Zero-Day Defense: Signature-Less Detection with Wazuh#

1
# Zero-Day Detection Framework
2
class ZeroDayBehavioralEngine:
3
    def __init__(self):
4
        self.detection_methods = {
5
            'anomaly_detection': 0.25,
6
            'heuristic_analysis': 0.20,
7
            'sandboxing': 0.15,
8
            'machine_learning': 0.30,
9
            'threat_hunting': 0.10
10
        }
11
        self.baseline_period = 30  # days
12
        self.sensitivity_threshold = 0.75
13

14
    def detect_zero_day(self, event):
15
        """Multi-method zero-day detection"""
16
        detection_scores = {}
17

18
        # Anomaly Detection
19
        detection_scores['anomaly'] = self.detect_anomalies(event)
20

21
        # Heuristic Analysis
22
        detection_scores['heuristic'] = self.apply_heuristics(event)
23

24
        # Behavioral Analysis
25
        detection_scores['behavioral'] = self.analyze_behavior(event)
26

27
        # ML Prediction
28
        detection_scores['ml'] = self.ml_predict(event)
29

30
        # Calculate weighted score
31
        final_score = sum(
32
            score * self.detection_methods.get(method, 0)
33
            for method, score in detection_scores.items()
34
        )
35

36
        return {
37
            'is_zero_day': final_score > self.sensitivity_threshold,
38
            'confidence': final_score,
39
            'detection_methods': detection_scores
40
        }

1
<!-- Process Hollowing Detection -->
2
<rule id="500001" level="12">
3
  <if_sid>61603</if_sid>
4
  <field name="win.eventdata.parentImage" type="pcre2">\\(explorer|svchost|winlogon)\.exe$</field>
5
  <field name="win.eventdata.image" type="pcre2">\\(powershell|cmd|wscript|cscript)\.exe$</field>
6
  <field name="win.eventdata.commandLine" type="pcre2" negate="yes">\\system32\\</field>
7
  <description>Zero-Day: Suspicious process spawned from system process</description>
8
  <group>zero_day,process_injection</group>
9
  <mitre>
10
    <id>T1055</id>
11
  </mitre>
12
</rule>
13

14
<!-- Unusual Process Relationships -->
15
<rule id="500002" level="11">
16
  <if_sid>61603</if_sid>
17
  <field name="win.eventdata.image" type="pcre2">\\(notepad|calc|mspaint)\.exe$</field>
18
  <field name="win.eventdata.commandLine" type="pcre2">cmd|powershell|wmic</field>
19
  <description>Zero-Day: Benign process executing suspicious commands</description>
20
  <group>zero_day,process_anomaly</group>
21
</rule>
22

23
<!-- Process with Suspicious Arguments -->
24
<rule id="500003" level="13">
25
  <if_sid>61603</if_sid>
26
  <field name="win.eventdata.commandLine" type="pcre2">
27
    (reflection\.assembly|frombase64string|downloadstring|invoke-expression|
28
    bypass|noprofile|windowstyle\s+hidden|executionpolicy)
29
  </field>
30
  <description>Zero-Day: Process launched with obfuscation/evasion techniques</description>
31
  <group>zero_day,evasion</group>
32
  <mitre>
33
    <id>T1027</id>
34
  </mitre>
35
</rule>

1
<!-- Abnormal Memory Allocation -->
2
<rule id="500010" level="12">
3
  <if_sid>61617</if_sid>
4
  <field name="win.eventdata.grantedAccess">^0x1F3FFF$|^0x1FFFFF$</field>
5
  <field name="win.eventdata.sourceImage" negate="yes" type="pcre2">
6
    (debugger|procmon|processhacker)
7
  </field>
8
  <description>Zero-Day: Process accessing another process with full control</description>
9
  <group>zero_day,memory_manipulation</group>
10
</rule>
11

12
<!-- Code Injection Patterns -->
13
<rule id="500011" level="14">
14
  <if_sid>61617</if_sid>
15
  <field name="win.eventdata.callTrace" type="pcre2">
16
    (kernelbase\.dll\+|ntdll\.dll\+).*\|unknown
17
  </field>
18
  <description>Zero-Day: Potential code injection detected via call trace</description>
19
  <group>zero_day,code_injection</group>
20
  <mitre>
21
    <id>T1055.001</id>
22
  </mitre>
23
</rule>

1
<!-- Beaconing Behavior -->
2
<rule id="500020" level="10">
3
  <if_sid>86001</if_sid>
4
  <field name="data.dest_port">^(80|443|8080|8443)$</field>
5
  <match>constant_interval</match>
6
  <description>Zero-Day: Regular interval network beaconing detected</description>
7
  <group>zero_day,c2_communication</group>
8
</rule>
9

