Threat detection system using graph-based behavioral fingerprints and distributed ledger validation

Abstract

A threat detection system in a distributed computing environment, consisting of: a processing unit for creating behavior graphs, configured to receive a continuous stream of event data associated with a variety of digital entities such as users, processes, devices, and services; wherein the processing unit for constructing behavioral graphs creates dynamic directed labeled graphs (DDLGs), each DDLG comprising nodes representing discrete behavioral events and edges representing temporal or causal relationships between these events; a fingerprinting module that is operationally coupled with the processing unit for the construction of behavior graphs and is configured to extract canonical subgraphs from the aforementioned DDLGs using temporal sliding windows and to apply graph normalization operations to generate a behavioral fingerprint that corresponds to the most recent activity of the digital entity; a cryptographic hash processor configured to compute a hash digest of the said behavioral fingerprint, the hash digest being digitally signed with a hardware-secured private key; a distributed ledger interface unit configured to transmit the aforementioned signed hash digest to a distributed ledger network via a consensus-based smart contract, so that the behavioral fingerprint is immutably anchored and timestamped; an anomaly detection control unit configured to receive a live DDLG associated with a monitored digital entity and compare it to one or more previously anchored behavioral fingerprints using graph isomorphism or graph editing distance metrics to generate a threat anomaly score; and A response controller configured to trigger a warning or containment action when the threat deviation value exceeds a predefined risk threshold.

Description

Field of invention

[0001] The present invention relates to the field of cybersecurity and network integrity monitoring. More specifically, it relates to a system and a method for detecting security threats by means of graph-based behavioral analysis of digital entities and for validating these analyses via a distributed ledger infrastructure in order to ensure tamper-proof and decentralized trust transfer in centralized and decentralized environments. background

[0002] The rapidly evolving landscape of cyber threats demands advanced and adaptive threat detection mechanisms. Traditional signature-based methods often fail to detect zero-day attacks, lateral movement, and sophisticated social engineering. Behavioral analytics has proven to be a more robust solution, as it analyzes the context and patterns of system and user behavior. However, behavioral patterns are often difficult to capture, model, and consistently verify across distributed systems. Furthermore, the lack of a unified, tamper-proof register of threat data undermines the trustworthiness of detection data shared between heterogeneous systems. There is a need for a system that generates verifiable, context-rich behavioral patterns and securely distributes and validates them across domains to enable early threat detection and mitigation.

[0003] The field of cybersecurity has evolved rapidly in recent decades due to the increasing complexity, scale, and prevalence of modern digital systems. From early intrusion detection systems (IDS) that relied heavily on static signature databases to today's AI-powered threat intelligence platforms, this evolution has been both necessary and a response to the rapidly changing threat landscape. Traditional solutions focused primarily on static, rule-based mechanisms and blacklisting approaches. While signature-based systems like Snort and Suricata are still widely used in enterprise environments to detect known threats, their reliance on predefined attack signatures renders them ineffective against zero-day exploits, polymorphic malware, or novel lateral movement strategies employed by sophisticated attackers.These systems are also vulnerable to circumvention techniques such as obfuscation, encryption, and delayed payload activation, rendering static patterns obsolete within minutes of an attack variant being published.

[0004] To overcome the limitations of signature-based systems, behavioral analytics tools have been introduced. These solutions monitor basic user, system, and network activity and generate alerts as soon as deviations from expected behavior are detected. User and entity behavior analysis (UEBA) platforms such as Splunk UEBA or Vectra AI use machine learning models to create behavioral patterns and flag anomalous activity. However, most of these systems have high false positive rates due to the inherent variability of human behavior and legitimate system updates. Their statistical models often fail to capture the causality and sequential dependencies between actions, which are crucial for distinguishing truly malicious behavior from unusual but harmless activity.Furthermore, behavioral detection systems often operate in isolation, lacking a unified framework for sharing or validating behavioral insights across different domains. This reduces their effectiveness in identifying distributed or coordinated attacks.

