1. A system for Intelligent Anomaly Detection and Predictive Risk Management in Cloud Computing Environments, comprising: • a Data Collection Layer (110) configured to ingest and preprocess heterogeneous cloud data including logs, network traffic, performance metrics, and user activity; • cloud data including logs, network traffic, performance metrics, and user activity; • a Cognitive AI Engine (120) coupled thereto and configured to process the pre-processed data using machine learning models including autoencoders, clustering algorithms, and neural networks to establish a dynamic baseline and detect anomalies, and to assign risk scores using time-series analysis; • a Decision and Mitigation Module (130) configured to classify anomalies into risk categories and automatically execute corrective actions based on the risk scores; and • a User Interface and Reporting Layer (140) configured to display real-time anomaly data, logs, and mitigation actions; wherein feedback from executed corrective actions is relayed to the Cognitive AI Engine (120) for adaptive model updating.

2. The system as claimed in claim 1, wherein the Data Collection Layer (110) comprises agents deployed across servers, virtual machines, or containers for performing noise filtering, normalization, and feature extraction.

3. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) utilizes an autoencoder network to detect anomalies based on reconstruction error.

4. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) employs clustering algorithms to distinguish normal and anomalous behaviour patterns.

5. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) computes risk scores using time-series analysis based on frequency, intensity, and historical occurrence of anomalies.

6. The system as claimed in claim 1, wherein the Decision and Mitigation Module (130) classify anomalies into low, medium, and high-risk categories and executes corresponding corrective actions including resource reallocation, component isolation, or traffic blocking.

7. The system as claimed in claim 1, wherein the User Interface and Reporting Layer (140) comprises a dashboard configured to display visual indicators, graphs, logs, and provide manual override controls.

8. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) includes an adaptive learning mechanism configured to update models based on feedback from the Decision and Mitigation Module (130).

9. A method for intelligent anomaly detection and predictive risk management in cloud computing environments, comprising: • receiving heterogeneous data from various cloud sources in a Data Collection Layer (110); • preprocessing the received data by performing noise filtering, normalization, and feature extraction; • assigning risk scores using time-series analysis; • classifying anomalies into risk categories and executing corrective actions; and • displaying anomaly and mitigation information via a user interface, wherein feedback from executed actions is used to update the machine learning models.

10. The method as claimed in claim 9, wherein preprocessing is performed using agents deployed on servers, virtual machines, or containers, and wherein anomaly detection is carried out using autoencoder networks and clustering algorithms, and further wherein corrective actions including resource reallocation, component shutdown, or traffic blocking are automatically executed in response to high-risk anomalies.

Description:FIELD OF THE INVENTION
[001] The invention broadly relates to computer systems and cloud computing and, more particularly, to a system and method for intelligent anomaly detection and predictive risk management in cloud computing environments for enhanced security and optimized resource allocation across diverse industries.
BACKGROUND FOR THE INVENTION:
[002] Cloud computing environments have become increasingly dynamic, with continuous fluctuations in user activity, resource utilization, and security events necessitating reliable monitoring and proactive risk management. Industries relying on cloud services often encounter disruptions caused by sudden cyberattacks, operational downtimes, and inefficient resource allocations, which underscore the importance of addressing emerging risks before they result in significant damage.
[003] Existing solutions in the field predominantly employ fixed, rule-based methods or reactive mechanisms that address incidents only after they occur. Current practices rely on static detection algorithms and prescribed thresholds for identifying anomalies, which can be insufficient for managing the evolving complexities of modern cloud infrastructures.
[004] These traditional approaches are impeded by several drawbacks. Firstly, the dependency on predetermined rules limits their ability to recognize novel or sophisticated threats; secondly, reliance on segmented monitoring fails to capture the full spectrum of interactions across various system components; and thirdly, the lack of integrated predictive analytics prevents timely forecasting of potential failures, thereby delaying risk mitigation measures.
OBJECTS OF THE INVENTION:
[005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
[006] Object of the invention is to provide a multilayered architecture that intelligently integrates advanced data analytics, machine learning, and real-time control to deliver proactive anomaly detection and predictive risk management in cloud computing environments, thereby overcoming the reactive limitations of existing solutions.