10
<!-- DGA Domain Detection -->
11
<rule id="500021" level="12">
12
  <if_sid>12</if_sid>
13
  <field name="dns.query" type="pcre2">
14
    ^[a-z0-9]{16,}\.(com|net|org|info|biz)$
15
  </field>
16
  <description>Zero-Day: Possible DGA (Domain Generation Algorithm) detected</description>
17
  <group>zero_day,dga</group>
18
  <mitre>
19
    <id>T1568.002</id>
20
  </mitre>
21
</rule>
22

23
<!-- Unusual Protocol Usage -->
24
<rule id="500022" level="11">
25
  <if_sid>86001</if_sid>
26
  <field name="data.protocol">^(ICMP|DNS)$</field>
27
  <field name="data.payload_size" compare=">=">100</field>
28
  <description>Zero-Day: Large payload in unusual protocol - possible tunneling</description>
29
  <group>zero_day,protocol_tunneling</group>
30
</rule>

1
# Network behavior profiling
2
class NetworkAnomalyDetector:
3
    def __init__(self):
4
        self.baseline = {}
5
        self.anomaly_threshold = 3.5  # Z-score
6

7
    def analyze_traffic(self, flow):
8
        """Detect anomalous network behavior"""
9
        anomalies = []
10

11
        # Analyze packet size distribution
12
        packet_sizes = [p['size'] for p in flow['packets']]
13
        size_anomaly = self.detect_size_anomaly(packet_sizes)
14
        if size_anomaly:
15
            anomalies.append({
16
                'type': 'packet_size',
17
                'score': size_anomaly,
18
                'details': 'Unusual packet size distribution'
19
            })
20

21
        # Analyze timing patterns
22
        intervals = self.calculate_intervals(flow['packets'])
23
        timing_anomaly = self.detect_timing_anomaly(intervals)
24
        if timing_anomaly:
25
            anomalies.append({
26
                'type': 'timing',
27
                'score': timing_anomaly,
28
                'details': 'Suspicious timing pattern detected'
29
            })
30

31
        # Protocol violation detection
32
        protocol_violations = self.check_protocol_compliance(flow)
33
        if protocol_violations:
34
            anomalies.append({
35
                'type': 'protocol',
36
                'score': 0.8,
37
                'details': protocol_violations
38
            })
39

40
        return anomalies
41

42
    def detect_beaconing(self, connections):
43
        """Identify C2 beaconing patterns"""
44
        from scipy import stats
45

46
        # Group connections by destination
47
        dest_groups = {}
48
        for conn in connections:
49
            dest = conn['dest_ip']
50
            if dest not in dest_groups:
51
                dest_groups[dest] = []
52
            dest_groups[dest].append(conn['timestamp'])
53

54
        beacons = []
55
        for dest, timestamps in dest_groups.items():
56
            if len(timestamps) < 5:
57
                continue
58

59
            # Calculate intervals
60
            intervals = [
61
                timestamps[i+1] - timestamps[i]
62
                for i in range(len(timestamps)-1)
63
            ]
64

65
            # Check for regularity
66
            cv = stats.variation(intervals)  # Coefficient of variation
67
            if cv < 0.2:  # Low variation indicates beaconing
68
                beacons.append({
69
                    'destination': dest,
70
                    'interval': np.mean(intervals),
71
                    'confidence': 1 - cv,
72
                    'sample_size': len(timestamps)
73
                })
74

75
        return beacons

1
<!-- Suspicious File Creation -->
2
<rule id="500030" level="11">
3
  <if_sid>61651</if_sid>
4
  <field name="win.eventdata.targetFilename" type="pcre2">
5
    \.(exe|dll|scr|bat|cmd|ps1)$
6
  </field>
7
  <field name="win.eventdata.targetFilename" type="pcre2">
8
    \\(temp|tmp|appdata\\local\\temp|programdata)\\
9
  </field>
10
  <description>Zero-Day: Executable created in temporary directory</description>
11
  <group>zero_day,file_creation</group>
12
</rule>
13

14
<!-- File Type Mismatch -->
15
<rule id="500031" level="12">
16
  <if_sid>61651</if_sid>
17
  <field name="file.magic_bytes">MZ</field>
18
  <field name="win.eventdata.targetFilename" type="pcre2" negate="yes">
19
    \.(exe|dll|sys|scr)$
20
  </field>
21
  <description>Zero-Day: PE file with non-executable extension</description>
22
  <group>zero_day,file_masquerading</group>
23
  <mitre>
24
    <id>T1036</id>
25
  </mitre>
26
</rule>
27

