Neuro-Generative Adversarial System for real-time detection and combating of malware morphing in high-density edge networks

Abstract

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

Description

Technical field

[0001] This disclosure relates to cybersecurity systems and devices for networked computing, in particular a neurogenerative adversarial architecture and associated edge machine devices for real-time detection of morphing malware and automated countermeasures in high-density edge networks. background

[0002] Modern malware increasingly uses morphing techniques—polymorphism, metamorphism, code obfuscation, packing, self-modifying binaries, and dynamic payload composition—to evade signature and static heuristic detectors. In high-density edge networks (e.g., dense IoT deployments, cellular base station clusters, edge networks of industrial control systems, and content delivery micronodes), data traffic is heavy, device resources are limited, and detection latency must be minimal to prevent lateral spread. Traditional centralized malware analysis (sandboxing, signature updates, offline deep analysis) is too slow and often impractical due to bandwidth, privacy, and availability constraints. Existing machine learning detectors trained on static datasets suffer from concept drift and cannot generalize to malware modified by attacks.Generative approaches have been proposed for training adversary systems. However, current methods typically focus on offline generation of attack examples rather than an integrated real-time system. This system generates plausible morphing variations and continuously uses them to harden a detector in the field while simultaneously coordinating immediate countermeasures through hardware-based enforcement. There remains a need for a device and system architecture that operates at the network edge, learns from streaming telemetry, anticipates morphing transformations, and automates defensible countermeasures with demonstrable latency and resource constraints.

[0003] The evolution of modern malware has transformed from simple, static binaries to highly adaptive, self-modifying, and polymorphic entities capable of evading even the most sophisticated detection systems. Traditional cybersecurity relied heavily on static, signature-based methods. Binary patterns, hashes, or code fragments characteristic of known malware samples were extracted and stored in antivirus vendors' databases. Upon the emergence of a new executable or network payload, detection systems would perform a signature comparison to determine if the code exhibited known malicious characteristics. While effective during the early stages of malware proliferation in the 1990s and early 2000s, this model was quickly rendered obsolete by the advent of polymorphic malware.Polymorphic malware alters its surface structure with each infection—for example, by encrypting its payload with different keys, mixing instructions, or inserting harmless operations—thus rendering conventional signatures useless. Even minor variations in binary sequences invalidate exactly matching signatures, leading to massive false positives and forcing defenders to constantly update signature databases.

[0004] To overcome the limitations of signature-based methods, heuristic and behavior-based detection techniques were developed. Heuristic analysis identifies patterns in the code structure, such as suspicious API calls, obfuscated strings, or self-modifying instructions. While this improved the detection of variants of known malware, heuristic methods still relied on manually created rules, limiting their scalability and adaptability. Behavior-based detection, on the other hand, monitors program execution in sandbox environments or through dynamic tracing on endpoints. These systems observe the runtime behavior of unknown binaries—including file system modifications, network communication, process startup, and registry changes—to infer malicious intent.Although sandbox-based solutions are conceptually superior to static detection, they face two major challenges: high latency and a lack of environmental awareness. Morphing malware can detect the presence of a sandbox through timing checks, artifact detection, or restricted system calls, and modify its behavior to appear harmless, effectively evading analysis. Furthermore, sandboxing incurs significant computational overhead, making it impractical in resource-constrained or high-throughput environments such as edge networks, where thousands of data flows and processes occur simultaneously.

[0005] The increasing sophistication of malware exacerbates these problems. Morphing malware families now utilize machine learning themselves, employing reinforcement learning to optimize their evasion strategies and generative techniques to continuously mutate their payloads in response to observed defenses. For example, self-evolving worms and ransomware use dynamic code generation, modifying command sequences at runtime, re-encrypting themselves with new keys, and communicating over covert channels using randomized protocol layers. These tactics ensure that each new infection appears unique at both the binary and behavioral levels, thus circumventing pattern recognition and static analysis.Without a defensive system capable of learning and adapting at the same pace as the attacker - ideally within the same opposing framework - the defenders are constantly one step behind.

