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AP_Prob_Rs_3_Scenarios_Latest.py
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1872 lines (1636 loc) · 83.7 KB
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# AP_Prob_RS_Complete_3_Scenarios.py
# Complete implementation preserving ALL original functionality
# PLUS: 3 training scenarios (100%, 50/50, 80/20) with error analysis vs NCISS scores
#
# Original pipeline flow (ALL PRESERVED):
# 1) MITRE ATT&CK techniques/sub-techniques + Campaigns (Excel)
# 2) Tactic-ordered chains from campaigns (pre-LSTM)
# 3) Train LSTM on chains + post-LSTM probability + risk
# 4) Ingest Unit42 Playbooks (local) + Attack Flow JSON (local) -> sequences
# 5) Train first-order Markov on JSON-only sequences
# 6) Validate + (6b) stream-safe Markov expansions of LSTM chains
# 7) Score Markov sequences (risk then probability)
# 8) (Optional) Dataset mapping → filter + rank
# 8b) Dataset-anchored predictions: start from dataset attack types, predict next steps
# + Save catalogs, plots, README, and print important counts.
#
# NEW ADDITIONS:
# 9) Three-scenario LSTM training (100% TRUE training - no validation, 50/50, 80/20)
# 10) Error rate calculation for 50/50 and 80/20 scenarios only
# 11) Cross-scenario comparison and visualization
import os, re, math, random, warnings, time, logging, json, datetime, glob, csv, signal, sys
from collections import defaultdict, Counter
from itertools import islice, permutations, product
from typing import List, Dict, Tuple
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from tqdm.auto import tqdm
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore", category=UserWarning, message="This figure includes Axes that are not compatible with tight_layout*")
# =========================================================
# 0) CONFIG — EDIT THESE PATHS (or set env vars)
# =========================================================
ATTACK_FILE = os.environ.get("ATTACK_FILE",
'/Users/mayankraj/Desktop/RESEARCH/6. Attack Prediction and Risk Score/Enterprise attack csvs/enterprise-attack-v16.0.xls')
UNIT42_REPO_DIR = os.environ.get("UNIT42_REPO_DIR",
'/Users/mayankraj/Desktop/RESEARCH/6. Attack Prediction and Risk Score/playbook_viewer-master')
ATTACK_FLOW_STIX_DIR = os.environ.get("ATTACK_FLOW_STIX_DIR",
"/Users/mayankraj/Desktop/RESEARCH/6. Attack Prediction and Risk Score/Attack flows")
OUT_ROOT = os.environ.get("OUT_ROOT",
"/Users/mayankraj/Desktop/RESEARCH/6. Attack Prediction and Risk Score/")
EPSS_CSV = os.environ.get("EPSS_CSV", "")
KEV_CSV = os.environ.get("KEV_CSV", "")
DETECTION_CSV = os.environ.get("DETECTION_CSV", "")
SEVERITY_CSV = os.environ.get("SEVERITY_CSV", "/Users/mayankraj/Desktop/RESEARCH/6. Attack Prediction and Risk Score/MITRE Campaign severity score/MITRE_Campaign_Severity_Scores.csv")
OCTAVE_IMPACT_0_10_DEFAULT = float(os.environ.get("OCTAVE_IMPACT", "10.0"))
DATASET_PATH = os.environ.get("DATASET_PATH", "")
DATASET_LABEL = os.environ.get("DATASET_LABEL", "Label")
LOG_LEVEL = logging.INFO
TRAIN_PROGRESS_EVERY = 200
SEED = 42
MIN_CHAIN_LEN = 3
MAX_PERMS_PER_TACTIC = 25
MAX_FULL_CHAINS_PER_CAMPAIGN = 200
TACTIC_ORDER_DEFAULT = [
"reconnaissance","resource development","initial access","execution","persistence",
"privilege escalation","defense evasion","credential access","discovery",
"lateral movement","collection","command and control","exfiltration","impact"
]
EMBED_DIM = 128
HIDDEN_DIM = 256
NUM_LAYERS = 2
DROPOUT = 0.2
BATCH_SIZE = 64
EPOCHS = 50
LR = 3e-3
MARKOV_TOPK_NEXT = 10
MARKOV_BEAM = 50
MARKOV_MAX_LEN = 20
MARKOV_MIN_PROB = 1e-12
POST_MARKOV_MAX_CHAINS = int(os.environ.get("POST_MARKOV_MAX_CHAINS", "800"))
POST_MARKOV_EXPAND_SEED_LEN = int(os.environ.get("POST_MARKOV_EXPAND_SEED_LEN", "3"))
POST_MARKOV_LOG_EVERY = int(os.environ.get("POST_MARKOV_LOG_EVERY", "50"))
SKIP_POST_MARKOV_EXPANSION = os.environ.get("SKIP_POST_MARKOV_EXPANSION", "0") == "1"
RUN_THREE_SCENARIOS = os.environ.get("RUN_THREE_SCENARIOS", "1") == "1"
random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)
# =========================================================
# 0.1) Logger + Output folder + graceful-cancel
# =========================================================
logging.basicConfig(
level=LOG_LEVEL,
format="%(asctime)s | %(levelname)-7s | %(message)s",
datefmt="%H:%M:%S"
)
log = logging.getLogger("attack-chain-pipeline")
def section(title):
line = "=" * 38
log.info(f"\n{line}\n{title}\n{line}")
STAMP = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
OUT_DIR = os.path.join(OUT_ROOT, f"ATTACK_{STAMP}")
os.makedirs(OUT_DIR, exist_ok=True)
def save_fig(path):
plt.tight_layout()
plt.savefig(path, dpi=150)
plt.close()
ABORT = [False]
def _handle_signal(sig, frame):
log.warning(f"Received signal {sig}. Finishing current item and exiting gracefully...")