[007] Another object of the invention is to provide a Cognitive AI Engine that employs unsupervised and semi-supervised learning models—including autoencoders, clustering algorithms, and neural networks—to dynamically learn normal operational patterns in a cloud environment and promptly detect deviations that may indicate evolving or previously unrecognized threats.
[008] Another object of the invention is to provide a predictive risk management methodology that utilizes time-series analysis and predictive analytics to assign quantitative risk scores to detected anomalies, thereby forecasting potential security breaches, system failures, or resource bottlenecks before they escalate into critical issues.
[009] Another object of the invention is to provide an automated Decision and Mitigation Module that, based on rule-based logic fused with dynamically generated risk scores, executes appropriate corrective actions—ranging from generating alerts to autonomously reallocating resources or blocking suspicious activities—thus significantly reducing the dependency on manual intervention.
[010] Another object of the invention is to provide a comprehensive Data Collection Layer capable of continuously ingesting heterogeneous data from diverse cloud sources—such as server logs, network traffic, application metrics, virtual machine usage, and user behavior and preprocessing this data via noise filtering, normalization, and feature extraction, ensuring robust inputs for subsequent machine learning processes.
[011] Yet another object of the invention is to provide an adaptive, self-improving AI system that continuously refines its underlying models via adaptive learning and reinforcement techniques, thereby maintaining high detection and prediction accuracy even as cloud environments and threat landscapes evolve over time.
[012] Finally, another object of the invention is to provide an intuitive User Interface and Reporting Layer that translates complex outcomes of anomaly detection and risk analysis into real-time dashboards with color-coded indicators and detailed reports, empowering system administrators with clear, actionable insights for effective decision-making and enhanced operational efficiency.
SUMMARY OF THE INVENTION:
[013] The present invention is described in the following sections by various embodiments. However, it should be understood that the invention can be implemented in various forms and is not limited to the specific embodiment provided herein. In the context of the present disclosure, it should be understood that the described embodiments in this section are put forth for illustrative purposes only. Those skilled in the art will appreciate that various modifications, adaptations, and alternative designs may be employed without departing from the scope and spirit of the invention. Accordingly, the present invention should not be limited to the specific embodiments illustrated herein, but rather should be construed according to the claims and description that follow.
[014] Embodiments of the present invention provide a system for intelligent anomaly detection and predictive risk management in cloud computing environments. The system comprises a Data Collection Layer configured to continuously ingest heterogeneous data from cloud sources such as server logs, network traffic, application performance metrics, virtual machine usage statistics, and user behavior logs. This layer further includes one or more data collection agents operable to perform noise filtering, normalization, and feature extraction on the raw data. The processed data is provided to a Cognitive AI Engine that comprises one or more machine learning models—including unsupervised learning, semi-supervised learning, autoencoder networks, clustering algorithms, and neural network models—configured to process historical as well as real-time preprocessed data to establish a dynamic baseline of normal cloud behavior and to flag deviations as anomalies. Additionally, the Cognitive AI Engine employs time-series analysis to assign risk scores to the detected anomalies. A Decision and Mitigation Module, communicatively coupled to the Cognitive AI Engine, comprises a rule-based decision system having decision trees for classifying detected anomalies into risk categories, and it is configured to automatically execute corrective actions based on the computed risk scores. In conjunction with these, a User Interface and Reporting Layer in communication with the Decision and Mitigation Module comprises an interactive dashboard that displays real-time visualizations, detailed logs, and reports of the detected anomalies and the corresponding mitigation actions. Feedback from executed corrective actions is relayed back to the Cognitive AI Engine to update and retrain the machine learning models.
[015] The advantages provided by this embodiment include the capacity to integrate diverse data streams with advanced machine learning techniques to enable the proactive detection and prediction of anomalous and potentially harmful behavior in cloud systems. The dynamic baseline determination and real-time risk scoring empower the system to preemptively forecast security breaches, performance degradations, or resource bottlenecks, thereby shifting cloud management from a reactive stance to a proactive, preventative approach. This not only minimizes downtime but also ensures optimal resource utilization and enhanced security posture.