[0006] The current malware detection landscape is characterized by a tension between adaptability and practicality. Signature-based, heuristic, and static learning methods are outdated; sandboxing and dynamic analysis are too slow and resource-intensive; deep learning improves accuracy but not resilience; and cloud-centric or federated architectures either incur latency or compromise data privacy. Such a system must integrate generative and discriminative intelligence in a closed-loop system, operate autonomously on resource-constrained edge hardware, enforce security measures without human intervention, and maintain data privacy through local data processing.The absence of such integrated, neurogenerative systems in the current state of the art underlines the urgent need for a new class of cybersecurity devices specifically designed for real-time detection and mitigation of malware morphing in high-density edge networks. Summary

[0007] The invention provides a Neuro-Generative Adversarial System (NGAS) and a corresponding edge device architecture that together train a lightweight generative adversarial network and a detection network based on streaming network and endpoint telemetry, thus enabling continuous adversarial hardening and rapid countermeasures against evolving malware.

[0008] The main objective of the present invention is to provide a neurogenerative adversarial system and an associated Edge Neuro-Defender Appliance (ENDA) that overcomes the limitations of existing malware detection and defense systems by enabling real-time detection, interpretation, and mitigation of evolving malware in high-density edge network environments. The invention aims to achieve this by combining adversarial deep learning, real-time behavior synthesis, and adaptive decision intelligence in a closed architecture that continuously improves itself through the analysis of constantly changing network traffic.By using both generative and discriminative neural mechanisms, the system not only recognizes known and unknown malware variants, but also anticipates potential evasion strategies by actively generating synthetic variants and retraining its detection limits in situ.

[0009] Another important goal of the invention is to provide an autonomous, low-latency malware defense mechanism that operates directly at the network edge without relying on central cloud resources. This ensures a response time of less than one millisecond and uninterrupted operations even in the event of bandwidth limitations, temporary connection interruptions, or a lack of connection to central security hubs. The system thus mitigates the inherent disadvantages of cloud-based malware analysis platforms with regard to latency and data privacy, while simultaneously enabling the same analytical performance through locally embedded, edge-computing-optimized learning loops for attackers.

[0010] Another objective of the invention is to provide a robust malware detection system capable of withstanding attacks that exploit the vulnerabilities of conventional machine learning models. The invention's neurogenerative framework enables the detection network to be continuously hardened against hostile adaptations. This is achieved through active competition between a generator network, which synthesizes plausible, difficult-to-detect malware variants, and a detection network, which adapts to combat them. This mutual evolution ensures robustness against gradient-based evasion, byte-level obfuscation, API call permutation, and dynamic code mutation, thus maintaining high detection accuracy even with constant threat evolution.

[0011] Another objective of the invention is to enable on-device learning and self-adaptation using computationally efficient, hardware-accelerated mechanisms suitable for resource-constrained edge devices. Through model distillation, quantization, and pruning, the large neural architectures used in cloud-based training environments are transformed into compact, inference-optimized versions suitable for on-device operation. The ENDA hardware includes specialized neural accelerators and FPGA-based computational pipelines that support concurrent generative and discriminative processing without overburdening general-purpose CPUs. This design ensures that even small edge gateways or IoT hubs can provide intelligent malware detection and prevention capabilities without compromising their primary networking or control functions.

[0012] Another objective of the invention is the integration of explainable artificial intelligence (XAI) mechanisms. The system integrates malware detection to provide both human operators and automated monitoring systems with traceable and auditable results. It maps detections to specific behavioral characteristics, code segments, or network flows, thus enabling transparent justification for each monitoring action. This not only supports accountability and regulatory compliance but also facilitates forensic analysis after an incident and policy optimization. By making the decision-making process transparent directly at the network edge, operators gain immediate insight into the nature and origin of the detected threat and can therefore respond quickly and effectively to incidents.

[0013] A further objective of the invention is to provide a hardware-based, tamper-proof protection mechanism capable of autonomously isolating or neutralizing detected threats in real time. The ENDA device integrates hardware-based protection functions such as programmable network interface controllers for selective packet filtering, microsegmentation rules for isolating compromised nodes, and secure endpoint orchestration for halting malicious processes. These protection mechanisms are executed based on policy constraints derived from detection reliability and system risk tolerance, thus ensuring proportionate and controlled defense measures. The physical and cryptographic tamper resistance of the device ensures that neither malware nor unauthorized users can disable, reprogram, or undermine the protection mechanisms.

[0014] Another important goal of the invention is to enable privacy-compliant learning and secure model updates in distributed edge environments. The system continuously learns from streaming telemetry data while ensuring that sensitive or identifiable data never leaves the local device unencrypted. Model updates are distributed as differential, signed patches and verified in secure enclaves to prevent supply chain attacks or the injection of malicious firmware. When coordination of multiple ENDA devices is required, the invention utilizes privacy-compliant aggregation and federated learning protocols. This enhances global intelligence without compromising local privacy. This feature makes the invention suitable for use in regulated sectors such as healthcare, finance, and critical infrastructure.