[0005] Another widespread approach is the use of Endpoint Detection and Response (EDR) systems and Security Information and Event Management (SIEM) platforms. While these offer centralized log aggregation and threat correlation, they rely on linear temporal event sequences and simple correlation heuristics. These can be easily circumvented by attackers using nonlinear or multi-stage attack vectors. Furthermore, these platforms typically operate within organizational boundaries and lack an interoperable or consensus-based mechanism for validating or sharing threat intelligence across different organizations. This results in delayed detection and fragmented threat visibility in environments where attackers often operate along supply chains or within federated infrastructures.

[0006] Graph-based analytics has established itself as a promising technique in recent years. Several research and commercial tools already utilize graph theory to model system and user behavior. Graphs offer a powerful abstraction for capturing the structural and contextual relationships between events, entities, and processes, thus enabling more precise modeling of complex behaviors and threat patterns. However, they lack the ability to perform efficient, real-time graph comparisons, mappings, and fingerprinting at the scale required for enterprise-wide or cross-organizational implementations.

[0007] In parallel, the use of blockchain and distributed ledger technology (DLT) in cybersecurity has become increasingly important, as it enables decentralized trust, traceability, and tamper-proof logging. Various solutions propose using blockchain for the secure logging of security events, identity management, and threat intelligence sharing. For example, Guardtime's KSI blockchain and the IBM Blockchain Security Framework aim to validate the integrity of logs and configurations. Other frameworks leverage blockchain to develop decentralized authentication and access control systems. Despite their innovation, these solutions often focus on static data (e.g., logs, certificates) rather than dynamic behavioral patterns. They offer neither real-time threat detection nor integration with behavioral modeling engines.Furthermore, the use of blockchain leads to latency and scaling issues, particularly in public ledger configurations where consensus mechanisms like Proof-of-Work or Proof-of-Stake delay transaction finality. This renders such solutions unsuitable for high-threat scenarios without careful architectural planning.

[0008] Federated learning and collaborative threat analysis platforms have been introduced as potential solutions to the lack of cross-domain learning and validation. In such systems, individual organizations train local threat models and share their model updates or threat indicators with a central aggregator or with each other. While these systems are theoretically promising, in practice they encounter limitations such as data heterogeneity, inconsistent feature spaces, privacy concerns, and a lack of standardization. Furthermore, the trustworthiness of shared models or indicators remains a challenge, as there is no cryptographic or consensus-based mechanism to ensure the origin, accuracy, or integrity of the shared data.The lack of verifiable threat fingerprinting potentially leads to the proliferation of false alarms, data poisoning attacks, and a loss of trust in shared threat analysis systems.

[0009] Furthermore, most existing solutions lack traceability or provenance tracking for threat detection decisions. The black-box nature of many AI-powered detection platforms poses regulatory and operational risks, particularly in critical sectors such as finance, healthcare, and government. Analysts need the ability to understand the logic behind an alert, comprehend the behavior of suspicious entities, and verify the historical validation of threat signatures. Traditional solutions lack the tools to reconstruct behavior in a way that is both understandable and cryptographically verifiable. This significantly hinders post-incident analysis, forensic investigations, and compliance reporting.

[0010] In summary, while current cybersecurity frameworks offer useful components—such as signature-based intrusion detection systems (IDS), behavioral analysis platforms, graph-based threat modeling, blockchain for tamper-proof logging, or federated learning for collective intelligence—none of these solutions fully integrate real-time behavioral detection with decentralized validation and traceable provenance. The gap between dynamic behavioral modeling and immutable trust infrastructures remains critical.As cyber threats become increasingly distributed, polymorphic, and driven by social engineering, a new class of systems is needed—one that can model complex behavioral patterns in real time, encode them as graph-based fingerprints, validate them using consensus-based distributed ledgers, and present the results in a fully explainable and forensically verifiable manner. The absence of such a unified architecture leaves modern digital infrastructures vulnerable to sophisticated attackers and systemic detection errors. Summary of the invention

