Spaces:
Sleeping
Sleeping
File size: 9,430 Bytes
4440ec1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | from typing import Any, Dict, List, Set
from models import NetworkForensicsAction, PacketRecord, GroundTruth, Reward
STEP_REWARD_MIN = -0.12
STEP_REWARD_MAX = 0.30
def _clamp01(value: float) -> float:
return max(0.0, min(1.0, value))
def _normalize_step_reward(raw_reward: float) -> float:
scaled = (raw_reward - STEP_REWARD_MIN) / (STEP_REWARD_MAX - STEP_REWARD_MIN)
return round(_clamp01(scaled), 4)
def _best_matching_session(
submitted: Set[str],
sessions: Dict[str, List[str]],
) -> tuple[str | None, float]:
best_session = None
best_overlap = 0.0
for session_name, session_packets in sessions.items():
truth = set(session_packets)
union = submitted | truth
overlap = (len(submitted & truth) / len(union)) if union else 0.0
if overlap > best_overlap:
best_overlap = overlap
best_session = session_name
return best_session, best_overlap
def compute_reward(
action: NetworkForensicsAction,
packets: List[PacketRecord],
ground_truth: GroundTruth,
flagged_packets: Set[str],
grouped_sessions: Dict[str, List[str]],
tagged_patterns: Dict[str, str],
reward_state: Dict[str, Any],
) -> Reward:
raw_step_reward = -0.005
breakdown = {"step_cost_raw": -0.005}
done = action.action_type == "submit_report"
message = ""
packet_map = {p.packet_id: p for p in packets}
malicious_set = set(ground_truth.malicious_packets)
sessions = ground_truth.sessions or {}
session_roles = ground_truth.session_roles or {}
inspected_malicious = reward_state.setdefault("inspected_malicious", set())
flagged_malicious = reward_state.setdefault("flagged_malicious", set())
rewarded_sessions = reward_state.setdefault("rewarded_sessions", set())
rewarded_tags = reward_state.setdefault("rewarded_tags", set())
reward_state.setdefault("entry_point_rewarded", False)
if action.action_type == "inspect_packet" and action.packet_id:
if action.packet_id in packet_map:
pkt = packet_map[action.packet_id]
if action.packet_id in malicious_set and not pkt.is_revealed:
delta = 0.08
if action.packet_id not in inspected_malicious:
delta += 0.04
inspected_malicious.add(action.packet_id)
breakdown["inspect_progress_raw"] = 0.04
raw_step_reward += delta
breakdown["malicious_inspect_raw"] = round(delta, 4)
elif action.packet_id not in malicious_set and not pkt.is_revealed:
raw_step_reward -= 0.02
breakdown["benign_inspect_raw"] = -0.02
else:
raw_step_reward -= 0.06
breakdown["repeat_inspect_raw"] = -0.06
pkt.is_revealed = True
else:
raw_step_reward -= 0.03
breakdown["invalid_packet_raw"] = -0.03
elif action.action_type == "flag_as_suspicious" and action.packet_id:
if action.packet_id in flagged_packets:
raw_step_reward -= 0.08
breakdown["already_flagged_raw"] = -0.08
elif action.packet_id in packet_map:
if action.packet_id in malicious_set:
delta = 0.12
if action.packet_id not in flagged_malicious:
delta += 0.05
flagged_malicious.add(action.packet_id)
breakdown["flag_progress_raw"] = 0.05
raw_step_reward += delta
breakdown["correct_flag_raw"] = round(delta, 4)
else:
raw_step_reward -= 0.10
breakdown["false_positive_raw"] = -0.10
else:
raw_step_reward -= 0.04
breakdown["invalid_packet_raw"] = -0.04
elif action.action_type == "group_into_session" and action.session_name and action.packet_ids:
submitted = {pid for pid in action.packet_ids if pid in packet_map}
best_session, best_overlap = _best_matching_session(submitted, sessions)
if best_session and best_overlap > 0:
truth = set(sessions[best_session])
precision = len(submitted & truth) / max(1, len(submitted))
recall = len(submitted & truth) / max(1, len(truth))
group_score = (precision + recall) / 2
delta = round(group_score * 0.18 - 0.04, 4)
if group_score >= 0.6 and best_session not in rewarded_sessions:
delta += 0.12
rewarded_sessions.add(best_session)