[0015] Another objective of the invention is the anticipation and simulation of potential future malware morphologies before they appear in practice. The system's generative component learns transformation patterns and functional equivalence relationships between malware variants and can thus extrapolate possible future mutations of known threats. By feeding these synthetic morphologies into the training process, the system proactively strengthens its detection model against variants that may not yet be present in real-world attack datasets. This predictive capability transforms malware defense from a reactive to a preventive approach, thereby significantly reducing the window of opportunity for exploiting vulnerabilities.

[0016] Another objective of the invention is to achieve operational scalability in diverse and high-density edge network environments where thousands of devices communicate simultaneously. The system architecture supports the parallel processing of telemetry data from multiple data streams, as well as load-balanced adversarial learning tasks, while maintaining limited latency. The ENDA device can operate in both inline and passive monitoring modes and dynamically adapts to network criticality and throughput requirements. The same hardware can be clustered for cooperative defense, forming a distributed neurogenerative defense network that exchanges anonymized information while operating autonomously. This scalability ensures applicability in a wide range of deployment contexts, including industrial automation networks, smart city networks, and mobile edge clouds.

[0017] A further objective of the invention is to maintain seamless verifiability of model integrity and adherence to guidelines throughout the entire system lifecycle. All model updates, attacker training runs, enforcement actions, and telemetry data are cryptographically signed and stored in an immutable register within the secure environment of the ENDA device. This enables complete traceability of system behavior and allows verification that detection and enforcement decisions are based on authentic, unaltered model states. This verifiability strengthens confidence in autonomous defense systems and supports compliance audits, forensic investigations, and chain of custody in cybersecurity operations.

[0018] Another objective of the invention is to reduce reliance on manual threat analysis and human intervention by introducing self-optimizing defense intelligence at the device level. The neurogenerative system autonomously updates its detection thresholds, trains on emerging patterns, and adapts enforcement policies through reinforcement learning based on operational metrics such as false alarm rates, latency impact, and service continuity. By integrating this adaptive intelligence into each ENDA unit, the invention minimizes the need for human operators to continuously adjust detection parameters or analyze false alarms, thereby improving the overall efficiency and resilience of the system.

[0019] Another objective of the invention is to bridge the gap between academic research in the field of adversarial learning and practically deployable cybersecurity infrastructure. Most existing research prototypes are limited to simulation environments and exhibit shortcomings in the hardware-software co-design necessary for field deployment. The present invention comprises a production-ready architecture that tightly integrates generative adversarial techniques with purpose-built hardware optimized for real-time inference and enforcement. Thanks to its modular and scalable design, the system enables integration into existing network topologies and supports phased deployment without requiring a complete infrastructure overhaul.

[0020] Ultimately, one goal of the invention is to establish a unified neuro-adversarial defense paradigm that evolves symmetrically with the attacker's intelligence, thus ensuring continuous security even when malware itself is AI-driven. By continuously participating in an adversarial feedback cycle between generation and detection, the system effectively mirrors the adaptive strategies of malicious AI systems, thereby neutralizing their evolutionary advantage. This establishes a continuously learning, self-defending cybersecurity architecture suitable for next-generation networks where the threat landscape evolves dynamically, unpredictably, and autonomously.

[0021] With these goals, the invention pursues a revolutionary approach to malware defense, combining neuroscience-inspired learning architectures, adversarial modeling, and hardware-based enforcement in a coherent, autonomous, and traceable security framework. The combination of these features establishes a new class of defense systems capable of detecting and counteracting evolving malware in high-density edge networks in real time. This represents a significant advancement over existing reactive and static cybersecurity paradigms. BRIEF DESCRIPTION OF THE IMAGE

[0022] 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 Neuro-Generative Adversarial System for real-time malware morphing detection and countermeasures in high-density edge networks.

[0023] 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

[0024] 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.