[0011] The present invention describes a system and a method for threat detection using graph-based behavioral analysis of digital entities—including users, applications, and devices—within a network. Each behavioral fingerprint is modeled as a dynamic, labeled graph that reflects the sequence, frequency, and dependencies of the actions, events, or transactions performed by the entity. These graph fingerprints are periodically hashed and stored in a distributed ledger (e.g., blockchain) to provide a tamper-proof, traceable, and decentralized verification framework. The system also includes a real-time anomaly detection unit that compares current graph fingerprints with validated historical behavioral templates using subgraph isomorphism and graph editing distance. Threat scores are calculated and assigned to each entity.Warning messages are generated when predefined risk thresholds are exceeded.

[0012] The present invention aims to provide a novel and robust threat detection system and method that overcomes the limitations of existing solutions by utilizing graph-based behavioral fingerprints in conjunction with distributed ledger validation. The invention enables the context-aware, real-time identification of anomalous or malicious activity by modeling the behavior of digital entities—such as users, devices, applications, and services—as dynamic graph structures that capture the semantic and temporal relationships between actions or events. A further objective of the invention is to ensure the integrity, traceability, and trustworthiness of these behavioral fingerprints by anchoring their cryptographic hash values ​​in a distributed ledger.This establishes a tamper-proof and decentralized validation mechanism that supports the secure exchange of threat information across organizational boundaries.

[0013] Another objective of the invention is the early detection of threats, particularly novel or polymorphic attacks. This is achieved by comparing subgraphs of the behavior of active entities with previously validated, benign or malicious graph templates stored in a behavioral fingerprint library. This allows the system to detect deviations in complex behavioral sequences that cannot be identified using conventional linear or statistical models. A further objective is the reduction of false alarms and the improvement of detection accuracy through the use of graph-theoretical metrics and machine learning models trained on topological features of behavioral graphs, rather than relying on static thresholds or signatures.

[0014] Another objective is to provide real-time feedback and incident response capabilities through the tight integration of the detection system with automated triggering mechanisms. These mechanisms can initiate containment measures, generate alerts, or trigger forensic workflows. The invention also aims to improve the traceability and forensic accountability of threat detection decisions by enabling analysts to visualize behavioral graphs, examine anomaly points, and verify the origin of each decision using cryptographically verifiable ledger entries. Furthermore, the invention supports both centralized and federated deployment topologies, thus ensuring scalability and adaptability to a wide range of enterprise, cloud, and edge computing environments.

[0015] The invention aims to promote collaborative threat analysis without compromising data privacy. This is achieved through privacy-compliant ledger anchoring and federated updates of behavioral models. The system is intended to address the current lack of trust and interoperability in common cybersecurity frameworks by establishing a consensus-based infrastructure that validates not only threat data but also its behavioral context. Overall, the invention aims to create a next-generation cybersecurity framework that is robust, adaptable, auditable, and deployable in real time to defend against evolving and distributed cyber threats. BRIEF DESCRIPTION OF THE IMAGE

[0016] 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 represent the same parts: Fig. Figure 1 shows a block diagram of a threat detection system using graph-based behavioral fingerprints and distributed ledger validation.

[0017] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0018] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0019] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation of it.

[0020] References to “an aspect”, “another aspect”, or similar phrases in this description 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, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0021] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0023] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0024] Fig.Figure 1 shows a block diagram of a threat detection system using graph-based behavioral analysis and distributed ledger validation. The system 100 comprises: a behavior graph generation unit (102) that receives a continuous data stream of events associated with multiple digital entities such as users, processes, devices, and services; the behavior graph generation unit creates dynamic directed labeled graphs (DDLGs) whose nodes represent discrete behavioral events and whose edges represent temporal or causal relationships between these events; a fingerprinting module (104) connected to the behavior graph generation unit that uses sliding time windows to extract canonical subgraphs from the DDLGs and applies graph normalization operations to generate a behavioral fingerprint corresponding to the recent activity of the digital entity;a cryptographic hash processor (106) that computes a hash value of the behavioral fingerprint and digitally signs it with a hardware-secured private key; a distributed ledger interface (108) configured to transmit the signed hash digest to a network of distributed ledgers via a consensus-based smart contract, thus immutably anchoring and timestamping the behavioral fingerprint; an anomaly detection controller unit (110) configured to receive a live DDLG associated with a monitored digital entity and compare it to one or more previously anchored behavioral fingerprints using graph isomorphism or graph editing distance metrics to generate a threat anomaly score;and a response controller (112) configured to trigger an alert or containment action when the threat deviation value exceeds a predefined risk threshold.

