An intelligent system for the joint detection of anomalies in distributed systems

Abstract

An intelligent system for the joint detection of anomalies in distributed systems, comprising the following: an anomaly detection module deployed on distributed nodes, with each agent configured to monitor local system metrics, detect deviations from expected behavior, and identify potential anomalies using machine learning models; a collaborative learning machine that can be operated to facilitate peer-to-peer communication between the anomaly detection module, with the module working together to verify detected anomalies and correlate cross-node behaviors in real time; a centralized aggregation server configured to aggregate anomaly data from the distributed nodes, update a global anomaly detection model, and redistribute the updated model to each of the anomaly detection modules; a federated learning module configured to enable secure data exchange between distributed nodes without disclosing sensitive data, exchanging only updates to the machine learning model to protect privacy; a warning and mitigation module configured to send real-time alerts to system administrators or automated incident response systems when an anomaly is detected; categorizes alerts by severity and initiates automatic mitigation protocols to address critical threats; a self-learning and adaptation module capable of continuously updating local and global machine learning models, learning from past anomalies to detect emerging threats, and adjusting detection thresholds based on evolving threat landscapes; the global anomaly detection model refines local detection capabilities by incorporating insights from system-wide anomaly data across all nodes.

Description

[0001] The present invention relates to anomaly detection in distributed systems, and more particularly to an intelligent, collaborative anomaly detection system that utilizes machine learning, peer-to-peer communication, and centralized aggregation to improve the detection and response capabilities of distributed environments.

[0002] In modern distributed systems such as cloud environments, data centers, and decentralized networks, ensuring operational stability and security is of utmost importance. These systems operate across multiple nodes, often located in different geographic regions or network segments, making the task of monitoring for anomalies, errors, or malicious activity extremely complex. Anomalies can include hardware failures, software bugs, network intrusions, distributed denial of service (DDoS) attacks, or deviations in traffic patterns, all of which can have a significant impact on system performance and security. Traditionally, anomaly detection systems operate in isolation. Each node or subsystem in a distributed environment is monitored independently. Predefined rules are used to detect deviations from normal behavior, such as threshold violations in CPU usage or memory consumption.These rules are static and must be frequently updated as systems evolve. Systems use statistical baselines to detect abnormal behavior by comparing real-time data with expected norms. While these models are effective for simple deviations, they struggle with complex, evolving anomalies, especially in dynamic environments. More advanced approaches use supervised or unsupervised machine learning models for anomaly detection. These models can adapt to changing patterns but often require extensive training data and computational resources and typically operate independently at each node.

[0003] In traditional systems, the focus is on detecting local anomalies, with each node operating autonomously and taking corrective actions based on anomalies it detects within its area. However, this approach has significant drawbacks, especially in large, interconnected, and distributed environments. Conventional methods focus on detecting anomalies within individual nodes without sharing information with other nodes. This limits the ability to detect coordinated or distributed attacks and makes it difficult to correlate anomalies that may seem insignificant at individual nodes but, taken collectively, represent a larger system-wide problem. Because nodes operate in silos, potential patterns of attacks or failures across the system may go unnoticed.In a DDoS attack that spreads across multiple nodes, isolated monitoring systems may only detect unusual traffic at individual nodes, but may not be able to identify the context and identify the attack as a coordinated attempt. Traditional rule-based systems often generate high false positive rates because they rely on static thresholds or rules.

[0004] Any deviation from a predefined threshold triggers an alert, even if it doesn't indicate an actual problem. Conversely, false negatives occur when evolving threats fall outside the predefined rule sets, leading to missed detections. Static models or hard-coded rules cannot keep pace with dynamic environments, especially in cloud or multi-node systems where workloads, traffic patterns, and resource utilization are constantly changing. While machine learning-based anomaly detection offers greater adaptability, it faces significant scaling challenges in distributed environments. Each node must train and update its models independently, consuming large amounts of compute resources and requiring extensive historical data.Sharing raw data across nodes for model training can raise privacy and security concerns, especially in sectors like healthcare and finance, where the data is highly sensitive. Traditional systems tend to respond slowly to threats that affect multiple nodes simultaneously. Because there is no real-time collaboration between nodes, the measures taken to contain an anomaly may not be sufficient to prevent the problem from spreading throughout the system. Most traditional anomaly detection systems are reactive, responding only after an anomaly has caused noticeable damage. This can lead to extended downtime, compromised data integrity, or security breaches before appropriate remediation measures are taken.