[0025] 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 thereof.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0030] Fig.Figure 1 shows a block diagram of a neurogenerative adversarial system for real-time malware morphing detection and mitigation in high-density edge networks. The system 100 comprises: a data acquisition unit (102) that receives, normalizes, and encodes multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs. The data acquisition unit also computes feature embeddings over sliding time windows and applies privacy-compliant redaction before storage. A generative neural processor (104) is operationally coupled to the data acquisition unit and generates synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations while preserving semantic functionality.while varying structural and behavioral features; a discriminative neural processor (106) that has been trained adversarially with the generative neural processor and is configured to detect morphing malware by evaluating a probability distribution across multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor (108) that is communicatively connected to both the generative and discriminative neural processors and is configured to orchestrate the adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negatives, and trigger countermeasures when a detection confidence level exceeds a predefined adaptive threshold; a secure inference and enforcement unit (110) integrated into the system and configured tothat it performs low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordination processor; and a hardware-embedded security enclave (112) embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity acknowledgment data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

[0031] In one embodiment, the data acquisition unit (102) comprises a multi-core telemetry preprocessor and a feature extraction processor. The multi-core telemetry preprocessor is configured to perform time-synchronized sampling of packets and processes across heterogeneous devices within the edge network. The feature extraction processor is configured to generate behavior vectors containing metrics such as entropy, opcode frequency, execution time, arrival variance between packets, and API call graph density. Each of these metrics is computed in real time using memory-constrained incremental methods.

[0032] In one embodiment, the generative neural processor (104) is implemented as a conditional deep generative architecture comprising a sequence synthesis subprocessor and a graph morphing subprocessor. The sequence synthesis subprocessor is configured to construct malware byte sequences and dynamic API call chains using a gated recurrent architecture with context-sensitive attention layers. The graph morphing subprocessor is configured to generate control flow and call graph variants by predicting edge perturbations and node substitutions under semantic-preserving constraints. The output of both subprocessors is validated by a domain consistency discriminator to ensure the functional equivalence of the generated variants.

[0033] In one embodiment, the discriminative neural processor (106) comprises a hybrid inference network with a convolutional analysis unit for extracting local sequence patterns, a temporal attention unit for modeling long-term dependencies across system event time axes, and a graph reasoning unit for topologically embedding behavioral graphs. The hybrid inference network generates a scalar malevolence score and an interpretable attribute vector that specifies the feature contributions to the decision outcome. The discriminative neural processor is continuously updated by adversarial gradient propagation from the generative neural processor to ensure robustness against new morphing variants.

[0034] In one embodiment, the coordination processor (108) comprises an adaptive control unit configured to monitor confidence metrics for inference in real time, calculate risk-weighted loss penalties based on historical distributions of false positive and false negative results, and dynamically reassign training cycles between the generative neural processor and the discriminative neural processor. The coordination processor is further configured to apply amplification feedback, where rewards are inversely proportional to expected system downtime or the cost of data corruption associated with undetected malware.

[0035] In one embodiment, the secure inference and enforcement unit (110) comprises a programmable network interface controller configured to perform inline traffic rerouting, data rate throttling, or selective quarantine of data flows without central policy synchronization. The enforcement unit further comprises a host-side process controller operationally connected to endpoint agents to suspend or revoke the privileges of infected processes upon receiving a command. The entire enforcement pipeline operates under real-time conditions to ensure sub-millisecond detection-to-response latency.

[0036] In one embodiment, the hardware-embedded security enclave (112) comprises an isolated computing environment with a cryptographic verification processor and a model attestation unit. The cryptographic verification processor is configured to verify digital signatures associated with model updates and adversarial training parameters. The model attestation unit is configured to generate a signed attestation at each model execution cycle. This attestation can be remotely verified by a trusted security orchestrator to confirm the system's integrity.

[0037] In one embodiment, the generative neural processor (104) and the discriminative neural processor (106) are jointly implemented on a hardware accelerator fabric comprising a field-programmable gate array with reconfigurable logic arrays. The fabric is configured to execute matrix multiplication kernels, activation functions, and attention computations in a pipelined manner with hardware parallelism. The coordinator dynamically configures the logic arrays to achieve an adaptive trade-off between throughput and precision, depending on the current network load.

[0038] In one embodiment, the system further includes a privacy-preserving update processor configured to apply encrypted gradient aggregation during distributed learning across multiple edge devices, ensuring that only homomorphically encrypted model weight updates are exchanged. This ensures that raw telemetry data and sensitive behavioral traces remain on each local device, preventing data leakage while enabling global convergence of adversarial learning.

[0039] In one embodiment, the data acquisition unit (102) further comprises a differential telemetry buffer configured to store compressed statistical sketches of network and process features. The buffer implements probabilistic counting structures such as count-min sketches and Bloom filters to provide approximate, yet memory-efficient, feature histograms. The outputs are fed directly into the discriminative neural processor to reduce input dimensionality while maintaining discriminative accuracy.

