// Example: Simple XDP program for packet counting and filtering #include <linux/bpf.h> #include <bpf/bpf_helpers.h> SEC("xdp_prog") int xdp_packet_inspector(struct xdp_md *ctx) { void *data_end = (void *)(long)ctx->data_end; void *data = (void *)(long)ctx->data; // Analyze packet headers here // High-speed telemetry extraction for NAPSE engine return XDP_PASS; } // Example: Simple XDP program for packet counting and filtering #include <linux/bpf.h> #include <bpf/bpf_helpers.h> SEC("xdp_prog") int xdp_packet_inspector(struct xdp_md *ctx) { void *data_end = (void *)(long)ctx->data_end; void *data = (void *)(long)ctx->data; // Analyze packet headers here // High-speed telemetry extraction for NAPSE engine return XDP_PASS; } // Example: Simple XDP program for packet counting and filtering #include <linux/bpf.h> #include <bpf/bpf_helpers.h> SEC("xdp_prog") int xdp_packet_inspector(struct xdp_md *ctx) { void *data_end = (void *)(long)ctx->data_end; void *data = (void *)(long)ctx->data; // Analyze packet headers here // High-speed telemetry extraction for NAPSE engine return XDP_PASS; } - **Inter-arrival times (IAT):** The timing between packets, which can reveal automated C2 (Command and Control) heartbeats. - **JA3/JA4 TLS Fingerprints:** Identifying the client and server software based on the SSL/TLS handshake, even when the traffic is encrypted. - **Entropy levels:** High entropy in packet payloads often suggests encrypted or compressed malicious payloads. - **Flow directionality:** Analyzing the ratio of sent vs. received bytes to detect data exfiltration. - **Inter-arrival times (IAT):** The timing between packets, which can reveal automated C2 (Command and Control) heartbeats. - **JA3/JA4 TLS Fingerprints:** Identifying the client and server software based on the SSL/TLS handshake, even when the traffic is encrypted. - **Entropy levels:** High entropy in packet payloads often suggests encrypted or compressed malicious payloads. - **Flow directionality:** Analyzing the ratio of sent vs. received bytes to detect data exfiltration. - **Inter-arrival times (IAT):** The timing between packets, which can reveal automated C2 (Command and Control) heartbeats. - **JA3/JA4 TLS Fingerprints:** Identifying the client and server software based on the SSL/TLS handshake, even when the traffic is encrypted. - **Entropy levels:** High entropy in packet payloads often suggests encrypted or compressed malicious payloads. - **Flow directionality:** Analyzing the ratio of sent vs. received bytes to detect data exfiltration. - **Ingress POD:** Handles raw packet capture via eBPF/XDP. - **Analysis POD (NAPSE):** The AI engine performing behavioral inference. - **Context POD:** Enriches alerts with threat intelligence and asset data. - **Mitigation POD (AEGIS):** Executes autonomous defense actions (e.g., firewall shunning). - **Storage POD:** Efficiently logs telemetry using time-series databases. - **Management POD:** Handles orchestration and configuration updates. - **Interface POD:** Provides the user dashboard and API access. - **Ingress POD:** Handles raw packet capture via eBPF/XDP. - **Analysis POD (NAPSE):** The AI engine performing behavioral inference. - **Context POD:** Enriches alerts with threat intelligence and asset data. - **Mitigation POD (AEGIS):** Executes autonomous defense actions (e.g., firewall shunning). - **Storage POD:** Efficiently logs telemetry using time-series databases. - **Management POD:** Handles orchestration and configuration updates. - **Interface POD:** Provides the user dashboard and API access. - **Ingress POD:** Handles raw packet capture via eBPF/XDP. - **Analysis POD (NAPSE):** The AI engine performing behavioral inference. - **Context POD:** Enriches alerts with threat intelligence and asset data. - **Mitigation POD (AEGIS):** Executes autonomous defense actions (e.g., firewall shunning). - **Storage POD:** Efficiently logs telemetry using time-series databases. - **Management POD:** Handles orchestration and configuration updates. - **Interface POD:** Provides the user dashboard and API access. tritonserver # Example Triton Server Configuration tritonserver --model-repository=/models \\ --strict-model-config=true \\ --log-verbose=1 \\ --allow-gpu-metrics=true # Example Triton Server Configuration tritonserver --model-repository=/models \\ --strict-model-config=true \\ --log-verbose=1 \\ --allow-gpu-metrics=true # Example Triton Server Configuration tritonserver --model-repository=/models \\ --strict-model-config=true \\ --log-verbose=1 \\ --allow-gpu-metrics=true - Inference Latency: The time taken for the AI model to score a network flow. False-Positive Rate (FPR): Essential for minimizing alert fatigue during the "cold