[0025] In one embodiment, the processing unit for constructing behavior graphs (102) further comprises a real-time graph parser configured to process heterogeneous event data formats such as syslog messages, process execution traces, API call sequences, network telemetry and access logs, and to map these events into a unified node schema using context-sensitive tagging techniques based on protocol type, user ID, process ID, timestamp granularity and semantic operation class.

[0026] Subgraphs employ a recursive neighborhood coding technique, where for each node a node signature vector is computed as a function of both its local attributes and the compressed adjacency matrices of its k-hop neighborhood, thus preserving structural and contextual uniqueness across time-varying behaviors.

[0027] In one embodiment, the cryptographic hash processor (106) is implemented in a tamper-proof hardware security module (HSM), wherein the HSM supports key rotation, secure key storage and logging of all signing operations in accordance with the FIPS 140-2 Level 3 standards.

[0028] In one embodiment, the distributed ledger interface unit (108) is configured to communicate with a permissioned blockchain network, with the smart contract enforcing verification logic that rejects behavioral fingerprint hashes if they fail schema validation, do not contain a valid timestamp nonce, or exhibit repetitions based on previously anchored digests.

[0029] In one embodiment, the anomaly detection control unit (110) includes a hybrid matching model that combines subgraph isomorphism detection with an entropy-weighted edit distance calculation, wherein the edit distance is normalized with respect to both the number of edge mutations and the semantic meaning of node label changes, so that rare but effective behavioral deviations are prioritized in the evaluation.

[0030] In one embodiment, the response controller (112) further comprises an update module with programmable containment logic, wherein this logic is able to issue automatic policy updates to firewall rules, identity and access management configurations, container orchestration services or endpoint isolation protocols upon receiving a threat deviation value above the containment threshold.

[0031] In one embodiment, each DDLG is encoded and stored in a compressed graph format using a dynamic variable-length encoding scheme and indexed using a multidimensional hash tree, so that the retrieval and comparison of past behavioral fingerprints is computationally optimized for a sublinear search time over time series intervals.

[0032] In one embodiment, the smart contract executed on the distributed ledger includes provisions for staking-based reputation metrics, whereby validation nodes are rewarded or penalized based on their past accuracy in validating behavioral fingerprints, and nodes with repeated incorrect validations are temporarily excluded from the ledger consensus pool.

[0033] In one embodiment, temporal alignment constraints are additionally taken into account when comparing behavioral fingerprints, with deviation values ​​being adjusted based on the relative timing of the observed behaviors, so that temporally shifted legitimate operations are not falsely classified as anomalies when their topological and semantic signatures match previously anchored patterns.

[0034] The system operates by continuously capturing diverse event telemetry data streams from a distributed computing environment, including application logs, network monitoring agent logs, system call logs, authentication records, access logs, and kernel audit data. These raw data streams are unified and preprocessed using a context-aware event normalization pipeline that resolves discrepancies in timestamp formats, semantic labels, and source granularity. Each telemetry input is assigned a unique source identifier, and overlapping or conflicting events are distinguished using a probabilistic causality model that ranks events based on their credibility and temporal resolution.

[0035] After data acquisition, the system creates a dynamic directed labeled graph (DDLG) for each monitored digital entity. Graph creation is event-driven and occurs in real time. Each node represents a specific, discrete event, tagged with metadata such as user ID, event type, command string, process ID, and timestamp. Directed edges between nodes are based on two dimensions: causality and temporal sequence. If event A directly leads to or triggers event B, a strong causal edge is created. Temporally successive events within a permissible time window are linked by weaker temporal edges. Each graph grows continuously but is segmented by a sliding time-window mechanism to extract subgraphs representing behavioral segments.The window size is dynamically adjusted based on the entropy of the most recent activity - more active entities generate denser graphs and trigger more frequent subgraph extraction.