[0040] The system for real-time detection and defense against evolving malware in high-density edge networks is an integrated cyber-physical intelligence framework. It is designed to autonomously identify and neutralize rapidly evolving, polymorphic, or self-modifying malware in large-scale distributed computing environments. The system achieves this by combining multimodal data acquisition, generative-discriminative adversarial learning, adaptive coordination control, and hardware-based enforcement within a single, tightly coupled architecture. Each system component functions as a technically independent module but contributes cooperatively to the continuous cycle of learning → detection → defense → adaptation.The technical operation begins with telemetry collection at edge nodes, continues with adversarial AI modeling to simulate and detect evolving malware, and culminates in localized, hardware-based defense and unchanging security assurance.

[0041] The data acquisition unit utilizes high-throughput telemetry adapters and parallel parsing engines capable of receiving heterogeneous input streams from various sources, including packet capture modules, system call monitors, binary execution tracers, and control flow instrumentation. Internally, it employs a feature harmonization pipeline that transforms each incoming data stream into a fixed-dimensional representation using embedding encoders trained to capture temporal-behavioral signatures in sliding windows. The unit also integrates a privacy-preserving redaction routine that removes user-identifiable content before persistent storage through deterministic masking or homomorphic transformation.Each normalized vector is stored in a secure buffer shared with the neural processors, thus enabling real-time streaming without loss of temporal coherence.

[0042] The generative neural processor functions as a deep probabilistic modeling engine that learns latent transformation functions describing the morphing processes of real-world malware variants while preserving their operational intent. The processor incorporates a variational model. The autoencoder backbone (VAE) is augmented by a conditional generator network trained on annotated, malicious telemetry embeddings. During training, the model parameterizes the distribution of structural features such as opcode patterns, control flow deviations, and entropy gradients. Subsequently, stochastic perturbations and semantic conservation conditions are applied to synthesize novel, morphing malware samples that mimic evolving attack characteristics.These synthetic variants continuously expand the adversarial training corpus, enabling the detection network to generalize beyond known malware families and resist evasion tactics.

[0043] The discriminative neural processor serves as the detection counterpart to the generative unit and is trained adversarially via a feedback loop in the style of a generative adversarial network (GAN). It utilizes a multi-head attention-based convolutional recurrent architecture that receives telemetry embeddings and evaluates their anomaly probability using probabilistic scoring functions.

[0044] The coordination processor serves as the cognitive control core, overseeing the interaction between generative and discriminative processors. Architecturally, it incorporates a reinforcement learning controller that calculates adaptive recognition thresholds using reward functions that balance recognition sensitivity, false-positive penalties, and inference latency. The coordination processor executes a co-training scheduler that controls the number of training epochs, enforces stability conditions through gradient regularization, and triggers countermeasures in real time whenever recognition accuracy exceeds a predefined adaptive threshold. Furthermore, it quantifies the system's decision reliability by calculating a false-negative penalty and reweighting the model's learning rates to minimize repeated misclassification patterns across distributed nodes.

[0045] The secure inference and enforcement unit is implemented as a hardware-based edge controller that executes low-latency countermeasures upon triggering by the coordination processor. Embedded in the network's data path, this unit comprises programmable logic modules capable of enforcing policy-based responses such as selective packet filtering, flow isolation, process interruption, and microsegmentation of virtual network functions. Execution latency is minimized through FPGA-based logic and inline policy caching, ensuring sub-millisecond enforcement even under high traffic density. The enforcement unit operates autonomously at each edge node, preventing the local spread of malware without relying on central orchestration.

[0046] The hardware-based security enclave provides a trusted execution environment (TEE) embedded in the system hardware at the firmware level. It securely stores cryptographic keys, neural model weights, policy configurations, and health metrics using hardware root-of-trust protocols such as Trusted Platform Module (TPM) or Intel SGX. All calls to load and infer neural models are cryptographically verified before execution to ensure that no manipulated model or unauthorized process can alter the decision logic. The enclave also maintains a secure, monotonic counter to monitor parameter evolution and can generate remote attestation certificates for cross-node trust verification.