breakdown["session_progress_raw"] = 0.12
raw_step_reward += delta
breakdown["group_overlap_raw"] = delta
message = f"Matched session {best_session} with score {group_score:.2f}"
else:
correct = sum(1 for pid in submitted if pid in malicious_set)
wrong = len(submitted) - correct
delta = round(correct * 0.03 - wrong * 0.05, 4)
raw_step_reward += delta
breakdown["group_fallback_raw"] = delta
elif action.action_type == "tag_pattern" and action.session_name and action.pattern_type:
if action.session_name in grouped_sessions:
pattern = action.pattern_type.strip().lower()
expected_role = session_roles.get(action.session_name)
matched_truth_session = action.session_name if expected_role else None
if not expected_role:
submitted = set(grouped_sessions[action.session_name])
matched_truth_session, overlap = _best_matching_session(submitted, sessions)
if matched_truth_session and overlap >= 0.6:
expected_role = session_roles.get(matched_truth_session)
if expected_role and pattern == expected_role.lower():
delta = 0.10
if matched_truth_session and matched_truth_session not in rewarded_tags:
delta += 0.06
rewarded_tags.add(matched_truth_session)
breakdown["tag_progress_raw"] = 0.06
raw_step_reward += delta
breakdown["correct_tag_raw"] = round(delta, 4)
else:
raw_step_reward -= 0.08
breakdown["wrong_tag_raw"] = -0.08
else:
raw_step_reward -= 0.05
breakdown["unknown_session_raw"] = -0.05
elif action.action_type == "identify_entry_point" and action.claimed_entry_point:
if ground_truth.entry_point and action.claimed_entry_point == ground_truth.entry_point:
delta = 0.12
if not reward_state["entry_point_rewarded"]:
delta += 0.08
reward_state["entry_point_rewarded"] = True
breakdown["entry_progress_raw"] = 0.08
raw_step_reward += delta
breakdown["correct_entry_point_raw"] = round(delta, 4)
else:
raw_step_reward -= 0.10
breakdown["wrong_entry_point_raw"] = -0.10
elif action.action_type == "submit_report":
flagged = set(flagged_packets)
true_positive = len(flagged & malicious_set)
precision = true_positive / max(1, len(flagged))
recall = true_positive / max(1, len(malicious_set))
f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
session_hits = 0
for session_name, truth_packets in sessions.items():
truth = set(truth_packets)
for grouped in grouped_sessions.values():
if truth == set(grouped):
session_hits += 1
break
session_score = session_hits / max(1, len(sessions))
tag_hits = 0
for submitted_name, submitted_packets in grouped_sessions.items():
matched_truth_session, overlap = _best_matching_session(set(submitted_packets), sessions)
if matched_truth_session and overlap >= 0.8:
expected_role = session_roles.get(matched_truth_session, "").lower()
if tagged_patterns.get(submitted_name, "").lower() == expected_role:
tag_hits += 1
tag_score = tag_hits / max(1, len(session_roles))
entry_score = 1.0 if reward_state.get("entry_point_rewarded") else 0.0
final_score = round(f1 * 0.45 + session_score * 0.25 + tag_score * 0.20 + entry_score * 0.10, 4)
final_bonus = round(final_score * 0.45, 4)
raw_step_reward += final_bonus
breakdown["final_f1"] = round(f1, 4)
breakdown["final_session_score"] = round(session_score, 4)
breakdown["final_tag_score"] = round(tag_score, 4)
breakdown["final_entry_score"] = round(entry_score, 4)
breakdown["final_score"] = final_score
breakdown["final_bonus_raw"] = final_bonus
message = f"Report precision={precision:.2f} recall={recall:.2f} score={final_score:.2f}"
success = done and breakdown.get("final_score", 0.0) >= 0.6
step_reward = _normalize_step_reward(raw_step_reward)
breakdown["raw_step_reward"] = round(raw_step_reward, 4)
breakdown["normalized_step_reward"] = step_reward
return Reward(
step_reward=step_reward,
cumulative_reward=step_reward,
done=done,
success=success,
breakdown=breakdown,
message=message or f"Action: {action.action_type}",
)
|