[016] In accordance with an embodiment of the present invention, the Data Collection Layer further comprises a plurality of data collection agents deployed on physical servers, virtual machines, or containers, each configured to preprocess the raw data by performing noise filtering, normalization, and extraction of salient features utilized by the Cognitive AI Engine, thereby ensuring that high-quality, structured data is supplied for further analysis.
[017] In accordance with an embodiment of the present invention, the Cognitive AI Engine further employs an autoencoder network to reconstruct the incoming data and to calculate reconstruction errors, thus facilitating the detection of discrepancies that indicate anomalous events in the cloud environment.
[018] In accordance with an embodiment of the present invention, the Cognitive AI Engine also incorporates clustering algorithms selected from methods such as k-means and density-based clustering to segment and distinguish between normal behavior patterns and outlier data clusters, ensuring effective and accurate classification of the detected phenomena.
[019] In accordance with an embodiment of the present invention, the Cognitive AI Engine further comprises a time-series analysis module that employs statistical models to compute risk scores based on parameters including the frequency, intensity, and historical recurrence of detected anomalies. Further, this additional feature enhances the predictive risk management capability through more granular assessment of potential threats.
[020] In accordance with an embodiment of the present invention, the Decision and Mitigation Module comprise a decision tree system configured to classify the detected anomalies into risk categories of low, medium, or high. Additionally, high-risk anomalies trigger the automated execution of corrective actions selected from options such as resource reallocation, temporary shutdown of compromised components, and blocking of suspicious network traffic, thereby reducing downtime and minimizing operational disruption.
[021] In accordance with an embodiment of the present invention, the User Interface and Reporting Layer comprises a web-based interactive dashboard that displays color-coded visual indicators, time-series graphs, and detailed anomaly logs. In addition, the dashboard provides options for the manual override of the automated corrective actions, thereby giving administrators enhanced control over the remediation process while simultaneously displaying real-time actionable insights.
[022] In accordance with an embodiment of the present invention, an adaptive learning mechanism is integrated within the Cognitive AI Engine, wherein outputs received from the Decision and Mitigation Module are fed back to update and retrain the machine learning models. This closed-loop feedback system ensures continuous enhancement of anomaly detection and risk prediction performance as the cloud environment evolves.
[023] Embodiments of the present invention additionally provide a method for intelligent anomaly detection and predictive risk management in cloud computing environments. In one embodiment, the method comprises receiving heterogeneous data from various cloud sources in a Data Collection Layer, preprocessing such data through noise filtering, normalization, and feature extraction, and processing the preprocessed data in a Cognitive AI Engine that employs machine learning models to establish a dynamic baseline of normal cloud behavior and to detect deviations therefrom. The method further comprises assigning risk scores to the detected anomalies by employing time-series analysis and statistical learning, classifying the detected anomalies into risk categories using a Decision and Mitigation Module with rule-based decision logic, and automatically executing corrective actions corresponding to the risk categories. In addition, the method includes displaying real-time visual dashboards, logs, and detailed reports via a User Interface and Reporting Layer, and relaying feedback from the corrective actions back to the Cognitive AI Engine to update the machine learning models.
[024] In further embodiments, the preprocessing step comprises deploying one or more software agents on physical servers, virtual machines, or containers to collect data and perform initial normalization before the feature extraction step, wherein the Cognitive AI Engine employs an autoencoder network along with clustering algorithms to compute reconstruction errors and isolate groups of outlier data indicative of anomalous events. Additionally, the corrective actions automatically executed in response to high-risk anomalies include measures such as resource reallocation, temporary shutdown of compromised components, and blocking of suspicious network traffic, thereby ensuring comprehensive proactive risk management.
BRIEF DESCRIPTION OF DRAWINGS:
[025] In order to facilitate a comprehensive understanding of the detailed features of the present invention, a more specific description of the invention, briefly summarized above, may have been referenced through various embodiments, some of which are depicted in the accompanying drawings. It should be emphasized, however, that the provided drawings merely exemplify typical embodiments of the present invention and should not be construed as limiting its scope, as the invention may encompass other equally efficacious embodiments.
[026] These and additional features, advantages, and benefits of the present invention will become apparent by consulting the following textual illustration, wherein similar reference numerals denote similar components throughout the various views.