[0047] The generative neural processor implements a hybrid architecture for conditional generation, consisting of a sequence synthesis subprocessor and a graph morphing subprocessor. The sequence synthesis subprocessor uses an autoregressive recurrent topology with gated linear units and scaled attention heads for edge deployment. It processes context vectors from current telemetry data and previously observed harmful embeddings to generate candidate sequences for API calls or opcode-like byte-token sequences. Sampling is performed using temperature-controlled top-k sampling to balance diversity and plausibility. The graph morphing subprocessor uses a variation graph generator that creates perturbations of adjacency matrices and node feature labels, enforcing semantic-preserving constraints through learned validation checks.These validity checks include lightweight static invariants such as import table consistency, base block reachability, and the absence of immediate dead ends in the control flow. These are implemented as feasibility checks that reject structurally invalid proposals. The generated candidates undergo a domain consistency discriminator, a low-latency, rule-based verifier, and a resource-efficient dynamic emulator. This confirms that the generated morphing sequence retains its essential functionality or behavioral intent without executing harmful payloads. Candidate morphings that pass these checks are stored in a prioritized replay buffer. The priority is determined by their novelty relative to the morphological signature repository and the discriminator's classification certainty.

[0048] The discriminative neural processor is a hybrid inference network that fuses temporal, structural, and statistical signals. It consists of a convolutional frontend for local pattern recognition in byte and API sequences, a temporal attention core for modeling long-term dependencies in event time histories, and a graph reasoning core based on message passing that embeds behavioral graphs. The embedding vectors of these cores are chained and processed by dense fusion layers, which output a scalar malevolence score with an associated attribution vector. The attribution vector is computed through combined layer-wise relevance propagation and backprojection of attention weights, attributing the decision to contributing sequence tokens, graph nodes, or statistical features.During training, the discriminative processor employs a compound loss that balances classification accuracy based on labeled telemetry data, robustness against adversarial attacks on generated morphing data, and stability regularizers to prevent catastrophic forgetting. To ensure continuous learning under streaming conditions, the discriminative processor uses elastic weight consolidation, where importance scores for network weights are calculated based on Fisher information approximations, and a replay mechanism that nests historical examples from the reservoir and high-priority generated morphs into mini-batches. Optimization utilizes variants of stochastic gradient descent with adaptive learning rates and gradient clipping to avoid destabilizing updates from the constrained on-device accelerator.

[0049] Adversarial co-training is controlled by the coordinator, which implements a controlled, alternating update schedule. The coordinator manages separate training schedules for the generator and discriminator with configurable ratios. For example, the discriminative neural processor is updated for multiple minibatches in each generation step to ensure recognition stability. The generator's loss signals consist of a syntactic plausibility component, a semantic validity penalty, and an adversarial reward term that depends on the discriminator's output probability near its decision boundary. To stabilize the model, the coordinator uses gradient regularization and diversity-enhancing targets that penalize the generation of low-variance data to prevent model collapse.The coordination processor also monitors the model's confidence statistics, detection latency, and the distributions of false positives and false negatives. It employs a reinforcement-based strategy, where actions such as temporarily increasing generator diversity or reweighting playback priorities are chosen to minimize a composite cost function that reflects operational damage and service interruptions.

[0050] The logic for countermeasure decisions in the coordination processor is implemented as an iteratively solved optimization problem with constraints under real-time telemetry. The coordination processor calculates an expected damage estimate for potential measures using a risk model parameterized by historical propagation patterns, the criticality of facilities, and the calibrated confidence of the discriminator. Potential measures are evaluated based on their expected reduction of propagation risk minus the cost of operational disruptions; the measure with the highest score within the latency and false alarm budget constraints is selected.In low-risk contexts, the coordinating processor can choose immediate automated enforcement; in higher-risk contexts, the processor can initiate a shadow enforcement path in which the system simulates enforcement to estimate collateral damage before actual enforcement. The selection process uses approximate dynamic programming to meet the need for sub-millisecond decision-making on the hardware-accelerated inference path.

[0051] The secure inference and enforcement unit executes selected countermeasures using hardware-accelerated primitives. Network-wide countermeasures are executed via a programmable network interface controller, enabling inline packet redirection, flow throttling, and hardware-level filtering using match action tables. Host-level enforcement interacts with authenticated endpoint agents over a signed command channel to halt processes, revoke network handles, or quarantine binaries in encrypted local storage. Enforcement actions are logged atomically, along with their triggering telemetry data and mapping vector, in the hardware-anchored security enclave. The enforcement pipeline includes latency monitors that adjust model stacking and pipeline depth to meet deterministic worst-case limits.As the latency approaches the threshold values, the coordination processor reduces the generation loads or switches to a mode that allows only inference in order to ensure decision speed.