[0005] Some conventional solutions attempt to solve the problem of distributed anomaly detection by using a central monitoring server that aggregates logs and data from multiple nodes. However, this results in a single point of failure. If the central server is compromised or overloaded, the entire anomaly detection mechanism can become ineffective. Centralized models struggle with scalability in large distributed systems. As the number of nodes increases, the central server can become a bottleneck, limiting the ability to process and analyze data in real time. Traditional methods, particularly rule-based and statistical models, are often ineffective at detecting sophisticated or zero-day attacks that exploit unknown vulnerabilities or introduce previously unseen behaviors into the system.These attacks can bypass the rigid detection mechanisms of conventional systems and remain undetected until significant damage has been done. Even in systems based on machine learning, nodes operate in isolation, so each node must learn from its own set of anomalies. Updates to detection models typically occur with a delay, meaning that an anomaly detected and resolved at one node may affect other nodes before they can be updated with the same insights.

[0006] The disadvantages of conventional anomaly detection methods highlight the need for a collaborative, efficient, and scalable solution. Distributed systems require a mechanism that enables nodes to not only detect anomalies within their local area, but also to share results and insights with other nodes. This collaboration would enable real-time correlation of anomalies across the entire system and improve detection accuracy and responsiveness.

[0007] To solve this problem, the present invention provides an intelligent system for collaborative anomaly detection in distributed systems.

[0008] The intelligent system for collaborative anomaly detection in distributed systems can improve the accuracy of anomaly detection by leveraging collaborative machine learning models across distributed nodes, thereby reducing false positives and negatives caused by isolated monitoring.

[0009] The intelligent system for collaborative anomaly detection in distributed systems, which enables real-time detection and mitigation of anomalies in distributed systems by enabling decentralized peer-to-peer communication between nodes for faster identification and response.

[0010] The intelligent system for collaborative anomaly detection across distributed systems, enabling cross-node correlation of anomalies so that the system can detect patterns and threats that manifest subtly across multiple nodes, improving the detection of system-wide problems.

[0011] The intelligent system for collaborative anomaly detection in distributed systems, which enables cross-node correlation of anomalies, enabling the system to detect patterns and threats that manifest subtly across multiple nodes, thereby improving the detection of system-wide problems.

[0012] The intelligent system for collaborative anomaly detection in distributed systems that scales efficiently with the size of the distributed environment, minimizing resource consumption while ensuring timely anomaly detection across all nodes.

[0013] The intelligent system for collaborative anomaly detection across distributed systems that can facilitate continuous learning and improvement of machine learning models through collaborative data sharing between nodes and periodic updates from a centralized aggregation server, ensuring adaptability to evolving anomalies and threats.

[0014] The intelligent system for collaborative anomaly detection across distributed systems, which can reduce the dependence on centralized monitoring systems by enabling decentralized anomaly detection through peer-to-peer communication, thus eliminating single points of failure and ensuring system resilience.

[0015] The intelligent system for collaborative anomaly detection in distributed systems, which enables proactive detection of emerging and previously unknown anomalies, including zero-day attacks, by leveraging the collective insights of distributed nodes and the collaborative learning engine.

[0016] The intelligent system for collaborative anomaly detection in distributed systems that can ensure that sensitive data from individual nodes remains secure by using privacy-preserving techniques such as federated learning, which enables the sharing of anomaly insights without compromising data privacy.

[0017] The intelligent system for collaborative anomaly detection in distributed systems, which can minimize downtime and improve system availability by providing an early warning system that quickly identifies anomalies and initiates corrective actions, thereby reducing the risk of large-scale system failures.