[027] Fig. 1 illustrates the system architecture of Cognitive Cloud Shield, in accordance with an embodiment of the present invention;
[028] Fig. 2 illustrates the Cognitive AI Engine Workflow, in accordance with an embodiment of the present invention;
[029] Fig. 3 illustrates the risk mitigation flowchart, in accordance with an embodiment of the present invention;
[030] Fig. 4 illustrates the dashboard example, in accordance with an embodiment of the present invention;
[031] Fig. 5 illustrates the implementation flow, in accordance with an embodiment of the present invention;
DETAILED DESCRIPTION OF INVENTION:
[032] The present invention is subsequently described herein using various embodiments with reference to the accompanying drawing, wherein the reference numerals utilized in the accompanying drawing correspond to the similar elements throughout the description. While the present invention is illustratively described herein by way of example using embodiments and accompanying drawings, those skilled in the art will acknowledge that the invention is not limited to the described embodiments or drawings and is not intended to represent the scale of the different components. Furthermore, certain components that may constitute a part of the invention might not be depicted in specific figures for the purpose of simplified illustration, and such omissions do not restrict the outlined embodiments in any manner. It should be comprehended that the drawings and the detailed description provided are not intended to limit the invention to the particular disclosed form, but instead, the invention is intended to encompass all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim. Throughout this description, the term 'may' is used in a permissive sense, indicating the potential to, rather than in a mandatory sense, indicating a requirement. Additionally, the words 'a' or 'an' signify at least one, and the word 'plurality' signifies 'one or more' unless otherwise specified. Moreover, the terminology and phraseology employed herein are solely for descriptive purposes and should not be construed as limiting in scope. Terms such as 'including', 'comprising', 'having', 'containing', or 'involving', and their variations, are intended to be broad and encompass the listed subject matter thereafter, as well as equivalents and additional subject matter not explicitly mentioned, and should not be interpreted as excluding other additives, components, integers, or steps. Similarly, the term 'comprising' is considered synonymous with the terms 'including' or 'containing' for applicable legal purposes.
[033] The invention relates to an intelligent anomaly detection and predictive risk management system for cloud computing environments, known as Cognitive Cloud Shield. The system comprises a multilayered architecture integrating data collection, machine learning, and automated control to continuously monitor cloud infrastructures. The data collection layer (110) is configured to ingest heterogeneous data from multiple cloud sources, including server logs, network traffic, and application performance metrics, and preprocess the inputs using noise filtering, normalization, and feature extraction. The Cognitive AI Engine (120) processes the preprocessed data using unsupervised and semi-supervised machine learning models, including autoencoders, clustering algorithms, and neural networks, to establish a dynamic baseline of normal cloud behavior and identify deviations as anomalies.
[034] The invention further provides a decision and mitigation module (130) that interprets risk scores generated by the AI engine and executes corrective actions using a rules-based decision system. The module determines mitigation actions such as resource reallocation or automated alerts corresponding to risk levels. A user interface and reporting layer (140) displays real-time dashboards, logs, and reports, enabling administrative oversight and intervention. The system includes a feedback mechanism wherein outcomes of corrective actions are relayed back to the AI engine to update and refine models, ensuring adaptive performance.
[035] Various modifications are possible, and the invention is not limited to the described embodiments but encompasses all variations consistent with its principles and inventive features.
[036] Figure 1 illustrates a multilayered system architecture comprising four components: data collection layer (110), Cognitive AI Engine (120), decision and mitigation module (130), and user interface layer (140). The data collection layer includes data acquisition modules that ingest logs, network metrics, user activity, and resource usage from distributed cloud nodes. These modules may include cloud-native agents deployed across servers, virtual machines, or containers. The layer may be implemented as a scalable subsystem with modular pipelines and buffering units for high-velocity data streams. Processed data is transmitted to the AI engine through secure interfaces.
[037] The Cognitive AI Engine (120) performs anomaly detection, predictive risk analysis, and adaptive learning. It includes software frameworks supporting machine learning and neural network models. Input data undergoes feature extraction, temporal modeling, and scoring. The engine supports real-time updates of training weights and features and may be deployed on scalable cloud-based computer resources with secure processing capabilities. The decision and mitigation module (130) receives outputs from the AI engine and executes corrective actions based on risk levels. It includes a rules engine, orchestration logic, and alerting subsystems arranged as a decision tree. It may be implemented as a fault-tolerant subsystem hosted on secure infrastructure and supports automated intervention and notifications.