[0052] Model lifecycle and integrity management are embedded in the hardware-based security environment. This environment ensures the verified launch of model artifacts, cryptographic verification of model updates, and an immutable register that stores model versions, training epochs, and enforcement actions as signed entries. Distillation processes on the device are controlled by the model evolution planner: Periodically, a larger, external teacher model, trained on aggregated corpora, generates soft labels that are used to retrain compressed student models using quantization-aware retraining. The distillation steps include simulated pruning plans with weighted sparsification, followed by calibration using a validation buffer from the field.For distributed learning across multiple edge devices, the privacy-friendly update processor supports encrypted gradient aggregation using secure aggregation primitives. Only aggregated, encrypted updates are transmitted to a central aggregator or peer federation coordinator to ensure that raw telemetry data never leaves the local devices in plaintext.

[0053] The implementation on the hardware accelerator fabric maps neural computations to reconfigurable logic for matrix multiplication, kernels for sparse graph aggregation, and attention computations. The coordinator manages microcode for switching accelerator configurations at runtime to balance precision against throughput. Quantization-aware training ensures that inference maintains decision fidelity in reduced-bitwidth environments, while structured sparsity and weight clustering reduce the model footprint. The software stack includes robust security mechanisms: sandbox generators run in restricted execution contexts with limited memory and no network access during proposal synthesis. Domain consistency checks prevent the generation of functionally destructive payloads. Explainability outputs are encrypted and made available to operator consoles.Forensic snapshots are stored in the enclave for post-incident review. Together, these technical components enable a locally autonomous, continuously adversarially trained detection system that protects privacy, ensures model integrity, meets stringent latency targets, and provides auditable enforcement measures suitable for high-density edge network deployments.

[0054] In a dense smart manufacturing environment, ENDA devices are installed alongside clusters of industrial controllers and local gateways. A sudden data stream originates from a device that intermittently downloads obfuscated payloads and initiates processes. The preprocessor creates a sequence embedding and a CFG snapshot. Gnet, trained on previously observed obfuscation patterns, generates morphs that mimic the obfuscation pattern and finds examples that circumvent the current Dnet. Dnet, trained online adversarially, sees these generated morphs in a replay buffer and adjusts the weights to close the decision boundary.The controller, receiving a high confidence-malice score strongly associated with newly introduced API patterns and increasing inter-arrival entropy, triggers an automated micro-segmentation action by updating ENDA's inline forwarding table to block endpoint data flows and simultaneously instructing the endpoint agent to halt the suspicious process and copy the suspicious binary to quarantine. The entire process, from detection to enforcement, adheres to the operator's policy of ≤ 50 ms for local action, minimizing the risk of lateral propagation. Incident telemetry data, used model artifacts, and the sample file are securely exported to the central SOC for in-depth forensic analysis. Privacy filters remove all customer data during this process.

[0055] Numerous alternative implementations are being considered. The backbone architectures of Gnet and Dnet can vary: pure transformers, hybrids of convolutional GNN and flow-based statistical detectors, integrated with learned embeddings. ENDA can be deployed as a virtualized appliance in a carrier NFV environment, with the accelerator being a cloud FPGA instance and the secure enclave a VMM-based TEE. The system can be configured to run exclusively on host agents when inline network enforcement is unavailable, or to coordinate multiple ENDA instances in a federated topology where model updates are aggregated through privacy-compliant federated learning. Countermeasures can extend to upstream orchestration (e.g., installing SDN controller rules) and regulatory compliance workflows.Security features can include signed boot chains, rollback protection, and regular remote attestation.

[0056] The invention is suitable for all environments requiring real-time protection against hard-to-detect malware in resource-constrained or distributed infrastructures: industrial control systems, telecommunications networks, smart buildings, vehicle edge clusters, and enterprise micro data centers. Implementation aspects include sizing ENDA SKUs according to the expected event throughput, configuring policy thresholds to optimize security and availability, and setting up SOC workflows for manual review. Compliance with regulations and data protection laws is ensured through on-device privacy filters, secure export controls for telemetry data, and explicit audit logs that support incident reporting and forensic investigations in accordance with applicable law.

[0057] The Neuro-Generative Adversarial System and Edge Neuro-Defender Appliance described here offer a practical, implementable, and demonstrably robust approach to detecting and defending against evolving malware in high-density edge networks. By combining on-device generative adversarial learning, efficient graph and sequence embedding, hardware acceleration, privacy-friendly update mechanisms, and auditable countermeasures, the invention overcomes the limitations of existing centralized and signature-based defense mechanisms and can be implemented on various hardware and deployment options. The described embodiments enable those skilled in the art to design, adapt, and deploy the system to achieve low-latency detection, limited resource consumption, and auditable, policy-compliant enforcement against evolving malware threats.