ABORT[0] = True
for _sig in (signal.SIGINT, signal.SIGTERM):
try:
signal.signal(_sig, _handle_signal)
except Exception:
pass
# =========================================================
# Helpers: Unit42 + Attack Flow + Markov
# =========================================================
def unit42_sequences_from_file(fp, tactic_order, ATTACK_ID_TO_NAME):
try:
with open(fp, "r", encoding="utf-8") as f:
bundle = json.load(f)
except Exception as e:
log.warning(f"Unit42 parse failed: {fp} ({e})")
return os.path.basename(fp), []
objs = bundle.get("objects", [])
playbook_name = next((o.get("name") for o in objs if o.get("type") == "report"), os.path.basename(fp))
tactic_buckets = {t: [] for t in tactic_order}
for o in objs:
if o.get("type") != "attack-pattern":
continue
name = o.get("name")
if not name:
ext = o.get("external_references", []) or []
tid = None
for ref in ext:
sid = ref.get("external_id", "")
if isinstance(sid, str) and re.match(r"^T\d{4}(\.\d{3})?$", sid):
tid = sid; break
if tid and tid in ATTACK_ID_TO_NAME:
name = ATTACK_ID_TO_NAME[tid]
if not name:
continue
tac = None
for p in o.get("kill_chain_phases", []) or []:
phase = str(p.get("phase_name", "")).lower()
kcn = str(p.get("kill_chain_name", "")).lower()
if phase in tactic_buckets and ("mitre" in kcn or kcn.startswith("mitre")):
tac = phase; break
if tac:
tactic_buckets[tac].append(name)
buckets = [tactic_buckets[t] for t in tactic_order if tactic_buckets[t]]
if not buckets:
return playbook_name, []
def sample_perms(bucket: list, k: int):
b = list(dict.fromkeys(bucket))
if len(b) <= 1: return [tuple(b)]
if len(b) <= 6: return list(islice(permutations(b), k))
out = set(); trials = 0
while len(out) < k and trials < k*20:
sh = b[:]; random.shuffle(sh); out.add(tuple(sh)); trials += 1
return list(out)
perms_per_bucket = [sample_perms(b, MAX_PERMS_PER_TACTIC) for b in buckets]
sequences = []
for combo in islice(product(*perms_per_bucket), MAX_FULL_CHAINS_PER_CAMPAIGN):
flat = [x for group in combo for x in group]
if len(flat) >= MIN_CHAIN_LEN:
sequences.append(flat)
return playbook_name, sequences
def load_unit42_sequences(repo_dir, tactic_order, ATTACK_ID_TO_NAME):
out = []
json_dir = os.path.join(repo_dir, "playbook_json")
if not os.path.isdir(json_dir):
log.warning(f"Unit42 directory missing 'playbook_json': {json_dir}")
return out
files = sorted(glob.glob(os.path.join(json_dir, "*.json")))
log.info(f"Unit42: found {len(files)} playbook JSON files.")
for fp in tqdm(files, desc="Unit42 playbooks", leave=False):
name, seqs = unit42_sequences_from_file(fp, tactic_order, ATTACK_ID_TO_NAME)
for s in seqs:
out.append(("Unit42", name, s))
return out
def af_sequences_from_file(fp, ATTACK_ID_TO_NAME):
try:
with open(fp, "r", encoding="utf-8") as f:
data = json.load(f)
except Exception as e:
log.warning(f"Attack Flow parse failed: {fp} ({e})")
return os.path.basename(fp), []
objs = data.get("objects", [])
if not isinstance(objs, list):
objs = [o for o in data.values() if isinstance(o, dict)]
by_id = {o.get("id"): o for o in objs if isinstance(o, dict) and o.get("id")}
flow = next((o for o in objs if o.get("type") == "attack-flow"), None)
if not flow:
return os.path.basename(fp), []
flow_name = flow.get("name", os.path.basename(fp))
starts = flow.get("start_refs", []) or []
paths = []
seen_cap = [0]
AF_MAX_PATHS_PER_FILE = 5000
def action_name(o):
tid = str(o.get("technique_id", "")).strip()
if tid and tid in ATTACK_ID_TO_NAME:
return ATTACK_ID_TO_NAME[tid]
tref = o.get("technique_ref")
if tref and tref in by_id:
nm = by_id[tref].get("name")
if nm: return nm
return o.get("name")
def next_refs(o):
t = o.get("type")
if t == "attack-action":
return o.get("effect_refs", []) or []
if t == "attack-condition":
return (o.get("on_true_refs", []) or []) + (o.get("on_false_refs", []) or [])
if t == "attack-operator":
return o.get("effect_refs", []) or []
return []
def dfs(node_id, acc, visited):
if seen_cap[0] >= AF_MAX_PATHS_PER_FILE: return
if node_id in visited: return
o = by_id.get(node_id)
if not o: return
visited = visited | {node_id}
if o.get("type") == "attack-action":
nm = action_name(o)
if nm: acc = acc + [nm]
nbs = next_refs(o)
if not nbs:
if len(acc) >= MIN_CHAIN_LEN:
paths.append(acc); seen_cap[0] += 1
return
for nb in nbs:
if seen_cap[0] >= AF_MAX_PATHS_PER_FILE: break
dfs(nb, acc, visited)
for s in starts:
if seen_cap[0] >= AF_MAX_PATHS_PER_FILE: break
dfs(s, [], set())
unique = list({tuple(seq) for seq in paths})
return flow_name, [list(t) for t in unique]
def load_attack_flow_sequences(stix_dir, ATTACK_ID_TO_NAME):
out = []
if not os.path.isdir(stix_dir):
log.warning(f"Attack Flow directory not found: {stix_dir}")
return out
files = sorted(glob.glob(os.path.join(stix_dir, "*.json")))
log.info(f"Attack Flow: found {len(files)} JSON files in {stix_dir}.")
