Indicator lifecycle management tracks IOCs from initial discovery through validation, enrichment, deployment, monitoring, and eventual retirement. This skill covers implementing systematic processes f
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| name | performing-indicator-lifecycle-management |
| description | Indicator lifecycle management tracks IOCs from initial discovery through validation, enrichment, deployment, monitoring, and eventual retirement. This skill covers implementing systematic processes f |
| domain | cybersecurity |
| subdomain | threat-intelligence |
| tags | ["threat-intelligence","cti","ioc","mitre-attack","stix","indicator-lifecycle","ioc-management"] |
| version | 1.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | ["ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02"] |
| mitre_attack | ["T1591","T1592","T1593","T1589"] |
Indicator lifecycle management tracks IOCs from initial discovery through validation, enrichment, deployment, monitoring, and eventual retirement. This skill covers implementing systematic processes for IOC quality assessment, aging policies, confidence scoring decay, false positive tracking, hit-rate monitoring, and automated expiration to maintain a high-quality, actionable indicator database that minimizes analyst fatigue and maximizes detection efficacy.
pymisp, requests, stix2 librariesIndicator confidence decreases over time as adversaries rotate infrastructure. A time-based decay function reduces confidence scores automatically, ensuring old indicators do not generate excessive alerts. Typical half-life: IP addresses (30 days), domains (90 days), file hashes (365 days).
from datetime import datetime, timedelta
from enum import Enum
class IOCState(Enum):
DISCOVERED = "discovered"
VALIDATED = "validated"
ENRICHED = "enriched"
DEPLOYED = "deployed"
MONITORING = "monitoring"
UNDER_REVIEW = "under_review"
RETIRED = "retired"
class IOCLifecycle:
def __init__(self, ioc_type, value, source, initial_confidence=50):
self.ioc_type = ioc_type
self.value = value
self.source = source
self.confidence = initial_confidence
self.state = IOCState.DISCOVERED
self.created = datetime.utcnow()
self.last_updated = datetime.utcnow()
self.last_seen = None
self.hit_count = 0
self.false_positive_count = 0
self.history = [{"state": "discovered", "timestamp": self.created.isoformat()}]
def transition(self, new_state: IOCState, reason=""):
self.state = new_state
self.last_updated = datetime.utcnow()
self.history.append({
"state": new_state.value,
"timestamp": self.last_updated.isoformat(),
"reason": reason,
})
def apply_decay(self):
"""Apply confidence decay based on IOC type half-life."""
half_lives = {"ip": 30, "domain": 90, "hash": 365, "url": 60}
half_life = half_lives.get(self.ioc_type, 90)
age_days = (datetime.utcnow() - self.created).days
decay_factor = 0.5 ** (age_days / half_life)
self.confidence = max(0, int(self.confidence * decay_factor))
def record_hit(self, is_true_positive=True):
self.hit_count += 1
self.last_seen = datetime.utcnow()
if not is_true_positive:
self.false_positive_count += 1
if self.false_positive_count > 3:
self.transition(IOCState.UNDER_REVIEW, "Excessive false positives")
def should_retire(self):
max_ages = {"ip": 90, "domain": 180, "hash": 730, "url": 120}
max_age = max_ages.get(self.ioc_type, 180)
age_days = (datetime.utcnow() - self.created).days
return age_days > max_age and self.hit_count == 0