The active prober emits tcpfp_fingerprint events with TTL, window, MSS etc.
from the attacker's SYN-ACK. These were invisible to the behavioral profiler
for two reasons:
1. target_ip (prober's field name for attacker IP) was not in _IP_FIELDS in
collector/worker.py or correlation/parser.py, so the profiler re-parsed
raw_lines and got attacker_ip=None, never attributing prober events to
the attacker profile.
2. sniffer_rollup only handled tcp_syn_fingerprint (passive sniffer) and
ignored tcpfp_fingerprint (active prober). Prober events use different
field names: window_size/window_scale/sack_ok vs window/wscale/has_sack.
Changes:
- Add target_ip to _IP_FIELDS in collector and parser
- Add _PROBER_TCPFP_EVENT and _INITIAL_TTL table to behavioral.py
- sniffer_rollup now processes tcpfp_fingerprint: maps field names, derives
OS from TTL via _os_from_ttl, computes hop_distance = initial_ttl - observed
- Expand prober DEFAULT_TCPFP_PORTS to [22,80,443,8080,8443,445,3389] for
better SYN-ACK coverage on attacker machines
- Add 4 tests covering prober OS detection, hop distance, and field mapping
Extends the prober with two new active probe types alongside JARM:
- HASSHServer: SSH server fingerprinting via KEX_INIT algorithm ordering
(MD5 hash of kex;enc_s2c;mac_s2c;comp_s2c, pure stdlib)
- TCP/IP stack: OS/tool fingerprinting via SYN-ACK analysis using scapy
(TTL, window size, DF bit, MSS, TCP options ordering, SHA256 hash)
Worker probe cycle now runs three phases per IP with independent
per-type port tracking. Ingester extracts bounties for all three
fingerprint types.