for fp in tqdm(files, desc="Attack Flow STIX", leave=False):
name, seqs = af_sequences_from_file(fp, ATTACK_ID_TO_NAME)
for s in seqs:
out.append(("AttackFlow", name, s))
return out
def train_markov_from_sequences(json_sequences: List[List[str]]):
start_counts = Counter()
trans_counts = defaultdict(Counter)
for seq in json_sequences:
if not seq: continue
start_counts[seq[0]] += 1
for a,b in zip(seq[:-1], seq[1:]):
trans_counts[a][b] += 1
total_starts = sum(start_counts.values())
start_prob = {k: (v/total_starts if total_starts else 0.0) for k,v in start_counts.items()}
next_prob = {}
trans_total = 0
for a, ctr in trans_counts.items():
s = sum(ctr.values())
next_prob[a] = {b: (c/s if s else 0.0) for b,c in ctr.items()}
trans_total += len(ctr)
return start_prob, next_prob, len(start_prob), trans_total
def markov_chain_prob(seq: List[str], start_prob: Dict[str,float], next_prob: Dict[str,Dict[str,float]]):
if not seq: return 0.0
p0 = max(start_prob.get(seq[0], MARKOV_MIN_PROB), MARKOV_MIN_PROB)
logp = math.log(p0)
for a,b in zip(seq[:-1], seq[1:]):
p = next_prob.get(a, {}).get(b, MARKOV_MIN_PROB)
logp += math.log(max(p, MARKOV_MIN_PROB))
return math.exp(logp)
def expand_with_markov(prefix: List[str], start_prob, next_prob,
max_len=MARKOV_MAX_LEN, beam=MARKOV_BEAM, topk=MARKOV_TOPK_NEXT):
BeamItem = Tuple[List[str], float]
init_prob = markov_chain_prob(prefix, start_prob, next_prob)
beam_list: List[BeamItem] = [(prefix[:], init_prob)]
visited = set([tuple(prefix)])
while True:
extended: List[BeamItem] = []
progressed = False
for seq, p in beam_list:
if len(seq) >= max_len:
extended.append((seq, p)); continue
last = seq[-1]
nexts = sorted(next_prob.get(last, {}).items(), key=lambda kv: kv[1], reverse=True)[:topk]
if not nexts:
extended.append((seq, p)); continue
for nb, prob in nexts:
cand = seq + [nb]
key = tuple(cand)
if key in visited: continue
visited.add(key); progressed = True
extended.append((cand, p * max(prob, MARKOV_MIN_PROB)))
if not progressed: break
extended.sort(key=lambda x: x[1], reverse=True)
beam_list = extended[:beam]
return beam_list
def generate_markov_top_sequences(start_prob, next_prob, top_starts=20, beam=MARKOV_BEAM, topk=MARKOV_TOPK_NEXT, max_len=MARKOV_MAX_LEN):
starts = sorted(start_prob.items(), key=lambda kv: kv[1], reverse=True)[:top_starts]
all_out = []
for s, _ in starts:
beam_out = expand_with_markov([s], start_prob, next_prob, max_len=max_len, beam=beam, topk=topk)
all_out.extend(beam_out)
all_out.sort(key=lambda x: x[1], reverse=True)
seen = set(); unique = []
for seq, p in all_out:
t = tuple(seq)
if t in seen: continue
seen.add(t); unique.append((seq, p))
return unique
# =========================================================
# 1) Load MITRE ATT&CK Excel + maps + SEVERITY SCORES
# =========================================================
section("STEP 1) Load ATT&CK Excel & build technique/tactic maps")
assert os.path.exists(ATTACK_FILE), f"File not found: {ATTACK_FILE}"
read_kwargs = {}
if ATTACK_FILE.lower().endswith(".xls"):
read_kwargs["engine"] = "xlrd"
else:
read_kwargs["engine"] = "openpyxl"
t0 = time.time()
tech_df = pd.read_excel(ATTACK_FILE, sheet_name="techniques", **read_kwargs)
rel_df = pd.read_excel(ATTACK_FILE, sheet_name="relationships", **read_kwargs)
camp_df = pd.read_excel(ATTACK_FILE, sheet_name="campaigns", **read_kwargs)
log.info(f"Loaded Excel sheets took {time.time()-t0:.2f}s")
campaign_severity = {}
if os.path.exists(SEVERITY_CSV):
severity_df = pd.read_csv(SEVERITY_CSV)
severity_df['NCISS_Normalized'] = severity_df['NCISS_Score'] / 10.0
campaign_severity = dict(zip(severity_df['Campaign_ID'], severity_df['NCISS_Normalized']))
log.info(f"Loaded {len(campaign_severity)} campaign severity scores from {SEVERITY_CSV}")
else:
log.warning(f"Severity CSV not found: {SEVERITY_CSV}. Error analysis for 50/50 and 80/20 will be skipped.")
