#include <linux/bpf.h>
#include <bpf/bpf_helpers.h> SEC("xdp")
int xdp_drop_malicious(struct xdp_md *ctx) { void *data_end = (void *)(long)ctx->data_end; void *data = (void *)(long)ctx->data; /* Example: Drop all traffic from a specific malicious IP range */ struct iphdr *iph = data + sizeof(struct ethhdr); if ((void *)(iph + 1) > data_end) return XDP_PASS; if (iph->saddr == bpf_htonl(0x0A000001)) { // 10.0.0.1 return XDP_DROP; } return XDP_PASS;
} char _license[] SEC("license") = "GPL";
#include <linux/bpf.h>
#include <bpf/bpf_helpers.h> SEC("xdp")
int xdp_drop_malicious(struct xdp_md *ctx) { void *data_end = (void *)(long)ctx->data_end; void *data = (void *)(long)ctx->data; /* Example: Drop all traffic from a specific malicious IP range */ struct iphdr *iph = data + sizeof(struct ethhdr); if ((void *)(iph + 1) > data_end) return XDP_PASS; if (iph->saddr == bpf_htonl(0x0A000001)) { // 10.0.0.1 return XDP_DROP; } return XDP_PASS;
} char _license[] SEC("license") = "GPL";
#include <linux/bpf.h>
#include <bpf/bpf_helpers.h> SEC("xdp")
int xdp_drop_malicious(struct xdp_md *ctx) { void *data_end = (void *)(long)ctx->data_end; void *data = (void *)(long)ctx->data; /* Example: Drop all traffic from a specific malicious IP range */ struct iphdr *iph = data + sizeof(struct ethhdr); if ((void *)(iph + 1) > data_end) return XDP_PASS; if (iph->saddr == bpf_htonl(0x0A000001)) { // 10.0.0.1 return XDP_DROP; } return XDP_PASS;
} char _license[] SEC("license") = "GPL"; - Snort/Suricata: These are primarily signature-based. They look for specific strings or byte sequences in packet payloads. While efficient for known threats, they are blind to zero-day exploits and polymorphic malware.- Zeek (formerly Bro): Zeek is a powerful network analysis framework that focuses on metadata and behavioral logging. It is more flexible than Snort but still requires significant manual scripting (Zeek scripts) to perform sophisticated threat hunting.- NAPSE (HookProbe AI-native engine): Unlike legacy systems, NAPSE doesn't just look for signatures. It uses unsupervised learning to establish a baseline of 'normal' network behavior and identifies anomalies in real-time. This allows it to detect novel attack patterns that have no existing signature. - Ingestion: Telemetry is captured at the edge via the 7-POD architecture.- Analysis: The Neural-Kernel processes the data using NAPSE, identifying anomalous patterns.- Reasoning: An LLM-based reasoning engine evaluates the context (e.g., is this a critical server? is the traffic consistent with known MITRE ATT&CK techniques?).- Action: AEGIS executes a defense reflex, such as isolating a container, dropping a flow via XDP, or revoking a Zero-Trust token.