[0058] 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.

[0059] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A Neurogenerative Adversarial System for Real-Time Detection and Combating of Malware Morphing in High-Density Edge Networks. 102 Data acquisition unit 104 Generative Neural Processor 106 Discriminative Neural Processor 108 Coordination Processor 110 For Safe Conclusions and Enforcement 112 Hardware-anchored security enclave

Claims

[1] A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations. [2] System according to claim 1, wherein the data acquisition unit comprises a multi-core telemetry preprocessor and a feature extraction processor, wherein the multi-core telemetry preprocessor is configured to implement time-synchronized sampling of packets and processes across heterogeneous devices within the edge network, and the feature extraction processor is configured to create behavior vectors containing metrics for entropy, opcode frequency, execution time, arrival variance between packets, and API call graph density, each computed in real time using memory-constrained incremental techniques. [3] System according to claim 1, wherein the generative neural processor is implemented as a conditional deep generative architecture comprising a sequence synthesis subprocessor and a graph morphing subprocessor, wherein the sequence synthesis subprocessor is configured to construct malware byte sequences and dynamic API call chains using a gated recurrent architecture with context-sensitive attention layers, and the graph morphing subprocessor is configured to generate control flow and call graph variants by predicting edge perturbations and node substitutions under semantic-preserving constraints, wherein the output of both subprocessors is validated by a domain consistency discriminator to ensure the functional equivalence of the generated variants. [4] System according to claim 1, wherein the discriminative neural processor comprises a hybrid inference network comprising a convolution analysis unit for extracting local sequence patterns, a temporal attention unit for modeling long-term dependencies across system event time axes, and a graph reasoning unit for topologically embedding behavior graphs, wherein the hybrid inference network generates a scalar malevolence score and an interpretable attribution vector indicating the feature contributions to the decision outcome, and wherein the discriminative neural processor is continuously updated by adversarial gradient propagation from the generative neural processor to ensure robustness against new morphing variants. [5] System according to claim 1, wherein the coordination processor comprises an adaptive control unit configured to monitor real-time inference confidence metrics, calculate risk-weighted loss penalties based on historical false positive and false negative distributions, and dynamically reassign training cycles between the generative neural processor and the discriminative neural processor, wherein the coordination processor is further configured to apply reinforcement feedback, the rewards being inversely proportional to the expected system downtime or data corruption costs associated with undetected malware. [6] System according to claim 1, wherein the secure inference and enforcement unit comprises a programmable network interface controller configured to perform inline traffic rerouting, data rate throttling or selective quarantine of data streams without central policy synchronization, wherein the enforcement unit further comprises a host-side process controller operationally connected to endpoint agents to suspend or revoke the privileges of infected processes upon receipt of a command, wherein the entire enforcement pipeline operates under real-time conditions to ensure sub-millisecond detection-to-response latency. [7] System according to claim 1, wherein the hardware-embedded security enclave comprises an isolated computing environment containing a cryptographic verification processor and a model attestation unit, wherein the cryptographic verification processor is configured to verify digital signatures associated with model updates and adversarial training parameters, and the model attestation unit is configured to generate a signed proof of integrity at each model execution cycle, wherein the proof is remotely verifiable by a trusted security orchestrator to confirm the intact state of the system. [8] System according to claim 1, wherein the generative neural processor and the discriminative neural processor are jointly implemented on a hardware accelerator fabric comprising a field-programmable gate array with reconfigurable logic arrays, wherein the fabric is configured to execute matrix multiplication kernels, activation functions and attention computations pipelined with hardware parallelism, and wherein the coordinating processor dynamically configures the logic arrays to achieve an adaptive trade-off between throughput and precision depending on the current network traffic load. [9] System according to claim 1, wherein the system further comprises a privacy-preserving update processor configured to apply encrypted gradient aggregation during distributed learning across multiple edge devices, such that only homomorphically encrypted model weight updates are exchanged, ensuring that raw telemetry data and sensitive behavioral traces remain on each local device, thereby preventing data leakage while enabling global convergence of adversarial learning. [10] System according to claim 1, wherein the data acquisition unit further comprises a differential telemetry buffer configured to store compressed statistical sketches of network and process features, the buffer implementing probabilistic counting structures such as count-min sketches and Bloom filters to provide approximate but memory-efficient feature histograms, the outputs of which are fed directly into the discriminative neural processor to reduce the input dimensionality while maintaining discriminative accuracy.

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