[0018] The intelligent system for collaborative anomaly detection in distributed systems, which can be used in sensitive environments such as healthcare or finance, where data protection and compliance are critical, and which ensures that the system operates within the legal and regulatory framework.

[0019] In one embodiment, an intelligent system for cooperative anomaly detection in distributed systems is provided. The system addresses the limitations of traditional, isolated approaches to anomaly detection in large-scale distributed environments such as cloud infrastructures, data centers, and decentralized networks. Conventional systems primarily focus on detecting local anomalies, resulting in a lack of coordination, a high number of false positives and negatives, delayed responses, and scalability issues. To overcome these challenges, this invention introduces a collaborative approach that enables distributed nodes to work together in real time to detect and mitigate anomalies more efficiently.

[0020] At the heart of the system are distributed anomaly detection modules installed on each node. These modules use machine learning models to monitor local activity such as CPU utilization, network traffic, and error logs, and detect deviations from normal behavior. When a potential anomaly is detected, the module communicates with neighboring nodes through a collaborative learning engine that enables peer-to-peer communication. The system also includes a central aggregation server that collects and processes anomaly insights from all nodes. This server updates a global anomaly detection model based on the aggregated data, which is then distributed to individual nodes to improve their local detection capabilities.By combining decentralized and centralized monitoring, the system mitigates the risks associated with a single point of failure while ensuring system-wide, real-time anomaly detection.

[0021] Additionally, the system uses privacy-preserving techniques such as federated learning, which allows nodes to share insights without exposing sensitive data, making it suitable for use in industries with strict privacy regulations, such as healthcare and finance.

[0022] The system is scalable, adaptive, and proactive, enabling continuous learning and the improvement of machine learning models through shared data. It enables real-time detection, faster responses to anomalies, and improved system resilience. The system also supports proactive detection of emerging threats, including zero-day attacks, by leveraging collective insights from distributed nodes.

[0023] The invention is explained again below with reference to the figure. It shows: Fig. : an intelligent system for collaborative anomaly detection in distributed systems.

[0024] Fig.shows an intelligent system for collaborative anomaly detection in distributed systems. The intelligent system for collaborative anomaly detection in distributed systems includes an anomaly detection module, a collaborative learning engine, a centralized aggregation server, a federated learning module, an alerting and mitigation module, and a self-learning and adaptation module. The anomaly detection module is deployed on distributed nodes, with each agent configured to monitor local system metrics, detect deviations from expected behavior, and identify potential anomalies using machine learning models.The collaborative learning engine is configured to facilitate peer-to-peer communication between the anomaly detection modules, where the modules work together to verify detected anomalies and correlate cross-node behaviors in real time. The centralized aggregation server is configured to aggregate anomaly data from the distributed nodes, update a global anomaly detection model, and further distribute the updated model to each of the anomaly detection modules. The federated learning module is configured to enable secure data exchange between the distributed nodes without exposing sensitive data, exchanging only updates to the machine learning model to preserve privacy.The alerting and mitigation module is configured to send real-time alerts to system administrators or automated incident response systems when an anomaly is detected; it categorizes alerts by severity and initiates automatic mitigation protocols to address critical threats. The self-learning and adaptation module continuously updates local and global machine learning models, learning from past anomalies to detect emerging threats and adjusting detection thresholds based on the evolving threat landscape. The global anomaly detection model refines local detection capabilities by incorporating insights from system-wide anomaly data across all nodes.

