is generated, the AI model (212) is configured to store the coherent data in the centralized repository for further processing. The AI model (212) performs its training based on the structured data and the current set of features. In this way, theAI model (212) continuously evolve and learn to consistently enhance its anomaly detection performance over time.

[0077] FIG. 4 illustrates an exemplary process flow (400) of detecting the anomalies at the edge locations in the network based on the AI, in accordance with an embodiment of the present disclosure. FIG. 4 is explained in conjunction with FIGS. 1 - 3. Each step of the method (300) may be executed by the processing engine (208) using the AI model (212).

[0078] In order to detect the anomalies, the process flow (400) starts at step (402). Further, at step (404), a raw clear code is received at an end point. The raw clear code data corresponds to the structured data. The end point may correspond to a network boundary between the data sources and the AI model (212). In one embodiment, the data sources correspond to network elements such as, the IoT gateways, the routers, the switches, the firewalls, the APs, the modems, the NICs, and the like. In another embodiments, the data sources correspond to the user equipments (104).

[0079] Upon receiving the raw clear code data, at step (406), the AI model (212) may process the raw clear code data to extract relevant features (i.e., the current set of features) using an inbuilt relevant feature extraction module. Once the relevant features, at step (408), the relevant features may be transferred to an anomaly detection module (214) that includes the AI model (212) for detecting the anomalies. Further, at step (412), the anomaly detection module (214) may be configured to compare the relevant features with the plurality of training feature to identify the deviation in one or more relevant features from the corresponding training feature.

[0080] Further, based on the identified deviation, the anomaly detection module (214) detects the anomalies (i.e., the one or more anomalies) within the one or more relevant features using the pre-defined threshold (e.g., 50%). Once the anomalies are detected, the anomaly detection module (214) discards the anomalies from the raw clear data to generate the coherent data. Further, at step (414), the coherent data is stored in the centralized repository that is transferred to subsequent layers (e.g., Integration and Orchestration Layer, Monitoring and Logging Layer,etc.) in the network. At step (416), the subsequent layers in the network uses the coherent data to perform further performance analysis corresponding to the network. In an embodiment, upon receiving the relevant features, the relevant features are transferred to the anomaly detection module (214), as depicted via step (410), for enhancing the anomaly detection process. Upon receiving the relevant features for enhancing the anomaly detection process, the anomaly detection module (214) performs retraining of the AI model (212) based on the relevant features and the raw clear code data.

[0081] FIG.5 illustrates an exemplary control logic (500) for detecting the anomalies at the edge locations in the network based on the AI, in accordance with an embodiment of a present disclosure. FIG. 5 is explained in conjunction with FIGS.1 - 4.

[0082] As depicted via the control logic (500), each Machine Learning probe, i.e., a first ML probe 504-2, a second ML probe 504-4, and a third ML probe (504-6) is configured for receiving a first clear code data (502-2), a second clear code data (502-4), and a third clear code data (502-6) respectively. As already known to the person skilled in art, each ML probe typically refers to a component or a module within a system (i.e., the system) that is responsible for monitoring and analyzing a performance, a behavior, and an output of machine learning model (i.e., the AI model (212)). Further, the first clear code data (502-2), the second clear code data (502-4), and the third clear code data (502-6) corresponds to the structured data. Further, each ML probe may be deployed at a different edge location.

[0083] Further, as depicted via the control logic (500), the first ML probe (504-2) includes a first ML model (506-2) that is in communication with a first computation cluster (508-2). Similarly, the second ML probe (504-4) includes a second ML model (506-4) that is in communication with a second computation cluster (508-4), and the third ML probe (504-6) includes a third ML model (506-6) that is in communication with a third computation cluster (508-6). Each of the first ML model (506-2), the second ML model (506-4), the third ML model (506-6) may correspond to the AI model (212). In an embodiment, for example, the first ML probe (504-2) including the first ML model (506-2) may be deployed at a first edgelocation, the second ML probe (504-4) including the second ML model (506-4) may be deployed at a second edge location, and the third ML probe (504-6) including the third ML model (506-6) may be deployed at a third edge location. Further, each computation cluster, i.e., the first computation cluster (508-2), the second computation cluster (508-4), and the third computation cluster (508-6) may include a set of three workers 1 and a computation vector 1, a computation vector 2, and a computation vector 3, respectively, as depicted via the control logic (500).

