System for identifying service interruptions in cable broadband networks using telemetry-based anomaly detection

Abstract

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

Description

Field of invention

[0001] The present invention relates to communication networks and, in particular, systems and devices for real-time monitoring and detection of service interruptions in cable broadband networks. The invention integrates telemetry data acquisition, anomaly detection, and predictive diagnostics to locate and characterize disruptions in service delivery with high accuracy. Background of the invention

[0002] Cable broadband networks form the backbone of internet and multimedia services in homes and businesses. These networks are typically based on a hybrid fiber-coaxial (HFC) infrastructure, where digital and analog signals are transmitted over coaxial cables connected to distributed amplifiers, optical nodes, and customer premises equipment (CPE). Due to the heterogeneity of these systems, outages and service interruptions can have various causes, including signal degradation, faulty amplifiers, overloaded aggregation nodes, or physical cable damage.

[0003] Traditional monitoring relies on customer complaints, manual queries, or static, threshold-based alerts from network elements. These approaches are reactive, latency-inducing, and cannot efficiently distinguish temporary anomalies from genuine service disruptions. Furthermore, the extensive telemetry data from modern cable modems, CMTS (Cable Modem Termination Systems), and edge devices is underutilized due to a lack of intelligent correlation mechanisms.

[0004] There is a need for a system that can dynamically collect telemetry data, detect anomalies using advanced statistical and machine learning models, and generate alerts for the accurate detection of service interruptions. Such a system would reduce downtime, minimize the need for service calls, and enable predictive maintenance in broadband cable networks.

[0005] Cable broadband networks have evolved into critical infrastructure, providing residential and commercial users with access to high-speed internet, voice-over-IP services, video-on-demand platforms, and other multimedia applications. The architecture typically integrates hybrid fiber-coaxial (HFC) cabling, customer equipment such as cable modems, distribution amplifiers, and central cable modem termination systems (CMTS). While widespread broadband adoption has transformed connectivity, the operational complexity of such networks presents ongoing challenges in ensuring continuous service availability. Service interruptions, whether caused by hardware failures, environmental damage to cables, congestion, or misconfigurations, negatively impact the customer experience and result in significant revenue losses for operators.Ensuring uninterrupted operation therefore requires not only continuous monitoring but also the intelligent detection of anomalies that can precede or directly cause service interruptions. Over the years, various technical approaches have been implemented to address these challenges. However, each has limitations that reduce its effectiveness in detecting and resolving service outages in real time.

[0006] Early approaches to detecting service outages relied heavily on customer complaint mechanisms. When customers experienced a loss of connection or reduced service quality, they typically contacted customer support, who logged the issue and escalated it to the technical teams for resolution. While this reactive mechanism allowed for immediate problem detection, it resulted in significant delays between the occurrence of a service outage and its recognition by operators. In many cases, it took hours for a problem to be identified and for troubleshooting to begin. Furthermore, such systems provided no information about the extent or cause of the problem. A single complaint might refer to an isolated modem malfunction in a single household or be symptomatic of a larger outage affecting entire neighborhoods.The lack of detail and customer dissatisfaction due to delayed responses made this method inadequate for modern broadband networks, where customers demand near real-time reliability.

[0007] Subsequent approaches focused on threshold-based alarms derived from network elements such as CMTS units, routers, and amplifiers. Network operators configured thresholds for parameters like signal-to-noise ratio (SNR), power levels, and latency. If these values ​​exceeded the preconfigured thresholds, alarms were triggered in the network management systems. While this enabled more automated monitoring, it had several drawbacks. Fixed thresholds were poorly suited to the dynamic nature of broadband networks with constantly fluctuating traffic loads and environmental conditions. For example, a temporary spike in noise could trigger an unnecessary alarm, while a gradual deterioration in service quality might not exceed the thresholds until the service was already severely impacted.Furthermore, static thresholds could not capture multidimensional patterns where a combination of sub-threshold deviations in various parameters might signal an actual imminent service disruption. The rigidity of threshold-based monitoring frequently led to false positives and false negatives, and thus to inefficient operational responses.

