Description:FIELD OF THE INVENTION

[001] The present invention relates to the field of computer science and engineering, more specifically to network security in Internet of Things (IoT) environments. It pertains to systems and methods for detecting anomalies in IoT network traffic using advanced computational models. In particular, the invention involves the application of Kolmogorov-Arnold Networks (KANs) for modeling and analyzing complex, high-dimensional, and nonlinear data patterns to identify potential security threats in real-time. The invention addresses challenges associated with conventional anomaly detection methods by offering a lightweight, scalable, and adaptive solution suitable for resource-constrained IoT devices and infrastructures.

BACKGROUND OF THE INVENTION
[002] The proliferation of Internet of Things (IoT) devices across multiple sectors, including healthcare, manufacturing, energy, and smart infrastructure, has led to an exponential increase in the volume and complexity of network traffic. While IoT devices enable real-time data exchange and automation, their widespread deployment introduces significant security vulnerabilities. The decentralized and heterogeneous nature of IoT ecosystems, coupled with limited computational capabilities and weak encryption protocols, renders them susceptible to a wide array of cyber threats, including denial-of-service (DoS) attacks, spoofing, data exfiltration, and man-in-the-middle intrusions.
[003] Conventional anomaly detection mechanisms in networked environments primarily rely on signature-based or rule-based models. These methods are inherently limited to identifying known patterns and are therefore ineffective in detecting previously unseen or evolving attack vectors. While certain machine learning and deep learning models have been proposed to overcome these limitations, they often entail significant computational overhead, require extensive labeled datasets for training, and may lack generalization capability in dynamic traffic scenarios typical of IoT deployments.
[004] The nature of IoT traffic is distinctively high-dimensional, nonlinear, and context-dependent, encompassing diverse data formats such as sensor readings, logs, and real-time streaming inputs. Owing to variations in user behavior, environmental conditions, and device interactions, the underlying patterns within the network traffic are highly dynamic and non-stationary. Capturing and analyzing such data using traditional linear models often results in suboptimal detection accuracy and increased false positives, thereby undermining the reliability and trustworthiness of security mechanisms.
[005] The inadequacy of existing solutions necessitates the adoption of a more mathematically robust and computationally efficient approach. The Kolmogorov–Arnold representation theorem provides a theoretical basis for modeling any multivariate continuous function as a composition of univariate functions. Leveraging this principle, Kolmogorov-Arnold Networks (KANs) have been developed to model complex nonlinear functions with fewer parameters than conventional deep neural networks. KANs are therefore well-suited for anomaly detection tasks in IoT environments, offering high expressive power, interpretability, and adaptability with significantly reduced computational requirements.
[006] Accordingly, there exists a need for an improved anomaly detection framework that can operate effectively under resource constraints while ensuring high accuracy and scalability across heterogeneous IoT environments. The present invention addresses this need by proposing a novel IoT security system that employs Kolmogorov-Arnold Networks for real-time traffic anomaly detection. The system is designed to detect a wide range of attack types, including zero-day threats, while remaining lightweight and adaptive, thereby ensuring robust and continuous protection for next-generation IoT infrastructures.
OBJECTIVES OF THE INVENTION
[007] The primary object of the present invention is to provide a novel and efficient security system for Internet of Things (IoT) networks, which employs Kolmogorov-Arnold Networks (KANs) for the accurate and adaptive detection of anomalies in network traffic.
[008] It is another object of the invention to develop a computationally lightweight anomaly detection framework that is specifically tailored for deployment on resource-constrained IoT devices, without compromising on detection performance, accuracy, or response time.
[009] It is a further object of the invention to enable real-time monitoring and detection of both known and unknown cyber threats, including but not limited to zero-day attacks, through the integration of adaptive learning mechanisms within the KAN-based detection architecture.
[010] Another object of the invention is to overcome the limitations associated with conventional security models, such as rule-based, signature-based, and deep learning approaches, by introducing a mathematically grounded, interpretable, and generalizable detection model capable of learning complex nonlinear relationships in high-dimensional data.
[011] It is yet another object of the invention to ensure scalability and interoperability of the proposed security system across diverse IoT infrastructures, including but not limited to smart homes, industrial IoT (IIoT), healthcare systems, and smart grid environments, thereby enhancing the overall resilience and trustworthiness of IoT ecosystems.