for col in ["ID","STIX ID","name","tactics"]:
if col not in tech_df.columns:
raise ValueError(f"'techniques' sheet missing column: {col}")
tech_df["tactics"] = tech_df["tactics"].fillna("").apply(
lambda x: [t.strip() for t in x.split(",")] if isinstance(x, str) else []
)
rel_type_col = None
for c in rel_df.columns:
if c.lower().strip() in ("relationship type","relationship_type","type"):
rel_type_col = c; break
stix_to_name = dict(zip(tech_df["STIX ID"], tech_df["name"]))
id_to_name = dict(zip(tech_df["ID"].astype(str), tech_df["name"]))
name_to_tactics = dict(zip(tech_df["name"], tech_df["tactics"]))
section("STEP 1a) Build sub-technique maps")
parent_to_subs = defaultdict(list)
sub_to_parent = dict()
if rel_type_col is not None:
for _, r in rel_df.iterrows():
src, tgt = r.get("source ref",""), r.get("target ref","")
rtype = str(r.get(rel_type_col,"")).lower()
if not (isinstance(src, str) and isinstance(tgt, str)): continue
if "attack-pattern--" in src and "attack-pattern--" in tgt and "subtechnique" in rtype:
sub_name = stix_to_name.get(src)
parent_name= stix_to_name.get(tgt)
if sub_name and parent_name:
parent_to_subs[parent_name].append(sub_name)
sub_to_parent[sub_name] = parent_name
tactics_flat = [t for lst in tech_df["tactics"] for t in lst]
tactic_order = [t.lower() for t in (TACTIC_ORDER_DEFAULT or sorted(set(tactics_flat), key=tactics_flat.index))]
log.info(f"Using tactic order (14): {', '.join(tactic_order)}")
def primary_tactic(name):
tacs = name_to_tactics.get(name, [])
return tacs[0].lower() if tacs else "unknown"
def is_subtech(name): return name in sub_to_parent
def parent_for(name): return sub_to_parent.get(name, "")
# =========================================================
# 2) Generate chains from campaigns (pre-LSTM)
# =========================================================
section("STEP 2) Generate tactic-ordered attack chains from Campaigns (pre-LSTM)")
def techniques_for_campaign(stix_campaign_id: str):
trefs = rel_df[
(rel_df["source ref"] == stix_campaign_id) &
(rel_df["target ref"].astype(str).str.startswith("attack-pattern--"))
]["target ref"].tolist()
names = [stix_to_name.get(tid) for tid in trefs if tid in stix_to_name]
expanded = set()
for n in names:
if n is None: continue
expanded.add(n)
for sub in parent_to_subs.get(n, []):
expanded.add(sub)
return list(expanded)
def bucket_by_tactic(names: list):
buckets = {t: [] for t in tactic_order}
for n in names:
for tac in name_to_tactics.get(n, []):
tkey = tac.lower()
if tkey in buckets:
buckets[tkey].append(n); break
return [buckets[t] for t in tactic_order if buckets[t]]
def sample_perms(bucket: list, k: int):
b = list(dict.fromkeys(bucket))
if len(b) <= 1: return [tuple(b)]
if len(b) <= 6: return list(islice(permutations(b), k))
out = set(); trials = 0
while len(out) < k and trials < k*20:
sh = b[:]; random.shuffle(sh); out.add(tuple(sh)); trials += 1
return list(out)
def generate_campaign_chains_for_row(row):
stix_id = row["STIX ID"]
names = techniques_for_campaign(stix_id)
if not names: return []
tactic_buckets = bucket_by_tactic(names)
if not tactic_buckets: return []
bucket_perms = [sample_perms(b, MAX_PERMS_PER_TACTIC) for b in tactic_buckets]
chains = []
for combo in islice(product(*bucket_perms), MAX_FULL_CHAINS_PER_CAMPAIGN):
flat = [x for group in combo for x in group]
if len(flat) >= MIN_CHAIN_LEN:
chains.append(flat)
return chains
t0 = time.time()
pre_chains, pre_camp_names, pre_camp_ids = [], [], []
for _, crow in tqdm(camp_df.iterrows(), total=len(camp_df), desc="Campaigns", leave=False):
cs = generate_campaign_chains_for_row(crow)
if cs:
pre_chains.extend(cs)
camp_name = crow.get("name","<unknown>")
camp_id = crow.get("ID", "")
pre_camp_names.extend([camp_name] * len(cs))
pre_camp_ids.extend([camp_id] * len(cs))
log.info(f"Chain generation took {time.time()-t0:.2f}s")
log.info(f"Generated {len(pre_chains):,} chains from {len(set(pre_camp_names))} campaigns.")
section("STEP 2a) Save pre-LSTM chain catalogs")
pre_long_rows, pre_wide_rows = [], []
for cid, (camp_name, camp_id, chain) in enumerate(zip(pre_camp_names, pre_camp_ids, pre_chains), start=1):
for step, name in enumerate(chain, start=1):
pre_long_rows.append({
"chain_id": cid,
"campaign_id": camp_id,
"campaign": camp_name,
"step": step,
"name": name,
"tactic": primary_tactic(name),
"is_subtech": int(is_subtech(name)),
"parent_technique": parent_for(name)
})
pre_wide_rows.append({
"chain_id": cid,
"campaign_id": camp_id,
"campaign": camp_name,
"chain_length": len(chain),
"chain": " -> ".join(chain)
})
pre_long_df = pd.DataFrame(pre_long_rows)
pre_wide_df = pd.DataFrame(pre_wide_rows)
pre_long_csv = os.path.join(OUT_DIR, "pre_lstm_chains_long.csv")
pre_wide_csv = os.path.join(OUT_DIR, "pre_lstm_chains_wide.csv")
pre_xlsx = os.path.join(OUT_DIR, "pre_lstm_chains.xlsx")
pre_long_df.to_csv(pre_long_csv, index=False)
pre_wide_df.to_csv(pre_wide_csv, index=False)
with pd.ExcelWriter(pre_xlsx, engine="xlsxwriter") as xlw:
pre_wide_df.to_excel(xlw, index=False, sheet_name="Chains")
pre_long_df.to_excel(xlw, index=False, sheet_name="Steps")
log.info(f"Saved:\n {pre_wide_csv}\n {pre_long_csv}\n {pre_xlsx}")
# =========================================================
# 3) Train LSTM on pre-LSTM chains + score (post-LSTM)
# =========================================================
section("STEP 3) LSTM training on pre-LSTM chains + post-LSTM scoring")