[0008] To address these shortcomings, operators implemented active monitoring techniques such as probing and synthetic transactions. This approach involves injecting test packets, or synthetic data streams, into the network to simulate user activity. By measuring response times, packet loss, and throughput, operators attempt to identify network disruptions. While active monitoring offered a proactive way to assess service integrity, it consumed additional bandwidth and computing resources. In high-density networks, the overhead of synthetic probing can negatively impact overall efficiency. Furthermore, synthetic probing may not accurately reflect the diversity of actual user traffic patterns.A synthetic test might be successful, while actual customers still experience degraded streaming quality due to subtle impairments not visible in simple ping or throughput tests. This discrepancy between synthetic tests and real-world user experiences limited the reliability of active monitoring in identifying service disruptions.

[0009] Another approach that gained importance involved event correlation units in network management systems. These units collected logs and alarms from multiple network elements and attempted to correlate them with higher-level events. For example, if several modems simultaneously reported a low upstream SNR, the system could infer a common fault in the upstream amplifier. While event correlation improved root cause identification, it remained heavily reliant on predefined correlation rules manually configured by uniters. These rules were difficult to enforce in large, dynamic networks where new device types, firmware versions, and topological changes frequently occurred. Furthermore, static rules lacked the adaptability to novel, previously unobserved fault modes.This meant that event correlation systems often had to be painstakingly adjusted manually and still delivered ambiguous or incomplete diagnoses.

[0010] More recently, with the introduction of large-scale telemetry collection, operators have sought to leverage data analytics platforms to identify service disruptions. Telemetry streams from modems, CMTS units, and network devices contain extensive parameters such as MER, latency statistics, error correction counters, and throughput metrics. Big data frameworks have been deployed to aggregate and visualize these streams. However, most existing analytics solutions are descriptive rather than diagnostic. They allow operators to visualize patterns and detect broad anomalies, but they do not provide automated, high-precision identification of service disruptions. Relying on dashboards and manual reviews, disruptions are often not noticed until human operators actively examine the data.Furthermore, while analytics platforms can process large amounts of data, they are not optimized for real-time anomaly detection, which leads to latencies in detection and response.

[0011] Machine learning has also been used in some experimental and commercial solutions. For example, clustering techniques have been used to detect unusual patterns in modem telemetry, while classifiers have been trained to predict outages based on historical data. While these approaches are promising, they suffer from several technical hurdles. Supervised learning requires large, labeled datasets of past outages, but service interruptions are relatively rare compared to normal operation, leading to class imbalance. Models trained on historical data may not translate to new or unknown types of failures, reducing detection accuracy. Unsupervised models, while able to detect anomalies, are often difficult to interpret and generate large volumes of alerts that overwhelm operators.The lack of contextual correlation with network topology and spatial distribution further limits the usefulness of purely machine learning approaches.

[0012] Edge monitoring devices have also been proposed, where probes are placed closer to the end user or integrated into modems to detect local service issues. While this edge-based monitoring offers granularity, it presents challenges in terms of scalability and coordination. Deploying smart probes in millions of subscriber modems requires significant costs and logistical effort. Furthermore, without central correlation, edge probes may indicate anomalies without recognizing that these are symptoms of a larger upstream fault. This local perspective can lead to fragmented information, making root cause analysis difficult.

[0013] Another problem with existing solutions is their inability to distinguish between temporary anomalies and genuine service interruptions. Cable broadband networks are inherently noisy environments, where minor fluctuations in signal strength or latency occur regularly. Existing systems either ignore these small fluctuations, risking delayed detection of genuine service interruptions, or they overreact, generating an excessive number of false alarms. This trade-off reduces confidence in the generated alerts and increases the effort required for manual review.

[0014] Another drawback of current approaches is the limited use of contextual information such as geographic clustering, temporal patterns, and cross-layer correlations. For example, if signal-to-noise ratio (SNR) anomalies are detected simultaneously in several neighboring districts, this could indicate a main line failure, while isolated anomalies might be due to the wiring of individual households. Without contextual correlation, systems can misinterpret local problems as widespread outages or overlook systemic issues. This lack of contextual integration hinders precise prioritization and timely response.