SUMMARY OF THE INVENTION
[012] The present invention discloses an intelligent and adaptive security system for Internet of Things (IoT) networks, which utilizes Kolmogorov-Arnold Networks (KANs) for the purpose of detecting anomalies in network traffic. The system is specifically designed to address the limitations of existing security mechanisms by offering a computationally efficient, scalable, and accurate method for identifying both known and previously unseen threats in dynamic IoT environments.
[013] In accordance with one aspect of the invention, the system comprises a data acquisition and preprocessing module configured to collect, normalize, and extract relevant statistical, behavioral, and protocol-level features from network traffic generated by various IoT devices. These features are subsequently input into a Kolmogorov-Arnold Network, which leverages the Kolmogorov–Arnold representation theorem to approximate complex multivariate functions using compositions of univariate functions, thereby modeling nonlinear patterns present in high-dimensional traffic data.
[014] The KAN-based detection engine produces an anomaly score for each data instance, which is then evaluated against a predefined or dynamically adaptive threshold to determine whether the observed behavior constitutes normal operation or a potential security threat. Upon detection of an anomaly, the system is further configured to generate alerts in real-time and transmit notifications through various channels, including but not limited to APIs, email, and security dashboards.
[015] The invention further encompasses secure logging and storage of detected anomalies, support for forensic analysis, and visual representation of traffic trends and device behavior via a user interface. The system supports deployment in both edge computing and cloud environments, enabling flexible and low-latency implementation across heterogeneous IoT infrastructures.
[016] By leveraging the computational efficiency, interpretability, and generalization capabilities of Kolmogorov-Arnold Networks, the present invention provides an improved anomaly detection framework that is not only lightweight and scalable but also capable of detecting a wide spectrum of attacks, including denial-of-service (DoS), spoofing, data injection, and probing, thereby significantly enhancing the security posture of IoT ecosystems.
DETAIL DESCRIPTION OF THE INVENTION
[017] The present invention discloses an intelligent and scalable IoT security system that leverages Kolmogorov-Arnold Networks (KANs) for real-time detection of anomalies in network traffic. The system operates within a networked environment (100) comprising a plurality of IoT devices (102), including but not limited to smart sensors (102a), actuators (102b), cameras (102c), wearable devices (102d), and industrial controllers (102e). These devices communicate over a network (104), generating large volumes of heterogeneous traffic data (106), which includes parameters such as packet size, communication frequency, device IP addresses, protocol types (108), and behavioral patterns arising from the normal functioning of each device.
[018] To facilitate anomaly detection, the system comprises a data acquisition module (110), configured to monitor and collect traffic data (106) from the IoT devices (102). This acquisition may be carried out at edge gateways (112) to enable low-latency processing or at centralized cloud servers (114) for broader aggregation and analysis. The acquired data (116) is time-stamped and stored temporarily within a storage unit (118), allowing for both immediate processing and retrospective analysis.
[019] The collected network data is passed to a preprocessing unit (120), where it undergoes cleaning to remove malformed or redundant packets through a noise filtering operation (122). The cleaned data is then normalized (124), typically using statistical normalization techniques such as z-score or min-max scaling to ensure consistent input ranges. A feature extraction process (126) is performed to generate a feature vector (128) comprising key indicators such as average packet rate per second (126a), destination entropy (126b), payload length (126c), source IP frequency (126d), and device uptime (126e). These features capture the essential characteristics of IoT traffic necessary for modeling by the anomaly detection engine.
[020] The core detection component (130) employs a Kolmogorov-Arnold Network, which is grounded in the mathematical foundation that any multivariate continuous function can be represented as a superposition of univariate functions. The feature vector (128) is provided as input to an input layer (132), which distributes the individual features to multiple univariate function layers (134). These layers transform each input dimension using basis functions such as polynomials or splines. The transformed outputs are then aggregated in an aggregation layer (136) using weighted summation to approximate the complex nonlinear relationships present in the data. The output node (138) of the KAN computes an anomaly score (140), typically scaled between 0 and 1, representing the probability of deviation from normal traffic behavior.