start" phase. GPU/NPU Memory Saturation: Ensuring the AI models don't exhaust edge compute resources. Queue Depth: Monitoring the buffer between packet capture and inference. - Inference Latency: The time taken for the AI model to score a network flow. False-Positive Rate (FPR): Essential for minimizing alert fatigue during the "cold start" phase. GPU/NPU Memory Saturation: Ensuring the AI models don't exhaust edge compute resources. Queue Depth: Monitoring the buffer between packet capture and inference. - Inference Latency: The time taken for the AI model to score a network flow. False-Positive Rate (FPR): Essential for minimizing alert fatigue during the "cold start" phase. GPU/NPU Memory Saturation: Ensuring the AI models don't exhaust edge compute resources. Queue Depth: Monitoring the buffer between packet capture and inference. - Snort: Best for high-speed signature matching; limited behavioral analysis. Zeek (Bro): Excellent for network metadata and scripting; requires significant manual effort to build detection logic. Suricata: Supports multi-threading and some ML plugins, but still primarily signature-driven. HookProbe: Built from the ground up as AI-native, focusing on autonomous response (AEGIS) and edge-first processing. - Snort: Best for high-speed signature matching; limited behavioral analysis. Zeek (Bro): Excellent for network metadata and scripting; requires significant manual effort to build detection logic. Suricata: Supports multi-threading and some ML plugins, but still primarily signature-driven. HookProbe: Built from the ground up as AI-native, focusing on autonomous response (AEGIS) and edge-first processing. - Snort: Best for high-speed signature matching; limited behavioral analysis. Zeek (Bro): Excellent for network metadata and scripting; requires significant manual effort to build detection logic. Suricata: Supports multi-threading and some ML plugins, but still primarily signature-driven. HookProbe: Built from the ground up as AI-native, focusing on autonomous response (AEGIS) and edge-first processing. - Start in Shadow Mode: Deploy your AI models in a non-blocking mode initially to tune the baseline and reduce false positives. Normalize Telemetry: Ensure all edge nodes report data in a consistent format (e.g., JSON via OpenTelemetry) to simplify central analysis. Human-in-the-Loop (HITL): Use the AI to filter the noise, but ensure critical alerts are validated by human analysts, especially during the initial deployment phase. Rigorous Versioning: Use tools like MLflow to track model versions and performance across different edge environments. - Start in Shadow Mode: Deploy your AI models in a non-blocking mode initially to tune the baseline and reduce false positives. Normalize Telemetry: Ensure all edge nodes report data in a consistent format (e.g., JSON via OpenTelemetry) to simplify central analysis. Human-in-the-Loop (HITL): Use the AI to filter the noise, but ensure critical alerts are validated by human analysts, especially during the initial deployment phase. Rigorous Versioning: Use tools like MLflow to track model versions and performance across different edge environments. - Start in Shadow Mode: Deploy your AI models in a non-blocking mode initially to tune the baseline and reduce false positives. Normalize Telemetry: Ensure all edge nodes report data in a consistent format (e.g., JSON via OpenTelemetry) to simplify central analysis. Human-in-the-Loop (HITL): Use the AI to filter the noise, but ensure critical alerts are validated by human analysts, especially during the initial deployment phase. Rigorous Versioning: Use tools like MLflow to track model versions and performance across different edge environments. - False-Positive Rate (FPR): Essential for minimizing alert fatigue during the "cold start" phase. - GPU/NPU Memory Saturation: Ensuring the AI models don't exhaust edge compute resources. - Queue Depth: Monitoring the buffer between packet capture and inference. - Zeek (Bro): Excellent for network metadata and scripting; requires significant manual effort to build detection logic. - Suricata: Supports multi-threading and some ML plugins, but still primarily signature-driven. - HookProbe: Built from the ground up as AI-native, focusing on autonomous response (AEGIS) and edge-first processing. - Normalize Telemetry: Ensure all edge nodes report data in a consistent format (e.g., JSON via OpenTelemetry) to simplify central analysis. - Human-in-the-Loop (HITL): Use the AI to filter the noise, but ensure critical alerts are validated by human analysts, especially during the initial deployment phase. - Rigorous Versioning: Use tools like MLflow to track model versions and performance across different edge environments.