[0015] In addition to technical disadvantages, operational inefficiencies are also noticeable. Many systems require constant manual configuration of thresholds, correlation rules, and reporting structures. This reliance on human intervention leads to delays and is prone to errors, especially in large networks where thousands of devices need to be continuously monitored. The heterogeneity of network devices—each with its own telemetry format and reporting frequency—further increases the complexity. Without harmonization, data from different devices cannot be effectively combined to provide a coherent picture of network health.

[0016] Security concerns further complicate existing solutions. Telemetry data streams are often transmitted over insecure channels and are therefore vulnerable to interception or manipulation. If anomalies are mistakenly injected into telemetry data, operators may waste resources searching for phantom failures. Existing systems often lack cryptographic anchors or verification mechanisms to ensure the authenticity of telemetry data. Without trusted telemetry, the reliability of anomaly detection is severely compromised.

[0017] Finally, scalability remains a persistent challenge. As networks expand to millions of subscribers, the volume of generated telemetry data grows exponentially. Traditional systems designed for smaller networks struggle to capture, process, and analyze telemetry data at this scale without delay. High computational demands make real-time anomaly detection impractical on many existing platforms, forcing operators to choose between precision and responsiveness. This compromises their ability to maintain uninterrupted service during rapid or widespread disruptions.

[0018] In summary, existing solutions—from customer complaint mechanisms, threshold-based alerts, active probing, event correlation units, big data analytics, and machine learning models to edge monitoring devices—all offer partial approaches to detecting service disruptions in cable broadband networks. However, they share common drawbacks, such as reactivity, high false alarm rates, limited adaptability, lack of context correlation, reliance on manual configuration, and insufficient scalability. These shortcomings highlight the urgent need for a comprehensive system that integrates telemetry harmonization, adaptive anomaly detection, context classification, and automated, real-time disruption detection to overcome the limitations of the current state of the art and provide users with a more reliable broadband experience. Objectives of the invention

[0019] The main objective of this invention is to provide a system capable of automatically identifying service interruptions in cable broadband networks through real-time telemetry-based anomaly detection.

[0020] Another goal is to develop an integrated telemetry harmonization layer that collects and normalizes data streams from heterogeneous devices such as modems, amplifiers, and CMTS units.

[0021] Another goal is to use adaptive anomaly detection models that capture temporal, spatial, and frequency-related fluctuations in signal parameters, thus distinguishing between temporary disturbances and genuine service interruptions.

[0022] Another objective of the invention is to provide a device architecture that embodies the monitoring and processing system in a distributed or edge-deployed form factor and enables both centralized and localized anomaly detection. Summary of the invention

[0023] The invention describes a system for detecting service interruptions in cable broadband networks using telemetry-based anomaly detection. The system comprises a telemetry acquisition unit, a preprocessing and harmonization module, an anomaly detection unit with hybrid statistical and machine learning models, and an interruption classification module that assigns anomalies to service-impairing events. The system is also configured to generate prioritized alerts for network operators.

[0024] The invention also discloses a device architecture in the form of a telemetry monitoring device or an integrated network probe that is embedded in the cable broadband infrastructure and can execute the pipeline for anomaly detection.

[0025] The objectives of the present invention arise from the need to overcome the inefficiency, inaccuracy, and limitations of existing systems for detecting service interruptions in cable broadband networks. A primary objective of the invention is to provide a system that automatically detects service-impairing anomalies by utilizing real-time telemetry data from various sources within the broadband infrastructure, including modems, amplifiers, and CMTS units. This ensures that interruptions are detected promptly without waiting for customer complaints or manual inspections. Another objective of the invention is to create an adaptive anomaly detection framework that not only detects abnormal deviations from expected performance but also intelligently distinguishes between transient disturbances and true interruptions, thereby reducing false alarms and unnecessary operator intervention.

[0026] A further objective of the invention is to create a mechanism for harmonizing telemetry that can normalize data streams from heterogeneous devices with different formats, sampling intervals, and reporting protocols, so that all telemetry data can be aggregated and analyzed uniformly. Furthermore, the invention enables context-aware classification of anomalies by correlating them across time, location, and network topology. This allows operators to determine whether a disturbance is localized or widespread and to prioritize corrective actions accordingly. Another objective is to embed these functions in a dedicated device or monitoring application with integrated processing, data acquisition, and alerting capabilities, which can be deployed centrally or at the network edge to achieve low-latency responses.