all_chains = list(pre_chains)
campaign_index = list(pre_camp_names)
campaign_ids_index = list(pre_camp_ids)
vocab = sorted({n for seq in all_chains for n in seq})
stoi = {s:i+1 for i,s in enumerate(vocab)}
itos = {i:s for s,i in stoi.items()}
def encode(seq): return torch.tensor([stoi[s] for s in seq if s in stoi], dtype=torch.long)
encoded = [encode(s) for s in all_chains if len(s) >= MIN_CHAIN_LEN]
def make_training_pairs(seqs, max_len=50):
X, Y = [], []
for t in seqs:
for i in range(1, len(t)):
start = max(0, i - max_len)
X.append(t[start:i]); Y.append(t[i])
return X, Y
X_list, Y_list = make_training_pairs(encoded, max_len=50)
log.info(f"Vocab size: {len(vocab):,} | Chains: {len(all_chains):,} | Training pairs: {len(X_list):,}")
class ChainDataset(Dataset):
def __init__(self, X, Y): self.X, self.Y = X, Y
def __len__(self): return len(self.X)
def __getitem__(self, i): return self.X[i], self.Y[i]
def collate(batch):
xs, ys = zip(*batch)
lengths = torch.tensor([len(x) for x in xs], dtype=torch.long)
padded = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=0)
return padded, torch.tensor(ys, dtype=torch.long), lengths
perm = np.random.permutation(len(X_list))
cut = int(0.9 * len(perm))
train_idx, val_idx = perm[:cut], perm[cut:]
train_ds = ChainDataset([X_list[i] for i in train_idx], [Y_list[i] for i in train_idx])
val_ds = ChainDataset([X_list[i] for i in val_idx], [Y_list[i] for i in val_idx])
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate)
val_loader = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate)
class NextStepLSTM(nn.Module):
def __init__(self, vocab_size, emb=EMBED_DIM, hid=HIDDEN_DIM, layers=NUM_LAYERS, dropout=DROPOUT):
super().__init__()
self.emb = nn.Embedding(vocab_size+1, emb, padding_idx=0)
self.lstm = nn.LSTM(emb, hid, num_layers=layers, dropout=dropout, batch_first=True)
self.proj = nn.Linear(hid, vocab_size+1)
def forward(self, x, lengths):
e = self.emb(x)
packed = nn.utils.rnn.pack_padded_sequence(e, lengths.cpu(), batch_first=True, enforce_sorted=False)
_, (hn, _) = self.lstm(packed)
last = hn[-1]
logits = self.proj(last)
return logits
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = NextStepLSTM(vocab_size=len(vocab)).to(device)
opt = torch.optim.AdamW(model.parameters(), lr=LR)
loss_fn = nn.CrossEntropyLoss()
log.info(f"Device: {device} | Params: {sum(p.numel() for p in model.parameters()):,}")
train_losses, val_losses, train_accs, val_accs = [], [], [], []
def run_epoch(loader, train=True, epoch_idx=0, total_epochs=0):
model.train(train)
total, correct, loss_sum = 0, 0, 0.0
pbar = tqdm(loader, desc=f"{'Train' if train else 'Val'} E{epoch_idx}/{total_epochs}", leave=False)
for step, (xb, yb, lb) in enumerate(pbar, start=1):
xb, yb, lb = xb.to(device), yb.to(device), lb.to(device)
logits = model(xb, lb)
loss = loss_fn(logits, yb)
if train:
opt.zero_grad(); loss.backward(); opt.step()
with torch.no_grad():
preds = logits.argmax(dim=-1)
correct += (preds == yb).sum().item()
total += yb.numel()
loss_sum += loss.item() * yb.numel()
if step % TRAIN_PROGRESS_EVERY == 0:
pbar.set_postfix(loss=f"{loss.item():.4f}", acc=f"{(correct/total):.3f}")
return (loss_sum/total if total else 0.0), (correct/total if total else 0.0)
t0 = time.time()
for epoch in range(1, EPOCHS+1):
tr_loss, tr_acc = run_epoch(train_loader, True, epoch, EPOCHS)
va_loss, va_acc = run_epoch(val_loader, False, epoch, EPOCHS)
train_losses.append(tr_loss); val_losses.append(va_loss)
train_accs.append(tr_acc); val_accs.append(va_acc)
log.info(f"Epoch {epoch:02d}/{EPOCHS} | train loss {tr_loss:.4f} acc {tr_acc:.3f} | val loss {va_loss:.4f} acc {va_acc:.3f}")
log.info(f"Training complete took {time.time()-t0:.2f}s")
@torch.no_grad()
def chain_prob_lstm(seq_names: List[str]) -> float:
logp = 0.0
for i in range(1, len(seq_names)):
prefix = seq_names[:i]
target = seq_names[i]
prefix_ids = [stoi[s] for s in prefix if s in stoi]
target_id = stoi.get(target, None)
if len(prefix_ids) == 0 or target_id is None:
logp += math.log(MARKOV_MIN_PROB); continue
t = torch.tensor(prefix_ids, dtype=torch.long).unsqueeze(0).to(device)
l = torch.tensor([len(prefix_ids)], dtype=torch.long).to(device)
logits = model(t, l).squeeze(0)
probs = torch.softmax(logits, dim=-1)
p = max(probs[target_id].item(), MARKOV_MIN_PROB)
logp += math.log(p)
return math.exp(logp)
def geometric_mean_prob(ps):
if not ps: return 0.0
return math.exp(sum(math.log(max(p,MARKOV_MIN_PROB)) for p in ps) / len(ps))
section("STEP 3a) Score & save POST-LSTM chains (probability + per-step risk)")
EPSS = defaultdict(lambda: None)
CAPEC_LIKELIHOOD = defaultdict(lambda: None)
KEV_FLAG = defaultdict(lambda: 0)
DETECTION_COVERAGE = defaultdict(lambda: 0.0)
def try_load_csv_map(path, key_col_candidates, val_col_candidates, cast=float, default=None):
if not path or not os.path.exists(path): return {}
df = pd.read_csv(path)
cols = {c.lower(): c for c in df.columns}
key_col = next((cols[c] for c in [k.lower() for k in key_col_candidates] if c in cols), None)
val_col = next((cols[c] for c in [v.lower() for v in val_col_candidates] if c in cols), None)
if key_col is None or val_col is None:
log.warning(f"Could not find expected columns in {path}. Skipping.")