[021] The anomaly score (140) is compared against a detection threshold (142), which may be either static or dynamically adapted based on observed baseline traffic behavior (148). If the score is below the threshold, the input is classified as normal (144); otherwise, an anomaly is flagged (146). To ensure continued relevance, the KAN model may be periodically retrained (150) using newly accumulated data, thereby enabling adaptation to changes in traffic patterns and minimization of false positives.
[022] Upon detection of an anomaly (146), an alert generation module (152) initiates the creation of a real-time alert (154). This alert includes contextual metadata such as device identifier (156), timestamp (158), computed anomaly score (140), and the type or classification of deviation (160). The alert is then transmitted via a notification system (162) that supports multiple communication channels including email (164), SMS (166), RESTful APIs (168), and integration with security dashboards (170), such as Grafana or Kibana, or custom-built interfaces.
[023] All detected anomalies and associated metadata are securely logged and stored in a repository (172), which may reside on a cloud platform (174) or on local edge infrastructure (176), depending on system architecture preferences. This storage system supports forensic operations (178), including post-attack investigation (180), identification of recurring attack patterns (182), and audit trails for validating or retraining the KAN model (184).
[024] The system further includes a user interface (186) comprising a real-time dashboard (188) designed to provide visualizations of current network activity, anomaly trends (190), heatmaps (192), device health status (194), and a historical archive of alerts (196). Administrators are provided with configuration tools (198) to adjust detection thresholds (142), filter alerts by device or anomaly type (200), and export logs for regulatory compliance and reporting purposes (202).
[025] The system architecture supports flexible deployment models, including edge-based deployment (204) for immediate on-site detection and cloud-based deployment (206) for large-scale centralized processing. The architecture is inherently scalable (208), enabling support for thousands of IoT devices (102) and is compatible with various protocols and operating systems (108). Additionally, application programming interfaces (210) facilitate seamless integration with existing security frameworks and SIEM platforms.
[026] Illustrative use cases of the invention include deployment in smart home environments (212) to detect anomalies in the behavior of connected locks, cameras, or lighting systems; industrial IoT environments (214) for monitoring the integrity of programmable logic controllers (PLCs); healthcare IoT systems (216) to identify unauthorized access attempts on patient-monitoring devices; and smart grid systems (218) for detecting injection attacks targeting energy distribution infrastructures. The invention thus provides a unified, adaptive, and lightweight solution for enhancing the security and resilience of heterogeneous IoT ecosystems.
[027] To further enhance the system's adaptability, the invention incorporates a model feedback loop (220), wherein verified anomalies are tagged and stored as part of an evolving training dataset (222). This enables the KAN-based detection engine (130) to improve over time through incremental learning. This continuous learning process ensures that the system remains responsive to emerging threats and contextual shifts in network behavior, especially relevant for environments where device behavior patterns are highly variable.
[028] The invention also provides a mechanism for threshold calibration (224), which utilizes statistical profiling of past traffic records. The profiling engine (226) generates baseline behavior models over configurable time windows, allowing the threshold value (142) to be dynamically adjusted based on observed variance and deviation margins. This feature reduces the dependency on manual threshold tuning and contributes to minimizing both false positives and false negatives in anomaly classification.
[029] An optional ensemble layer (228) may be included to combine multiple KAN instances, each specialized on a subset of feature types or device categories. This architecture supports parallel model execution and enables a hierarchical detection strategy, where the output from multiple KAN models is synthesized using a weighted voting or aggregation function (230). This design offers enhanced robustness and fault tolerance, particularly in large-scale IoT environments with diverse device types and data modalities.
[030] To ensure that resource consumption remains within acceptable limits, especially for battery-powered devices, the invention incorporates a computational load manager (232). This module monitors system metrics such as CPU usage, memory load, and battery level (234) in real time. Based on predefined policies, the system can dynamically switch between full-feature and low-power modes, wherein certain non-critical features are deferred or offloaded to edge gateways (112) or cloud nodes (114) as necessary.
[031] In embodiments where privacy is a concern, such as healthcare or home automation domains, the invention may implement data anonymization mechanisms (236) prior to preprocessing. This module ensures that personally identifiable information (PII) is masked or encrypted (238) before any analysis or storage, in accordance with data protection regulations such as GDPR or India’s Digital Personal Data Protection Act. Secure key management (240) is also integrated to protect encrypted fields and ensure auditability.