[0027] The invention also aims to increase scalability to capture and process the large volumes of telemetry data from millions of broadband subscribers without compromising detection accuracy or speed. Beyond detection, the invention seeks to enhance operational efficiency by automating root cause analysis and presenting operators with actionable insights instead of overwhelming amounts of raw data or generic alerts. A further objective is the integration of robust security measures to ensure that the telemetry data used for anomaly detection is authentic, tamper-proof, and resistant to manipulation.The overarching goal of the invention is to provide a reliable, proactive and intelligent system that transforms broadband network monitoring from a reactive and fragmented process into an automated, integrated and predictive mechanism, thereby ensuring continuous and high-quality connectivity for end users while reducing operating costs for service providers. BRIEF DESCRIPTION OF THE FIGURE

[0028] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of a system for identifying service interruptions in cable broadband networks using telemetry-based anomaly detection.

[0029] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0030] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0031] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0032] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0033] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, so that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0035] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0036] In Fig.Figure 1 shows a block diagram of a system for detecting service interruptions in cable broadband networks using telemetry-based anomaly detection. The system 100 comprises: a telemetry acquisition unit (102) configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements such as cable modems, amplifiers, optical nodes, and cable modem termination systems (CMTS), where the telemetry data includes signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels, and latency statistics; a preprocessing and harmonization module (104) operationally coupled to the telemetry acquisition unit and configured to normalize heterogeneous telemetry streams by matching sampling rates, synchronizing timestamps, interpolating missing data, and filtering outgoing data;an anomaly detection unit (106) that is communicatively connected to the preprocessing and harmonization module and comprises a hybrid detection framework with statistical predictive models and machine learning models, wherein the statistical predictive models include ARIMA or Holt-Winters models for predicting expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble scoring subsystem (108) within the anomaly detection unit that combines the outputs of the statistical predictive models and the machine learning models to generate anomaly probability values ​​with adaptive confidence intervals;an interruption classification module (110) that receives anomaly probability values ​​and correlates anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module distinguishes between transient anomalies and service-disrupting interruptions based on a multidimensional correlation; and an alert interface (112) that sends interruption alerts with severity, root cause metadata, and geolocation to a network management system for operator intervention.

[0037] In one embodiment, the preprocessing and harmonization module (104) applies weighted interpolation techniques to compensate for missing values ​​in telemetry data streams, with the weights being dynamically adjusted based on temporal proximity and device class to preserve the statistical accuracy of the harmonized data set.

[0038] In one embodiment, the anomaly detection unit (106) also includes an adaptive retraining subsystem configured to periodically update the model parameters of the recurrent neural networks and autoencoders using sliding window telemetry batches, with the retraining being triggered by statistical drift detection techniques that monitor the divergence between real-time data distributions and historical baselines.

[0039] In one embodiment, the ensemble evaluation subsystem (108) implements a weighted voting mechanism in which anomaly evaluations generated by statistical forecasting models are prioritized in short-term deviation scenarios and anomaly evaluations generated by machine learning models are prioritized in long-term trend deviation scenarios, with the weighting being dynamically adjusted using reinforcement learning feedback.

[0040] In one embodiment, the interruption classification module (110) uses a rule-based inference unit configured to apply correlation rules to anomaly clusters, classifying a simultaneous deterioration of the upstream SNR across multiple modems connected to a single amplifier as an amplifier fault, while classifying an isolated anomaly in a single modem telemetry data stream as a localized subscriber-side disturbance.

[0041] In one embodiment, the warning interface (112) comprises a geospatial mapping unit configured to display outage warnings on a map of a geographic information system (GIS), with device identifiers being assigned to physical coordinates to enable visual localization of service outages in residential areas, road segments or fiber optic main lines.

[0042] In one embodiment, the telemetry acquisition unit (102) also includes a multiprotocol acquisition layer configured to receive telemetry via Simple Network Management Protocol (SNMP), DOCSIS-compliant telemetry interfaces and gRPC streaming protocols, wherein the acquisition layer normalizes incoming data into a common scheme using a streaming ETL (Extract, Transform, Load) pipeline.

[0043] In one embodiment, the anomaly detection unit (106) comprises a temporal anomaly correlation subsystem configured to analyze the persistence of anomalies across multiple observation windows, classifying anomalies that persist beyond a statistically defined temporal threshold as service interruptions and anomalies below the threshold as transient disturbances.