return {}
out = {}
for _, r in df.iterrows():
k = str(r[key_col]).strip()
v = r[val_col]
try: out[k] = cast(v)
except Exception: out[k] = default
return out
for k,v in try_load_csv_map(EPSS_CSV, ["technique","name"], ["epss","score"], float, None).items(): EPSS[k]=v
for k,v in try_load_csv_map(KEV_CSV, ["technique","name"], ["kev","is_kev","flag"], int, 0).items(): KEV_FLAG[k]=v
for k,v in try_load_csv_map(DETECTION_CSV, ["technique","name"], ["coverage","detect","d3fend"], float, 0.0).items(): DETECTION_COVERAGE[k]=v
def step_likelihood(name, modeled_next_prob=None):
base = None
if EPSS[name] is not None:
base = EPSS[name]
elif CAPEC_LIKELIHOOD[name] is not None:
base = CAPEC_LIKELIHOOD[name]
elif modeled_next_prob is not None:
base = modeled_next_prob
else:
base = 0.1
if KEV_FLAG[name]:
base = min(1.0, base + 0.1)
return float(base)
def step_detectability(name):
cov = min(max(float(DETECTION_COVERAGE[name]), 0.0), 1.0)
return 1.0 - cov
@torch.no_grad()
def score_chain(seq_names, octave_impact_0_10=OCTAVE_IMPACT_0_10_DEFAULT):
modeled_next = []
for i in range(1, len(seq_names)):
prefix = seq_names[:i]
target = seq_names[i]
prefix_ids = [stoi[s] for s in prefix if s in stoi]
target_id = stoi.get(target, None)
if len(prefix_ids) == 0 or target_id is None:
modeled_next.append(None); continue
t = torch.tensor(prefix_ids, dtype=torch.long).unsqueeze(0).to(device)
l = torch.tensor([len(prefix_ids)], dtype=torch.long).to(device)
logits = model(t, l).squeeze(0)
probs = torch.softmax(logits, dim=-1).cpu().numpy()
modeled_next.append(float(probs[target_id]))
per_step_scores, per_step_likes, per_step_detect = [], [], []
for i, name in enumerate(seq_names):
modeled_prob = modeled_next[i-1] if i > 0 and i-1 < len(modeled_next) else None
like = step_likelihood(name, modeled_prob)
det = step_detectability(name)
per_step_likes.append(like); per_step_detect.append(det); per_step_scores.append(like*det)
chain_like = max(MARKOV_MIN_PROB, min(1.0, geometric_mean_prob([max(p,MARKOV_MIN_PROB) for p in per_step_likes if p is not None and p > 0.0])))
final_risk = 10.0 * chain_like * (float(octave_impact_0_10) / 10.0)
final_risk = min(10.0, final_risk)
return {
"per_step_scores": per_step_scores,
"per_step_likelihoods": per_step_likes,
"per_step_detectability": per_step_detect,
"chain_geomean_likelihood": chain_like,
"octave_impact_0_10": float(octave_impact_0_10),
"final_risk_score_0_10": float(final_risk)
}
post_rows = []
for cid, (camp_id, camp_name, chain) in enumerate(zip(campaign_ids_index, campaign_index, all_chains), start=1):
prob_lstm = chain_prob_lstm(chain)
rs = score_chain(chain, OCTAVE_IMPACT_0_10_DEFAULT)
post_rows.append({
"chain_id": cid,
"campaign_id": camp_id,
"campaign": camp_name,
"chain_length": len(chain),
"chain": " -> ".join(chain),
"lstm_chain_probability": prob_lstm,
"chain_geomean_likelihood": rs["chain_geomean_likelihood"],
"final_risk_score_0_10": rs["final_risk_score_0_10"],
"per_step_likelihoods": json.dumps(rs["per_step_likelihoods"]),
"per_step_detectability": json.dumps(rs["per_step_detectability"]),
"per_step_scores": json.dumps(rs["per_step_scores"]),
"octave_impact_0_10": rs["octave_impact_0_10"]
})
post_df = pd.DataFrame(post_rows)
post_csv = os.path.join(OUT_DIR, "post_lstm_chains_scored.csv")
post_xlsx = os.path.join(OUT_DIR, "post_lstm_chains_scored.xlsx")
post_df.to_csv(post_csv, index=False)
with pd.ExcelWriter(post_xlsx, engine="xlsxwriter") as xlw:
post_df.to_excel(xlw, index=False, sheet_name="Chains+Scores")
post_sorted = post_df.sort_values(["final_risk_score_0_10","lstm_chain_probability"], ascending=[False,False])
post_sorted.to_csv(os.path.join(OUT_DIR,"post_lstm_chains_scored_sorted.csv"), index=False)
log.info(f"Saved POST-LSTM catalogs:\n {post_csv}\n {post_xlsx}")
section("STEP 3b) Terminal: Top chains by probability and risk")
if not post_df.empty:
top_prob = post_df.sort_values("lstm_chain_probability", ascending=False).head(5)
top_risk = post_df.sort_values("final_risk_score_0_10", ascending=False).head(5)
log.info("\nTop-5 by Probability:")
for _, r in top_prob.iterrows():
log.info(f" P={r['lstm_chain_probability']:.3e} | Len={int(r['chain_length'])} | Camp={r['campaign']} | {r['chain'][:140]}")
log.info("\nTop-5 by Risk:")
for _, r in top_risk.iterrows():
log.info(f" RISK={r['final_risk_score_0_10']:.2f} | like={r['chain_geomean_likelihood']:.3f} | Camp={r['campaign']} | {r['chain'][:140]}")
section("STEP 3c) Visualizations")
if len(train_losses):
plt.figure(); plt.plot(range(1,EPOCHS+1), train_losses, marker="o", label="train loss")
plt.plot(range(1,EPOCHS+1), val_losses, marker="o", label="val loss")
plt.xlabel("Epoch"); plt.ylabel("Loss"); plt.title("LSTM Loss"); plt.legend()
save_fig(os.path.join(OUT_DIR, "plot_lstm_loss.png"))
if len(train_accs):
plt.figure(); plt.plot(range(1,EPOCHS+1), train_accs, marker="o", label="train acc")
plt.plot(range(1,EPOCHS+1), val_accs, marker="o", label="val acc")
plt.xlabel("Epoch"); plt.ylabel("Accuracy"); plt.title("LSTM Accuracy"); plt.legend()
save_fig(os.path.join(OUT_DIR, "plot_lstm_acc.png"))
# =========================================================
# 4) Ingest Unit42 + Attack Flow
# =========================================================
section("STEP 4) Ingest Unit42 Playbooks + Attack Flow JSON -> chains")
ATTACK_ID_TO_NAME = dict(zip(tech_df["ID"].astype(str), tech_df["name"]))
external_entries = []
if os.path.isdir(UNIT42_REPO_DIR):
external_entries.extend(load_unit42_sequences(UNIT42_REPO_DIR, tactic_order, ATTACK_ID_TO_NAME))
else:
log.warning(f"Unit42 path not found: {UNIT42_REPO_DIR}")
if os.path.isdir(ATTACK_FLOW_STIX_DIR):
external_entries.extend(load_attack_flow_sequences(ATTACK_FLOW_STIX_DIR, ATTACK_ID_TO_NAME))
else:
log.warning(f"Attack Flow dir not found: {ATTACK_FLOW_STIX_DIR}")
if external_entries:
ext_rows = [{"source": src, "document": doc, "chain_length": len(seq), "chain": " -> ".join(seq)} for (src, doc, seq) in external_entries]
ext_df = pd.DataFrame(ext_rows)
ext_csv = os.path.join(OUT_DIR, "external_sequences.csv")
ext_df.to_csv(ext_csv, index=False)
log.info(f"Saved external sequences CSV: {ext_csv}")
else:
log.warning("No external sequences found (check local paths).")