[0025] The collaborative learning module enables real-time collaboration between nodes to share insights into local anomalies, correlate anomalies based on system-wide data patterns, and ensure faster detection of distributed threats or anomalies affecting multiple locations in the system. The anomaly detection module uses historical data and real-time metrics such as CPU utilization, memory usage, network traffic, and error logs to detect deviations from baseline performance. The centralized aggregation server maintains and updates the global anomaly detection model by aggregating anomaly metadata from distributed nodes and applying machine learning algorithms to optimize detection performance across the system.The Anomaly Correlation and Analysis module is configured to analyze anomaly metadata such as timestamps, system states, and node-specific behavior, detect patterns indicative of distributed attacks or multi-vector anomalies, and provide cross-node insights to system administrators. The Federated Learning module ensures data privacy compliance by enabling secure collaboration between distributed nodes without directly sharing raw system data. The Scalability and Load Balancing module is configured to dynamically distribute compute resources across distributed nodes to balance the anomaly detection workload and ensure efficient operation as the system scales.The collaborative learning module reduces false positives and false negatives in anomaly detection by aggregating and correlating anomaly insights from multiple nodes. The data preprocessing and feature extraction module is configured to filter noise, reduce dimensionality, and select relevant system metrics to improve the accuracy of machine learning-based anomaly detection models. The incident response interface enables the following: providing a real-time dashboard for administrators to visualize anomaly and system performance trends, providing actionable insights for manual intervention when needed, and providing logs and reports for audit purposes. List of reference symbols 100 systems

Claims

[1] An intelligent system for collaborative anomaly detection in distributed systems, comprising: an anomaly detection module deployed on distributed nodes, with each agent configured to monitor local system metrics, detect deviations from expected behavior, and identify potential anomalies using machine learning models; a collaborative learning engine operable to facilitate peer-to-peer communication between the anomaly detection module, with the module collaborating to verify detected anomalies and correlate cross-node behaviors in real time; a centralized aggregation server configured to aggregate anomaly data from the distributed nodes, update a global anomaly detection model, and distribute the updated model to each of the anomaly detection modules; a federated learning module configured to enable secure data exchange between the distributed nodes without exposing sensitive data, exchanging only updates to the machine learning model to preserve privacy; An alerting and mitigation module configured to send real-time alerts to system administrators or automated incident response systems when an anomaly is detected; categorizes alerts by severity and initiates automatic mitigation protocols to address critical threats; a self-learning and adaptation module capable of continuously updating local and global machine learning models, learning from past anomalies to detect emerging threats, and adjusting detection thresholds based on evolving threat landscapes; where the global anomaly detection model refines local detection capabilities by incorporating insights from system-wide anomaly data across all nodes. [2] The system of claim 1, wherein the collaborative learning engine enables real-time collaboration between nodes to share insights on local anomalies, correlate anomalies based on system-wide data patterns, and ensure faster detection of distributed threats or multi-point anomalies affecting the system. [3] The system of claim 1, wherein the anomaly detection module uses historical data and real-time metrics such as CPU utilization, memory usage, network traffic, and error logs to detect deviations from baseline performance. [4] The system of claim 1, wherein the centralized aggregation server maintains and updates the global anomaly detection model by aggregating anomaly metadata from distributed nodes and applying machine learning algorithms to optimize detection performance across the system. [5] The system of claim 1, further comprising an anomaly correlation and analysis module configured to analyze anomaly metadata such as timestamps, system states, and node-specific behaviors, detect patterns indicative of distributed attacks or multi-vector anomalies, and provide cross-node insights to system administrators. [6] The system of claim 1, wherein the federated learning module ensures privacy compliance by enabling secure collaboration between distributed nodes without directly sharing raw system data. [7] The system of claim 1 further comprises a scalability and load balancing module configured to dynamically distribute computing resources among the distributed nodes to balance the workloads in detecting anomalies and to ensure efficient operation as the system scales. [8] The system of claim 1, wherein the collaborative learning module reduces false positives and false negatives in anomaly detection by aggregating and correlating anomaly insights from multiple nodes. [9] The system of claim 1, further comprising a data preprocessing and feature extraction module configured to filter noise, reduce dimensionality, and select relevant system metrics to improve the accuracy of the machine learning-based anomaly detection models. [10] The system of claim 1 further comprises an incident response interface operable to: provide a real-time dashboard for administrators to visualise anomaly trends and system performance, provide actionable insights for manual intervention when required, and provide logs and reports for audit purposes.

Images