[0084] Upon receiving the first clear code data (502-2), the first ML model (506-2) within the first ML probe (504-2) may be configured for processing the first clear code data (502-2) in conjunction with the first computation cluster (508-2) to detect the anomalies in the first clear code data (502-2). For this, the first ML model (506-2) in conjunction with the first computation cluster (508-2) extract the current set of features and compares the current set of features with the corresponding training feature. Further, based on the comparison, the deviation is identified within the one or more current set of features and the anomalies are detected within the current set of features using the pre-defined threshold (e.g., 50%). In an embodiment, the anomalies detected is above the predefined threshold. Once the anomalies are detected, the anomalies are discarded from the first clear code data (502-2) and the coherent data including data within the pre-defined threshold is generated. Further, as depicted via an arrow (510-2), the generated coherent data is transferred and stored in a distributed data lake (512) (same as the centralized repository).

[0085] In a similar manner, upon receiving the second clear code data (502- 4), the second ML model (506-4) within the second ML probe (504-4) may be configured for processing the second clear code data (502-4) in conjunction with the second computation cluster (508-4) to detect the anomalies in the second clear code data (502-4). Based on the anomalies detected within the second clear code data (502-4), the coherent data may be generated that is further transferred and stored in the distributed data lake (512). Similarly, upon receiving the third clear code data (502-6), the third ML model (506-6) within the third ML probe (504-6) may be configured for processing the third clear code data (502-6) in conjunctionwith the third computation cluster (508-6) to detect the anomalies in the third clear code data (502-6). Based on the anomalies detected in the third clear code data (502- 6), the coherent data may be generated that is further transferred and stored in the distributed data lake (512).

[0086] Furthermore, a distributed computing orchestrator (514) is configured used the coherent data received from the first ML probe (504-2), the second ML probe (504-4), and the third ML probe (504-6) to perform further performance analysis corresponding to the network. In some embodiments, the distributed computing orchestrator (514) may include the subsequent layers corresponding to the network.

[0087] FIG. 6 illustrates an exemplary computer system (600) in which or with which embodiments of the present disclosure may be implemented. As shown in FIG.6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read only memory (640), a mass storage device (650), a communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system (600) may include more than one processor (670) and communication ports (660). The processor (670) may include various modules associated with embodiments of the present disclosure.

[0088] In an embodiment, the communication port(s) (660) may be any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (660) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (600) connects.

[0089] In an embodiment, the memory (630) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory (640) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (670).

[0090] In an embodiment, the mass storage (650) may be any current orfuture mass storage solution, which may be used to store information and / or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays).

[0091] In an embodiment, the bus (620) communicatively couples the processor (670) with the other memory, storage and communication blocks. The bus (620) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the computer system (600).

[0092] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operator and administrative interfaces may be provided through network connections connected through the communication port(s) (660). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (600) limit the scope of the present disclosure.

[0093] While the foregoing describes various embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof. The scope of the present disclosure is determined by the claims that follow. The present disclosure is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the present disclosure when combined with information and knowledge available to the person having ordinary skill in the art.

[0094] The present disclosure provides technical advancement related todetecting and managing anomalies at edge locations. The disclosure overcomes the limitations of the conventional approaches which includes gathering all data before initiating anomaly detection process, and lack of real-time or near real-time anomaly detection in edge locations. The conventional approaches are bandwidth- heavy, inefficient, and time-consuming. The current disclosure describes performing real-time or near-real time anomaly detection at edge locations. The current disclosure approach leads to saving bandwidth, efficient anomaly detection and saves time. ADVANTAGES OF THE PRESENT DISCLOSURE

[0095] The present disclosure provides a system and a method to detect anomalies at an edge location in a network using an Artificial Intelligence (AI) model.

[0096] The present disclosure performs a real-time anomaly detection by supporting a real-time or a near-real-time anomaly detection at the edge location. This ensures that anomalies are detected and addressed promptly, without a need for a data (i.e., a structured data) to be transmitted to a central location (i.e., a centralized database).