# =========================================================
# 5) Train Markov
# =========================================================
section("STEP 5) Train first-order Markov model on JSON-derived chains (pre-Markov)")
json_only = [seq for (_,_,seq) in external_entries]
log.info(f"JSON-derived chains (Unit42 + Attack Flow): {len(json_only):,}")
start_prob, next_prob, start_states, unique_transitions = train_markov_from_sequences(json_only)
markov_model_path = os.path.join(OUT_DIR, "markov_model.json")
with open(markov_model_path, "w", encoding="utf-8") as f:
json.dump({"start_prob": start_prob, "next_prob": next_prob}, f, indent=2)
log.info(f"Saved Markov model: {markov_model_path}")
log.info(f"Markov states: {start_states} | Unique transitions: {unique_transitions:,}")
top_markov = generate_markov_top_sequences(start_prob, next_prob, top_starts=30, beam=MARKOV_BEAM, topk=MARKOV_TOPK_NEXT, max_len=MARKOV_MAX_LEN)
mk_top_rows = [{"chain_length": len(seq), "chain": " -> ".join(seq), "markov_chain_probability": p} for (seq,p) in top_markov[:300]]
mk_top_df = pd.DataFrame(mk_top_rows)
mk_top_csv = os.path.join(OUT_DIR, "markov_top_sequences.csv")
mk_top_xlsx= os.path.join(OUT_DIR, "markov_top_sequences.xlsx")
mk_top_df.to_csv(mk_top_csv, index=False)
with pd.ExcelWriter(mk_top_xlsx, engine="xlsxwriter") as xlw:
mk_top_df.to_excel(xlw, index=False, sheet_name="MarkovTop")
log.info("Saved pre-Markov predictions:\n " + mk_top_csv + "\n " + mk_top_xlsx)
# =========================================================
# 6) Validate Markov
# =========================================================
section("STEP 6) Validate Markov with POST-LSTM chains (probabilities out of 1)")
val_rows = []
for cid, (camp, chain) in enumerate(zip(campaign_index, all_chains), start=1):
mk_p = markov_chain_prob(chain, start_prob, next_prob)
trans = list(zip(chain[:-1], chain[1:]))
cov = (sum(1 for (a,b) in trans if b in next_prob.get(a, {})) / len(trans)) if trans else 0.0
val_rows.append({
"chain_id": cid,
"campaign": camp,
"chain_length": len(chain),
"coverage": round(cov, 4),
"markov_chain_probability": mk_p,
"lstm_chain_probability": chain_prob_lstm(chain),
"chain": " -> ".join(chain)
})
val_df = pd.DataFrame(val_rows)
val_csv = os.path.join(OUT_DIR, "validation_lstm_vs_markov.csv")
val_xlsx= os.path.join(OUT_DIR, "validation_lstm_vs_markov.xlsx")
val_df.to_csv(val_csv, index=False)
with pd.ExcelWriter(val_xlsx, engine="xlsxwriter") as xlw:
val_df.to_excel(xlw, index=False, sheet_name="Validation")
log.info("Saved LSTM vs Markov validation tables:\n " + val_csv + "\n " + val_xlsx)
# =========================================================
# 6b) Post-Markov expansions
# =========================================================
section("STEP 6b) Post-Markov expansions (streaming, memory-safe)")
post_mk_stream_csv = os.path.join(OUT_DIR, "post_markov_expansions_stream.csv")
stream_cols = [
"orig_chain_id","orig_prefix","chain_length","chain",
"markov_chain_probability","lstm_chain_probability",
"chain_geomean_likelihood","final_risk_score_0_10"
]
if SKIP_POST_MARKOV_EXPANSION:
log.warning("SKIP_POST_MARKOV_EXPANSION=1 -> skipping Step 6b expansions.")
else:
total_to_expand = min(len(all_chains), POST_MARKOV_MAX_CHAINS)
log.info(f"Expanding Markov from {total_to_expand} LSTM chains "
f"(seed len={POST_MARKOV_EXPAND_SEED_LEN}, beam={MARKOV_BEAM}, topk={MARKOV_TOPK_NEXT}).")
with open(post_mk_stream_csv, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=stream_cols)
writer.writeheader()
rows_written = 0
t_start = time.time()
for chain_id, chain in enumerate(all_chains[:total_to_expand], start=1):
if ABORT[0]:
log.warning("Abort requested; stopping expansion loop.")