[0044] In one embodiment, the preprocessing and harmonization module (104) is also configured to perform a frequency domain analysis of the telemetry parameters of the power spectral density (PSD), using deviations in the spectral noise distribution as secondary indicators for classifying service-disrupting interruptions.

[0045] In one embodiment, the anomaly detection unit (106) is deployed in a dedicated device comprising a multi-core processor, hardware-accelerated tensor computation units, a high-speed DRAM memory subsystem, and non-volatile memory, wherein the device executes the preprocessing, anomaly detection, and interrupt classification pipelines in real time at the edge of the broadband network.

[0046] The blockchain-based system for autonomous compliance with legal regulations in smart contracts is made possible by structurally defined hardware and tightly integrated, software-accelerated subsystems that create a tangible, practical apparatus: Distributed ledger nodes and validator devices are embodied as server-level hosts or dedicated ledger devices with network interface controllers, fast NVMe storage for blockchains, and consensus accelerator coprocessors for executing PoS / PoA routines; an on-chain environment for executing smart contracts is implemented on execution engines running within containerized runtimes on these hosts, with sandboxing enforced by hardware-backed trusted execution environments (TEEs) or secure enclaves.To protect contract status and confidentiality, a regulatory rules engine is structurally implemented as a combination of FPGA-accelerated pattern matching units and CPU / GPU-based policy interpreters that evaluate legal rules expressed in a formal policy language based on contract bytecode and transaction traces. Oracle adapters and data ingestion gateways are implemented as hardened API gateway devices and NIC-equipped middleware servers that retrieve, validate, and cryptographically sign off-chain compliance data (e.g., sanctions lists, license registers). Hardware security modules (HSMs) are used for key storage and signing. A compliance attestation module runs in TEEs and writes immutable, timestamped attestations and reasoning metadata to an append-only store or dedicated ledger channels.Meanwhile, an audit controller uses explainability processors to create explainable decision paths and stores them in tamper-proof archive arrays. A monitoring and enforcement fabric is implemented through telemetry collectors, intrusion detection devices, and automated transaction interceptors that, at the network fabric level, halt, flag, or forward transactions for human review via hardware-backed policy switches. Upgrade and governance controllers run on orchestration nodes with secure CI / CD pipelines that deliver signed policy updates to validator nodes, with rollback supported by snapshot-capable storage arrays. Management consoles and dashboards are rendered by GPU-enabled visualization servers and provide role-based access via hardware-backed authentication tokens and multi-factor modules. Finally, secure interoperability is achieved through cryptographic accelerators,Protocol translation modules and hardware-based rate limiters are achieved, connecting the ledger to external compliance systems. The concrete arrangement of these processors, accelerators, secure elements, storage systems, and network hardware ensures that the system is materially realizable and fully activated, and not merely an abstract specification.

[0047] The system for identifying service interruptions in cable broadband networks using telemetry-based anomaly detection employs an integrated pipeline of telemetry acquisition, harmonization, anomaly detection, and interruption classification, culminating in actionable alerts for operators. The core of the invention lies in its ability to transform diverse, noisy, and large-scale telemetry data into reliable insights that distinguish temporary fluctuations from genuine, service-disrupting events. The architecture was intentionally designed to overcome the lack of flexibility inherent in threshold-based monitoring and the interpretability issues of standalone machine learning models by implementing a hybrid anomaly detection framework with adaptive retraining and ensemble scoring.

[0048] The telemetry acquisition unit is designed as a multiprotocol acquisition unit capable of receiving data streams from cable modems, amplifiers, optical nodes, and CMTS units. Because these devices transmit telemetry in heterogeneous formats and at varying rates, the acquisition unit features dedicated interfaces supporting Simple Network Management Protocol (SNMP) traps, DOCSIS-compliant counters, and gRPC-based continuous streaming. Each telemetry stream contains multiple parameter classes, including physical layer statistics such as signal-to-noise ratio (SNR) and modulation error ratio (MER), transport layer statistics such as forward error correction (FEC) counters and latency, and higher-order performance metrics such as throughput and retransmission count.To manage this diversity, the acquisition unit applies real-time schema normalization using a streaming extract transform load (ETL) pipeline, which maps all data into a common representation format, thus enabling downstream modules to process it without taking device-specific characteristics into account.