[038] The user interface and reporting layer (140) provides dashboards, alerts, and control panels accessible via web or native applications. It includes visualization panels, log viewers, and reporting tools, enabling administrators to monitor system status and take actions.
[039] The data collection layer (110) may include adaptive buffering logic to handle variable data volumes. Lightweight agents may be embedded within cloud infrastructure using secure containerization. Feature extraction may utilize low-latency processing units for efficient transformation of telemetry data. The Cognitive AI Engine (120) may employ hierarchical neural networks combining autoencoders and clustering models to establish behavioral baselines. Reinforcement-based modules may auto-tune parameters based on feedback, enabling continuous learning. The engine may be deployed on GPU-enabled clusters with integration to external intelligence feeds.
[040] The decision and mitigation module (130) may include customizable rules and secure execution environments for real-time orchestration of mitigation actions. Feedback from executed actions may be returned to the AI engine for model refinement. Audit logging may also be incorporated. The user interface may include advanced visualization tools, role-based access control, and automated reporting features for compliance. The system may integrate with external platforms and support hybrid cloud deployments.
[041] Figure 2 illustrates the AI workflow comprising input data, feature extraction, anomaly detection, predictive risk analysis, and output modules. The input module receives data from logs, metrics, and user activity through secure connectors. The feature extraction module transforms raw data into structured features using filtering, normalization, and dimensionality reduction. It may be implemented using parallel processing units or cloud-native functions. The anomaly detection module utilizes machine learning models such as autoencoders and clustering algorithms to establish baselines and identify deviations. It may be deployed across cloud nodes with hardware acceleration for efficiency. The predictive risk analysis module computes risk scores based on anomaly severity and impact using time-series analytics and predictive models. Visualization elements may map quantitative scores to qualitative levels. The output module generates alerts, mitigation commands, and reports, communicating results to downstream systems through messaging protocols.
[042] Feature extraction may use programmable logic to dynamically select features. Anomaly detection may incorporate recurrent neural networks to capture temporal dependencies and differentiate normal fluctuations from anomalies. Risk analysis may integrate external threat intelligence and employ statistical or Bayesian models for enhanced prediction. Output modules may support automated orchestration, multi-channel alerts, and reporting customization.
[043] Figure 3 illustrates a mitigation workflow including anomaly detection, risk scoring, and categorized responses. Anomaly detection identifies deviations and forwards them to a risk scoring module, which evaluates severity and impact. The workflow branches into low, medium, and high-risk pathways. Low-risk events trigger logging and notifications. Medium-risk events generate mitigation suggestions such as resource adjustments. High-risk events initiate automated actions such as isolation or termination of compromised resources. All pathways lead to notification systems for administrator awareness.
[044] The anomaly detection module may operate as a cloud-native microservice with redundant resources. Risk scoring may use adaptive models with continuous calibration. Low-risk modules may include logging and alert systems. Medium-risk modules may provide intelligent recommendations using decision trees. High-risk modules may execute automated interventions using secure execution environments. Variations may include integration with external incident management platforms, additional risk categories, and customizable workflows.
[045] Figure 4 illustrates a dashboard comprising system overview, risk visualization graphs, anomaly lists, mitigation logs, and recommended actions. The overview panel displays key metrics such as anomaly count and risk levels. Graphs show time-based risk trends. Anomaly lists provide categorized events with severity indicators. Logs display mitigation actions, and recommendation panels suggest next steps. The dashboard includes navigation panels for accessing system modules. It is designed for modular configuration and secure communication.
[046] The dashboard may include customizable filters, real-time updates, and interactive visualization tools. Alerts may be delivered via multiple channels such as email or notifications. Role-based access control may restrict actions, and reporting tools may generate compliance reports. The interface may support mobile or multi-tenant deployment and integration with external systems.