[0036] Once the behavioral fingerprint is generated, it is signed with a private key stored in a tamper-proof hardware security module (HSM). This ensures the integrity and origin of the fingerprint. The signed fingerprint, along with metadata such as timestamps, entity ID, and classification label (if available), is transmitted to a distributed ledger network. The ledger can be operated with or without permissions. Smart contracts deployed on the network are responsible for validating incoming fingerprint records. The smart contract checks the structural integrity of the data, the consistency of the timestamps, the validity of the key, and replay resistance. After verification, the fingerprint is immutably stored in the ledger with an anchor transaction hash, which can be queried for verification at a later time.

[0037] In parallel, the system maintains a real-time behavioral model for each active entity. Based on ongoing event telemetry data, a live DDLG (Graph Edit Distance) is built and compared to previously anchored, inconspicuous fingerprints using two main techniques: subgraph isomorphism and graph edit distance. Subgraph isomorphism checks whether the current behavior structurally and semantically matches a known fingerprint, while graph edit distance measures the degree of deviation by quantifying the insertions, deletions, or replacements required to transform the current behavior into a known template. The system asymmetrically weights different types of edits, with the deletion of a safety-critical edge (e.g., an attempted escalation of privilege) resulting in a higher penalty than inconspicuous label changes (e.g., changing the process name due to a software update).Additionally, time-matching restrictions are enforced to exclude legitimate, time-shifted activities that would otherwise lead to false alarms.

[0038] The results of this comparison feed into a threat assessment module that generates a composite anomaly score. This score considers multiple dimensions, including structural variance, temporal divergence, and semantic outlier features. The system utilizes and applies a high-dimensional behavioral space. The response controller can execute a range of predefined or dynamically configured actions, such as isolating the endpoint from the network, initiating a credential reset workflow, blocking specific process execution trees, or escalating the incident to a human analyst. Simultaneously, the anomaly and its behavioral context are logged and cryptographically tied to the anchor in the ledger, enabling full post-incident traceability.Analysts can visualize the anomaly using a graph difference viewer that overlays the live DDLG with its associated, unproblematic anchor and highlights node or edge mutations using color-coded risk labels. Each anomaly report includes a verifiable chain of evidence that links the current detection to its historical fingerprint and ledger anchor.

[0039] In advanced implementations, the system includes a federated learning layer where behavioral models are encrypted and shared between multiple organizations. Each organization's system generates local anomaly vectors and updates its threat detection model locally. These model updates are aggregated using a secure, enclave-based federated averaging process. This ensures that raw event telemetry data is never exposed while enabling collaborative learning. The federated system also supports model rollback and cross-validation by checking the immutable fingerprint history in the distributed ledger to determine whether new model updates affect detection accuracy.

[0040] To further enhance data privacy, the system supports zero-knowledge proof anchoring (ZKP). This involves supplementing the hash of a behavioral fingerprint with a zk-SNARK, which proves that the fingerprint fulfills certain structural properties (e.g., a maximum of three privilege escalations, no unauthorized outbound network spikes) without revealing the actual graph topology or content. This feature enables the secure validation of behavioral patterns in semi-trusted or hostile networks without the risk of data leakage.

[0041] Overall, the invention represents a novel synthesis of advanced graph theory, cryptographic anchoring, machine learning, and distributed systems engineering. By transforming complex and changing behaviors into verifiable graph fingerprints and linking them to immutable, ledger-based validation, the invention offers a robust, traceable, and tamper-proof framework for the real-time detection and defense against modern cyber threats across diverse and distributed digital infrastructures.