[0097] The present disclosure reduces a latency and a bandwidth consumption by performing anomaly detection at the edge location, hence minimizes the latency and the bandwidth requirements. This optimization allows for a faster response time and an efficient utilization of network resources in the network.

[0098] The present disclosure performs resource and time optimization by implementing anomaly detection at the edge location which prevents unnecessary data forwarding to subsequent layers in the network for further processing and enrichment. This optimization reduces the network resources consumption and a processing time, improving overall efficiency of the network.

Claims

CLAIMS 1. A method (300) for detecting anomalies at edge locations in a network based on Artificial Intelligence (AI), the method comprising: receiving (302), by a processing engine (208), a structured data corresponding to an edge location; processing (304), by the processing engine (208), the structured data to extract a current set of features from the structured data; comparing (306), by the processing engine (208), each of the current set of features with a corresponding training feature of a plurality of training features; identifying (308), by the processing engine (208), a deviation within one or more of the current set of features from the corresponding training feature in response to comparing; and detecting (310), by the processing engine (208), one or more anomalies within the one or more of the current set of features based on the identified deviation and a pre-defined threshold.

2. The method (300) as claimed in claim 1, wherein each of the one or more anomalies is detected as an anomaly when the deviation within the one or more of the current set of features is determined to be above the pre-defined threshold.

3. The method (300) as claimed in claim 1, further comprising: discarding, by the processing engine (208), the one or more anomalies from the structured data; and generating, by the processing engine (208), a coherent data in response to discarding the one or more anomalies.

4. The method (300) as claimed in claim 1, further comprising storing, by the processing engine (208), the coherent data in a centralized repository.

5. The method (300) as claimed in claim 1, wherein the processing engine (208) comprises an AI model (212) configured for detecting anomalies at the edge location in the network.

6. The method (300) as claimed in claim 5, wherein the AI model (212) is trained based on a training dataset associated with a plurality of edge locations, and wherein the training dataset comprises a plurality of anomalies and the plurality of training features corresponding to the plurality of edge locations.

7. The method (300) as claimed in claim 5, further comprising: re-training, by the processing engine (208), the AI model (212) based on the structured data and the current set of features associated with the edge location.

8. A system (108) for detecting anomalies at edge locations in a network based on Artificial Intelligence (AI), the system (108) comprising: a memory (204); and a processing engine (208) communicatively coupled with the memory (204), configured to: receive (302) a structured data corresponding to the edge location; process (304) the structured data to extract a current set of features from the structured data; compare (306) each of the current set of features with a corresponding training feature of a plurality of training features; identify (308) a deviation within one or more of the current set of features from the corresponding training feature in response to comparing; and detect (310) one or more anomalies within the one or more of the current set of features based on the identified deviation and a pre-defined threshold.

9. The system (108) as claim in claim 8, wherein each of the one or more anomalies is detected as an anomaly when the deviation within the one or more of the current set of features is determined to be above the pre-defined threshold.

10. The system (108) as claim in claim 8, wherein the processing engine (208) is further configured to: discard the one or more anomalies from the structured data; and generate a coherent data in response to discarding the one or more anomalies.

11. The system (108) as claim in claim 8, wherein the processing engine (208) is further configured to store the coherent data in a centralized repository.

12. The system (108) as claim in claim 8, the processing engine (208) comprises an AI model (212) configured for detecting anomalies at the edge location in the network.

13. The system (108) as claim in claim 12, wherein the AI model (212) is trained based on a training dataset associated with a plurality of edge locations, and wherein the training dataset comprises a plurality of anomalies and the plurality of training features corresponding to the plurality of edge locations.

14. The system (108) as claim in claim 12, wherein the processing engine (208) is further configured to re-train the AI model (212) based on the structured data and the current set of features associated with the edge location.

15. A computer program product comprising a non-transitory computer- readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to: receive (302) a structured data corresponding to the edge location; process (304) the structured data to extract a current set of features from the structured data;compare (306) each of the current set of features with a corresponding training feature of a plurality of training features; identify (308) a deviation within one or more of the current set of features from the corresponding training feature in response to comparing; and detect (310) one or more anomalies within the one or more of the current set of features based on the identified deviation and a pre-defined threshold.

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