[0049] Once the telemetry data is acquired, it is processed by the preprocessing and harmonization module, which ensures that the data is suitable for anomaly detection methods. Since different devices operate at different sampling frequencies, timestamps are aligned to a synchronized clock reference to guarantee temporal coherence across the network. Missing values, often caused by packet loss or intermittent messages, are filled in using weighted interpolation techniques. Unlike simple linear interpolation, the system dynamically adjusts the interpolation weights based on the temporal proximity of the available data points and the device class from which the data originates.For example, missing data from a CMTS can be interpolated with higher temporal sensitivity due to its important role in aggregation, while data from an end-user modem can be interpolated with broader smoothing to reduce noise sensitivity. Additionally, the module applies outlier filtering to remove telemetry points that deviate beyond plausibility limits but do not represent the actual network state, such as transient spikes due to measurement interference. Optionally, frequency domain transformations are also applied to specific telemetry streams, such as spectral power density data. This enables the detection of broadband noise interference patterns that may indicate upstream data intrusion or faulty shielding.

[0050] The harmonized telemetry data is then passed to the anomaly detection unit, which is designed as a hybrid framework combining statistical forecasting and machine learning models. Statistical forecasting modules such as ARIMA and Holt-Winters exponential smoothing are used to generate short-term predictions of expected values ​​for parameters such as SNR or latency. These models maintain adaptive baselines that evolve with seasonal and diurnal usage patterns. Deviations between predicted and observed values ​​are quantified using confidence intervals that dynamically adjust to reflect parameter variability. For example, a larger variance is acceptable for latency during peak hours, while a sudden spike outside this expected range will trigger anomaly flags.In parallel, machine learning models, particularly recurrent neural networks (RNNs) and autoencoders, are trained using historical telemetry sequences to detect nonlinear dependencies and temporal correlations. RNNs with long short-term memory (LSTM) units learn the temporal persistence of signal degradation, while autoencoders compress normal telemetry behavior into latent spaces and flag high reconstruction errors as potential anomalies.

[0051] The system does not rely on a single model for anomaly detection. Instead, an ensemble evaluation subsystem integrates results from both statistical forecasting and machine learning pipelines. The ensemble uses a weighted voting mechanism that dynamically prioritizes one model family over another based on the type of observed deviation. Short-term anomalies, such as sudden drops in upstream SNR, are assessed with a higher weighting of statistical forecast residuals, while long-term deteriorations, such as gradual deviations in power levels, are more reliably captured through autoencoder reconstruction losses or RNN-based deviation assessments. Reinforcement learning agents continuously adjust the ensemble weights by incorporating operator feedback on previous alerts, ensuring that the ensemble improves over time in the operational context.

[0052] To prevent model obsolescence, the anomaly detection unit includes an adaptive retraining subsystem. This subsystem monitors statistical deviations by comparing the distribution of current telemetry data with historical baselines. If the deviation metrics exceed predefined divergence thresholds, indicating a change in the underlying network behavior, retraining is automatically triggered. Retraining occurs using telemetry batches in a sliding window, ensuring the models remain synchronized with changing network conditions such as seasonal demand fluctuations, equipment upgrades, or firmware updates. By updating the models, the system reduces false alarms that would otherwise arise from outdated baselines.

[0053] Detected anomalies are processed by the interruption classification module. This module applies both temporal and spatial correlation to determine whether the anomalies represent service-disrupting outages. A temporal correlation subsystem tracks the persistence of anomalies across multiple observation windows and classifies short-term deviations as transient disturbances and longer-lasting deviations as likely service outages. Spatial correlation integrates information across devices and topologies. For example, simultaneous anomalies in multiple modems connected to the same amplifier strongly suggest an amplifier failure, while isolated anomalies in individual modems are flagged as subscriber-side disturbances. A rule-based inference unit formalizes these correlations using context-sensitive rules.Unlike conventional static correlation systems, this unit dynamically updates its control parameters based on feedback from confirmed interruptions. This allows the classification logic to adapt to new network topologies and novel fault conditions.