break
mk_prob = markov_chain_prob(chain, start_prob, next_prob)
sl = POST_MARKOV_EXPAND_SEED_LEN
seed = chain[:sl] if len(chain) >= sl else chain[:]
expansions = expand_with_markov(
seed, start_prob, next_prob,
max_len=min(MARKOV_MAX_LEN, len(chain)+5),
beam=MARKOV_BEAM, topk=MARKOV_TOPK_NEXT
)
candidates = [(chain, mk_prob)] + expansions
with open(post_mk_stream_csv, "a", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=stream_cols)
for seq, p_mk in candidates:
rs = score_chain(seq, OCTAVE_IMPACT_0_10_DEFAULT)
writer.writerow({
"orig_chain_id": chain_id,
"orig_prefix": " -> ".join(seed),
"chain_length": len(seq),
"chain": " -> ".join(seq),
"markov_chain_probability": float(p_mk),
"lstm_chain_probability": float(chain_prob_lstm(seq)),
"chain_geomean_likelihood": rs["chain_geomean_likelihood"],
"final_risk_score_0_10": rs["final_risk_score_0_10"]
})
rows_written += 1
if chain_id % POST_MARKOV_LOG_EVERY == 0:
elapsed = time.time() - t_start
log.info(f" Expanded {chain_id}/{total_to_expand} chains; wrote {rows_written} rows in {elapsed:.1f}s")
log.info(f"Post-Markov streaming expansions complete. Total rows: {rows_written}. "
f"CSV: {post_mk_stream_csv}")
if os.path.exists(post_mk_stream_csv):
post_mk_df = pd.read_csv(post_mk_stream_csv)
else:
post_mk_df = pd.DataFrame(columns=stream_cols)
# =========================================================
# 7) Score Markov sequences
# =========================================================
section("STEP 7) Score Markov top sequences with per-step probabilities and per-step risk (0–10)")
if not post_mk_df.empty:
post_mk_sorted = post_mk_df.sort_values(
["final_risk_score_0_10","markov_chain_probability"],
ascending=[False, False]
)
else:
post_mk_sorted = post_mk_df.copy()
mk_scored_csv = os.path.join(OUT_DIR, "markov_top_sequences_scored.csv")
mk_scored_xlsx = os.path.join(OUT_DIR, "markov_top_sequences_scored.xlsx")
post_mk_sorted.to_csv(mk_scored_csv, index=False)
with pd.ExcelWriter(mk_scored_xlsx, engine="xlsxwriter") as xlw:
post_mk_sorted.to_excel(xlw, index=False, sheet_name="PostMarkov+Scores")
log.info("Saved post-Markov sequences with probability + risk:\n " + mk_scored_csv + "\n " + mk_scored_xlsx)
if not post_mk_sorted.empty:
plt.figure()
x = post_mk_sorted["chain_geomean_likelihood"].values
y = post_mk_sorted["final_risk_score_0_10"].values
plt.scatter(x, y, s=10)
plt.xlabel("Geometric-Mean Likelihood"); plt.ylabel("Final Risk (0–10)")
plt.title("Risk vs Likelihood (Post-Markov)")
save_fig(os.path.join(OUT_DIR, "plot_risk_vs_likelihood_post_markov.png"))
# =========================================================
# 8) Dataset mapping
# =========================================================
section("STEP 8) Dataset mapping -> chain filtering + scoring")
LABEL_TO_ATTACK = {
"Benign": [],
"Port Scan": ["Network Service Discovery"],
"OS Scan": ["System Information Discovery"],
"Ping Sweep":["Network Service Discovery"],
"HTTP Flood":["Network DoS"],
"UDP Flood": ["Network DoS"],
"TCP SYN": ["Network DoS"],
"Slowloris": ["Application Layer Protocol"],
"Dictionary Attack": ["Brute Force"],
"ARP Spoofing": ["Adversary-in-the-Middle"],
"DoS": ["Network DoS"],
"PortScan": ["Network Service Discovery"],
"DDoS": ["Network DoS"],
"BruteForce": ["Brute Force"],
"WebAttack": ["Exploitation for Client Execution"],
"Botnet": ["Command and Control"],
"Infiltration": ["Exploitation for Privilege Escalation"]
}
def load_dataset_labels(csv_path: str, label_col: str) -> List[str]:
if not csv_path or not os.path.exists(csv_path): return []
df = pd.read_csv(csv_path)
if label_col not in df.columns:
alt = [c for c in df.columns if c.lower() == label_col.lower()]
if not alt: raise ValueError(f"Label column '{label_col}' not found")
label_col = alt[0]
return list(df[label_col].astype(str).values)
dataset_labels = load_dataset_labels(DATASET_PATH, DATASET_LABEL)
log.info(f"Loaded dataset labels: {len(dataset_labels)} rows")
def chains_containing_any(attacks: List[str], universe_df: pd.DataFrame, chain_col="chain") -> pd.DataFrame:
if not attacks: return universe_df.copy()
pat = r"|".join(re.escape(a) for a in attacks)
return universe_df[universe_df[chain_col].str.contains(pat, na=False)]
universe = post_mk_sorted.copy()
universe["rank"] = range(1, len(universe)+1)
filtered_rows = []
if dataset_labels:
label_counts = Counter(dataset_labels)
for label, freq in label_counts.items():
attacks = LABEL_TO_ATTACK.get(label, [])
sub = chains_containing_any(attacks, universe)
sub = sub.copy()
sub["dataset_label"] = label
sub["label_frequency"] = freq
filtered_rows.append(sub)
if filtered_rows:
result_df = pd.concat(filtered_rows, ignore_index=True)
lbl_csv = os.path.join(OUT_DIR, "dataset_label_filtered_chains.csv")
result_df.to_csv(lbl_csv, index=False)
log.info(f"Saved dataset-filtered chains: {lbl_csv}")
tops = result_df.sort_values(
["dataset_label","final_risk_score_0_10","markov_chain_probability"],
ascending=[True, False, False]