[0042] The proposed system comprises a graph-based behavioral profiling module, a distributed ledger interface, an anomaly detection control unit, a consensus and validation layer, and a communication and actuator subsystem. These modules can be deployed in a single integrated hardware unit or distributed across a cloud-based or edge-oriented deployment topology. The system is implemented through a combination of software-defined components, FPGA-based graph processing accelerators, and cryptographic hardware security modules (HSMs) for managing ledger interactions.

[0043] In one embodiment, the system is configured as a Graph-Encoded Threat Detection Appliance (GETDA) and consists of a robust hardware enclosure mounted in a rack-based server infrastructure. The enclosure includes a multi-core CPU, high-speed RAM, a graph coprocessor for real-time modeling of behavioral patterns, a secure enclave for cryptographic operations, and a tamper-proof interface for ledger nodes. The GETDA device is integrated at the network edge and continuously monitors packet streams, system logs, API calls, and event telemetry. A real-time graph constructor interprets event sequences as multi-layered directed graphs, with nodes representing atomic actions and edges representing causal or temporal dependencies. Each node is populated with contextual metadata (e.g.,The data is tagged with timestamps, origin, protocol, and user ID, and subgraph patterns are extracted using sliding time windows to create behavioral fingerprints.

[0044] These behavioral patterns are regularly stored in a distributed ledger jointly maintained by multiple detection nodes. Each fingerprint hash is signed with the local HSM and passed to the ledger via smart contracts that ensure schema integrity, timestamps, and consensus rules. Fingerprints of harmless entities form a growing graph fingerprint library (GFL), while fingerprints of detected threats are tagged with threat classifications (e.g., ransomware, phishing, DDoS) and shared between trusted nodes.

[0045] The anomaly detection unit performs a real-time graph comparison between live-stream fingerprints and the GFL (Global Field Map). This involves using approximate graph matching techniques and entropy-based deviation metrics. For each deviation, a threat confidence score is calculated using a hybrid scoring function that incorporates statistical, topological, and contextual factors. If the score exceeds a system-defined threshold, the communication and action subsystem generates alerts, initiates automated containment protocols (e.g., endpoint quarantine, session termination), and updates the distributed ledger with the anomaly report.

[0046] The system also includes an intuitive threat analysis dashboard where security analysts can visualize behavioral graphs, investigate anomaly points, and review cryptographic evidence that links threat assessments to validated fingerprint history. The dashboard further supports forward and backward provenance analysis, enabling the tracking of infection vectors and lateral spread within the organizational network.

[0047] The distributed ledger's interface supports both permission-based and permissionless configurations. In permission-based mode, fingerprint validation is controlled by a consortium of pre-approved validation nodes operating under the Byzantine fault-tolerant (BFT) consensus mechanism. In permissionless mode, the system interacts with public blockchains using zero-knowledge proofs to maintain confidentiality while ensuring auditability.

[0048] The system is implemented through a coordinated arrangement of physical, hardware-based components within a distributed computing infrastructure, thus fulfilling the system capability requirements. The processing unit for creating behavioral graphs comprises one or more physical processors, memory circuits, and network interface controllers. These are configured to receive and buffer event telemetry via dedicated communication interfaces and to execute graph construction instructions stored in non-volatile, machine-readable storage media. The fingerprinting module is implemented as a processor-coupled circuit that includes a high-speed cache and data buses, which perform subgraph extraction and normalization operations at the hardware level.The cryptographic hash processor consists of a secure processing element with embedded cryptographic acceleration circuitry and a hardware-protected key storage area for generating digital signatures. The distributed ledger interface includes network transceiver hardware and protocol processing circuitry configured to communicate with external ledger nodes via authenticated communication channels. The anomaly detection controller unit is implemented with computational hardware configured to execute graph comparison algorithms. These are supported by dedicated arithmetic logic circuits and memory addressing units for storing and retrieving large graphs.The response controller includes hardware-based actuator interfaces that are electrically coupled to network security devices, identity management systems, and endpoint control subsystems to trigger containment signals when a threshold is exceeded.

[0049] The drawing and the preceding description illustrate 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. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

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