[0054] After classification is complete, the system generates structured alerts via the alert interface. Each alert contains metadata such as severity, fault type, associated cause, and affected geographic region. To assist operators with rapid visualization, the system features a geospatial mapping unit that overlays fault data onto a Geographic Information System (GIS). Device identifiers are assigned to physical coordinates, allowing operators to visualize faults at the level of residential areas, road segments, or main lines. This spatial representation not only accelerates troubleshooting but also supports predictive maintenance by highlighting recurring fault clusters.Warning messages are securely transmitted to the network management system using cryptographically anchored communication protocols, thereby ensuring the authenticity and integrity of the data against manipulation or injection attacks.

[0055] In one embodiment, the system is deployed as a dedicated device integrated into the broadband infrastructure. The device consists of a multi-core processor coupled with hardware-accelerated tensor compute units optimized for neural network inference, a high-speed DRAM subsystem for real-time telemetry buffering, and non-volatile memory for persistent base models and historical telemetry logs. Equipped with multiple telemetry acquisition ports supporting SNMP, DOCSIS, and gRPC protocols, the device includes an embedded software stack that performs real-time preprocessing, anomaly detection, and classification. By co-locating the device with CMTS units or amplifiers, the system achieves low-latency anomaly detection at the network edge.This reduces reliance on centralized processing and minimizes bandwidth consumption for telemetry backhaul. Simultaneously, the device can be synchronized with cloud-based controllers that aggregate data from multiple devices for fleet-wide analysis and provide regular model updates, improving detection accuracy across the network.

[0056] The described technical pipeline—comprising telemetry harmonization, hybrid anomaly detection with ensemble scoring, adaptive retraining, and context-aware outage classification—ensures that the system is not limited by the rigidity of threshold alarms, the lack of transparency of standalone machine learning models, or the latency of manual analysis. Instead, it provides a self-adapting, real-time, and context-sensitive mechanism for highly accurate service outage identification. The ability to distinguish transient anomalies from genuine outages reduces false alarms, while the integration of geospatial and topological context enables precise root cause localization.By combining statistical accuracy with the adaptability of machine learning, the system achieves scalability across millions of devices without compromising detection speed, making it a robust solution for modern broadband networks. The system consists of four main layers: 1. Telemetry data acquisition layer

[0057] This layer captures raw telemetry data from customer equipment, CMTS units, intermediate amplifiers, and optical nodes. The telemetry includes signal-to-noise ratio (SNR), modulation error ratio (MER), latency statistics, forward error correction (FEC) counters, and power levels. The data is collected via both push and pull protocols such as SNMP, DOCSIS telemetry interfaces, and gRPC streaming APIs. 2. Preprocessing and harmonization layer

[0058] The collected telemetry data often originates from heterogeneous sources with varying sampling rates and formats. This layer normalizes the data streams, aligns timestamps, fills in missing values ​​using interpolation techniques, and applies filters to remove outliers that do not represent the actual network state. The harmonized telemetry stream is then passed on to the anomaly detection layer. 3. Anomaly Detection Unit

[0059] This unit uses hybrid detection techniques that combine statistical baselines and machine learning models. Statistical modules perform time-series forecasting using ARIMA or Holt-Winters methods to predict expected behavior and flag deviations beyond adaptive confidence intervals. Machine learning modules include autoencoders and recurrent neural networks (RNNs) trained on historical telemetry data to detect nonlinear patterns and temporal dependencies. Ensemble anomaly scores are generated to enhance the robustness of the detection. 4. Interruption classification and warning level

[0060] The detected anomalies are further processed to classify whether they represent actual service interruptions. A rule-based inference system correlates anomalies across multiple devices and locations to isolate the causes. For example, simultaneous anomalies in the upstream SNR across a modem cluster are flagged as a likely upstream amplifier failure. Each interruption is assigned a severity level, cause metadata, and a geographic location to enable operators to quickly troubleshoot the issue. Alerts are then generated in real time and can be integrated into the network management system (NMS). Device for implementing the system