[047] Figure 5 illustrates data flow from cloud resources to the Cognitive Cloud Shield system, followed by analysis and output to the admin dashboard. Cloud services provide telemetry through agents or sensors. Data is processed through anomaly detection, risk analysis, and mitigation modules. Outputs include alerts, actions, and reports delivered to administrators. The system may include redundant architecture and failover mechanisms to ensure reliability. It supports integration with cloud APIs for automated control.
[048] Sensors may vary depending on deployment context and use secure communication channels. Analytical modules may operate in parallel for efficiency. The dashboard provides controls for monitoring and managing system behavior. Reporting tools support audit and compliance requirements. Variations may include integration with external platforms, hybrid cloud support, and configurable automation levels.
[049] The method performs real-time anomaly detection and predictive risk management through sequential steps. Data is collected from cloud sources via the data collection layer, pre-processed, and transmitted to the AI engine. Machine learning models establish behavioural baselines and compute anomaly risk scores. These scores are processed by the decision module, which classifies risks and executes appropriate mitigation actions. Feedback from these actions is returned to the AI engine for continuous model refinement.
[050] Various modifications to these embodiments are evident to those skilled in the art based on the description and accompanying drawings. The principles associated with the various embodiments described herein can be applied to additional embodiments. Consequently, the description is not intended to be limited to the embodiments shown in conjunction with the accompanying drawings but aims to provide the broadest scope consistent with the principles and the innovative and inventive features disclosed or suggested herein. Therefore, the invention is expected to encompass all other such alternatives, modifications, and variations falling within the scope of the present invention and the appended claims. , Claims:We Claim:
1. A system for Intelligent Anomaly Detection and Predictive Risk Management in Cloud Computing Environments, comprising:
• a Data Collection Layer (110) configured to ingest and preprocess heterogeneous cloud data including logs, network traffic, performance metrics, and user activity;
• cloud data including logs, network traffic, performance metrics, and user activity;
• a Cognitive AI Engine (120) coupled thereto and configured to process the pre-processed data using machine learning models including autoencoders, clustering algorithms, and neural networks to establish a dynamic baseline and detect anomalies, and to assign risk scores using time-series analysis;
• a Decision and Mitigation Module (130) configured to classify anomalies into risk categories and automatically execute corrective actions based on the risk scores; and
• a User Interface and Reporting Layer (140) configured to display real-time anomaly data, logs, and mitigation actions;
wherein feedback from executed corrective actions is relayed to the Cognitive AI Engine (120) for adaptive model updating.
2. The system as claimed in claim 1, wherein the Data Collection Layer (110) comprises agents deployed across servers, virtual machines, or containers for performing noise filtering, normalization, and feature extraction.
3. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) utilizes an autoencoder network to detect anomalies based on reconstruction error.
4. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) employs clustering algorithms to distinguish normal and anomalous behaviour patterns.
5. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) computes risk scores using time-series analysis based on frequency, intensity, and historical occurrence of anomalies.
6. The system as claimed in claim 1, wherein the Decision and Mitigation Module (130) classify anomalies into low, medium, and high-risk categories and executes corresponding corrective actions including resource reallocation, component isolation, or traffic blocking.
7. The system as claimed in claim 1, wherein the User Interface and Reporting Layer (140) comprises a dashboard configured to display visual indicators, graphs, logs, and provide manual override controls.
8. The system as claimed in claim 1, wherein the Cognitive AI Engine (120) includes an adaptive learning mechanism configured to update models based on feedback from the Decision and Mitigation Module (130).
9. A method for intelligent anomaly detection and predictive risk management in cloud computing environments, comprising:
• receiving heterogeneous data from various cloud sources in a Data Collection Layer (110);
• preprocessing the received data by performing noise filtering, normalization, and feature extraction;
• assigning risk scores using time-series analysis;
• classifying anomalies into risk categories and executing corrective actions; and
• displaying anomaly and mitigation information via a user interface,
wherein feedback from executed actions is used to update the machine learning models.
10. The method as claimed in claim 9, wherein preprocessing is performed using agents deployed on servers, virtual machines, or containers, and wherein anomaly detection is carried out using autoencoder networks and clustering algorithms, and further wherein corrective actions including resource reallocation, component shutdown, or traffic blocking are automatically executed in response to high-risk anomalies.