[032] For advanced analytics, the invention supports a plugin interface (242) that allows external analytics engines or domain-specific threat intelligence modules to be integrated. Through well-defined APIs (244), third-party systems can access processed traffic summaries or subscribe to anomaly event streams. This allows for a modular and extensible architecture where security tools such as firewalls, access control engines, or compliance verifiers can act upon anomaly alerts in a closed feedback loop.
[033] Another important aspect of the invention is its compatibility with multi-tenant deployments (246), wherein the security system can concurrently monitor and segregate traffic data from multiple logical or physical IoT networks. Each tenant may be assigned a dedicated instance of the KAN model (130) or share a common detection layer with tenant-specific calibration parameters. Tenant separation (248) is enforced using virtual private network segments and access control lists, ensuring strict data isolation.
[034] In industrial applications, particularly in Operational Technology (OT) networks, the invention is designed to interoperate with industrial protocols such as Modbus, DNP3, and OPC-UA (250). The feature extraction pipeline (126) is configured with protocol-specific parsers (252), enabling the KAN engine to account for command semantics and timing constraints specific to PLCs and SCADA devices. This domain-awareness significantly enhances detection accuracy and enables early identification of control-flow anomalies.
[035] In order to support long-term deployment and evolution, the system includes a lifecycle management controller (254), which facilitates remote model updates (256), firmware upgrades, and configuration management via over-the-air (OTA) mechanisms. This ensures that field-deployed systems can receive continuous improvements without the need for manual intervention or physical access. The system also supports version tracking (258) to maintain rollback capability and compliance traceability.
[036] Finally, the invention incorporates fault recovery and redundancy strategies (260) to ensure high availability in mission-critical deployments. This includes failover mechanisms (262), load balancing among multiple instances of the detection engine, and automatic reboot or restart policies in the event of runtime errors or resource exhaustion. Heartbeat signals (264) and health monitoring modules periodically verify system integrity and report to centralized dashboards, enabling proactive maintenance and rapid incident response. , Claims:We Claim:
1. An intelligent security system for detecting anomalies in Internet of Things (IoT) network traffic, the system comprising:
a. a plurality of IoT devices configured to generate network traffic data;
b. a data acquisition module configured to capture said network traffic data from said IoT devices;
c. a preprocessing module configured to clean and normalize said network traffic data and extract one or more features to generate a feature vector;
d. a Kolmogorov-Arnold Network (KAN)-based anomaly detection engine comprising:
e. an input layer to receive the feature vector,
f. a plurality of univariate function transformation layers to process each input dimension using a learnable univariate function,
g. an aggregation layer to combine outputs of the transformation layers through weighted summation, and
h. an output node configured to compute an anomaly score representing deviation from normal traffic behavior;
i. a decision logic unit configured to compare the anomaly score to a predefined or adaptive threshold and classify the traffic as normal or anomalous based on said comparison; and
j. an alert generation module configured to generate a real-time alert upon detection of an anomaly.
2. The system as claimed in claim 1, wherein the data acquisition module is positioned at an edge gateway or a cloud server for capturing inbound and outbound network traffic.
3. The system as claimed in claim 1, wherein the preprocessing module is configured to perform statistical normalization using z-score or min-max scaling.
4. The system as claimed in claim 1, wherein the extracted features include at least one of: average packet rate per second, destination entropy, payload length, source IP frequency, or device uptime.
5. The system as claimed in claim 1, wherein the Kolmogorov-Arnold Network is configured to approximate multivariate continuous functions using a composition of univariate functions in accordance with the Kolmogorov–Arnold representation theorem.
6. The system as claimed in claim 1, wherein the threshold used for anomaly classification is dynamically adjusted based on historical traffic statistics or baseline profiling.
7. The system as claimed in claim 1, wherein the alert generation module is configured to transmit alerts through at least one of: email, SMS, RESTful API, or integration with a security dashboard.
8. The system as claimed in claim 1, further comprising a secure storage module configured to log anomaly metadata, alert history, and model outputs for forensic analysis and retraining.
9. The system as claimed in claim 1, wherein the Kolmogorov-Arnold Network is retrained periodically using newly acquired traffic data to adapt to evolving network behavior and minimize false positives.
10. The system as claimed in claim 1, wherein the system is deployed in one or more IoT environments selected from: smart homes, industrial IoT, healthcare IoT, and smart grid infrastructure.