[0061] The invention further discloses a device in the form of a telemetry-based anomaly detection device. The device comprises: • Processing unit: A multi-core processor with dedicated signal processing accelerators, configured to run time series forecasts and neural network inference models in real time. • Data acquisition interfaces: Multiple input ports supporting DOCSIS-compatible telemetry streams, SNMP traps, and Ethernet-based gRPC connections. • Memory subsystem: High-speed DRAM and non-volatile memory for storing base models, historical telemetry logs, and anomaly detection parameters. • Communication module: An Ethernet / optical transceiver that supports secure data transmission to central monitoring systems. • Embedded software stack: Firmware that implements the pipelines for data harmonization, anomaly detection, and alarm generation, optimized for low-latency execution. • Form factor: The device can be implemented as a rack-mounted probe, as an edge-deployable device together with CMTS units, or as a modular pluggable unit in cable modem arrays.

[0062] The present invention falls within the technical field of monitoring and managing communication networks and relates in particular to systems and devices for detecting and classifying service interruptions in cable broadband infrastructures. It encompasses the acquisition, harmonization, and analysis of telemetry data from hybrid fiber-coaxial (HFC) networks and associated broadband elements, as well as the application of advanced anomaly detection techniques to differentiate transient anomalies from service-disrupting disturbances. The invention also includes the development of dedicated monitoring devices and edge deployment devices capable of implementing telemetry-based anomaly detection pipelines in real time. By integrating statistical, machine learning, and context-related correlation approaches, the invention provides a technical solution for highly accurate, low-latency, and scalable interruption detection in heterogeneous broadband networks.

[0063] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material usage. The range of embodiments is at least as broad as specified in the following claims.

[0064] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A system for detecting service interruptions in cable broadband networks using telemetry-based anomaly detection. 102 Telemetry - Acquisition Unit 104 Preprocessing and Harmonization Module 106 Anomaly Detection Unit 108 Ensemble evaluation subsystem 110 Interruption Classification Module 112 Alarm interface

Claims

[1] A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, the system comprising: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene. [2] System according to claim 1, wherein the preprocessing and harmonization module applies weighted interpolation techniques to compensate for missing values ​​in telemetry data streams, wherein the weights are dynamically adjusted based on temporal proximity and device class to preserve the statistical accuracy of the harmonized data set. [3] System according to claim 1, wherein the anomaly detection unit further comprises an adaptive retraining subsystem configured to periodically update the model parameters of the recurrent neural networks and autoencoders using sliding window telemetry batches, wherein the retraining is triggered by statistical drift detection techniques that monitor the divergence between real-time data distributions and historical baselines. [4] System according to claim 1, wherein the ensemble evaluation subsystem implements a weighted voting mechanism in which anomaly assessments generated by statistical forecasting models are prioritized in short-term deviation scenarios and anomaly assessments generated by machine learning models are prioritized in long-term trend deviation scenarios, wherein the weighting is dynamically adjusted using reinforcement learning feedback. [5] System according to claim 1, wherein the interruption classification module uses a rule-based inference unit configured to apply correlation rules to anomaly clusters, wherein a simultaneous deterioration of the upstream SNR across multiple modems connected to a single amplifier is classified as an amplifier fault, while an isolated anomaly in a single modem telemetry data stream is classified as a localized subscriber-side disturbance. [6] System according to claim 1, wherein the warning interface comprises a geospatial mapping unit configured to display outage warnings on a map of a geographic information system (GIS), wherein device identifiers are assigned to physical coordinates to enable visual localization of service outages in residential areas, road segments or fiber optic main lines. [7] System according to claim 1, wherein the anomaly detection unit includes a temporal anomaly correlation subsystem configured to analyze the persistence of anomalies across multiple observation windows, classifying anomalies that persist beyond a statistically defined temporal threshold as service interruptions and anomalies below the threshold as transient disturbances. [8] System according to claim 1, wherein the preprocessing and harmonization module is further configured to perform a frequency domain analysis of the telemetry parameters of the power spectral density (PSD), wherein deviations in the spectral noise distribution are used as secondary indicators for classifying service-disrupting interruptions. [9] System according to claim 1, wherein the anomaly detection unit is deployed in a dedicated device comprising a multi-core processor, hardware-accelerated tensor computation units, a high-speed DRAM memory subsystem and non-volatile memory, wherein the device executes the preprocessing, anomaly detection and interrupt classification pipelines in real time at the edge of the broadband network.

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