28
<!-- Entropy-Based Detection -->
29
<rule id="500032" level="13">
30
  <if_sid>554</if_sid>
31
  <field name="file.entropy" compare=">=">7.5</field>
32
  <field name="file.size" compare=">=">10240</field>
33
  <description>Zero-Day: High entropy file detected - possible encryption/packing</description>
34
  <group>zero_day,packed_malware</group>
35
</rule>

1
<!-- Unusual Registry Persistence -->
2
<rule id="500040" level="12">
3
  <if_sid>92211</if_sid>
4
  <field name="win.eventdata.eventType">^SetValue$</field>
5
  <field name="win.eventdata.targetObject" type="pcre2">
6
    \\(run|runonce|explorer\\shell|winlogon\\shell|image file execution options)
7
  </field>
8
  <field name="win.eventdata.details" type="pcre2">
9
    (wscript|cscript|mshta|rundll32|regsvr32).*http
10
  </field>
11
  <description>Zero-Day: Suspicious registry persistence with download capability</description>
12
  <group>zero_day,persistence</group>
13
  <mitre>
14
    <id>T1547</id>
15
  </mitre>
16
</rule>
17

18
<!-- COM Hijacking Detection -->
19
<rule id="500041" level="11">
20
  <if_sid>92211</if_sid>
21
  <field name="win.eventdata.targetObject" type="pcre2">
22
    \\CLSID\\.*\\InprocServer32
23
  </field>
24
  <field name="win.eventdata.image" negate="yes">installer|msiexec|setup</field>
25
  <description>Zero-Day: Potential COM object hijacking detected</description>
26
  <group>zero_day,com_hijack</group>
27
</rule>

1
# API call sequence analysis
2
class APISequenceAnalyzer:
3
    def __init__(self):
4
        self.suspicious_sequences = [
5
            # Process injection
6
            ['OpenProcess', 'VirtualAllocEx', 'WriteProcessMemory',
7
             'CreateRemoteThread'],
8
            # Keylogging
9
            ['SetWindowsHookEx', 'GetKeyState', 'GetAsyncKeyState'],
10
            # Screen capture
11
            ['CreateCompatibleDC', 'BitBlt', 'GetDIBits'],
12
            # Credential theft
13
            ['LsaOpenPolicy', 'LsaQueryInformationPolicy',
14
             'SamConnect', 'SamOpenDomain']
15
        ]
16

17
    def analyze_api_calls(self, process_apis):
18
        """Detect suspicious API call patterns"""
19
        detected_patterns = []
20

21
        # Convert API calls to sequence
22
        api_sequence = [call['api_name'] for call in process_apis]
23

24
        # Check for known malicious sequences
25
        for pattern in self.suspicious_sequences:
26
            if self.sequence_contains(api_sequence, pattern):
27
                detected_patterns.append({
28
                    'pattern': pattern,
29
                    'category': self.categorize_pattern(pattern),
30
                    'severity': 'HIGH'
31
                })
32

33
        # Detect unusual API velocity
34
        api_velocity = self.calculate_api_velocity(process_apis)
35
        if api_velocity > 1000:  # APIs per second
36
            detected_patterns.append({
37
                'pattern': 'High API velocity',
38
                'category': 'Evasion',
39
                'severity': 'MEDIUM',
40
                'velocity': api_velocity
41
            })
42

43
        return detected_patterns

1
# ML-based zero-day detection
2
import numpy as np
3
from sklearn.ensemble import RandomForestClassifier, IsolationForest
4
from sklearn.neural_network import MLPClassifier
5
from sklearn.preprocessing import StandardScaler
6

7
class ZeroDayMLEnsemble:
8
    def __init__(self):
9
        self.models = {
10
            'random_forest': RandomForestClassifier(
11
                n_estimators=200,
12
                max_depth=20,
13
                random_state=42
14
            ),
15
            'isolation_forest': IsolationForest(
16
                contamination=0.001,
17
                random_state=42
18
            ),
19
            'neural_network': MLPClassifier(
20
                hidden_layer_sizes=(128, 64, 32),
21
                activation='relu',
22
                random_state=42
23
            )
24
        }
25
        self.scaler = StandardScaler()
26
        self.feature_extractors = {
27
            'static': self.extract_static_features,
28
            'dynamic': self.extract_dynamic_features,
29
            'network': self.extract_network_features
30
        }
31

32
    def extract_features(self, sample):
33
        """Extract comprehensive features for zero-day detection"""
34
        features = []
35

36
        # Static analysis features
37
        static = self.extract_static_features(sample)
38
        features.extend(static)
39

40
        # Dynamic behavior features
41
        if 'behavior' in sample:
42
            dynamic = self.extract_dynamic_features(sample['behavior'])
43
            features.extend(dynamic)
44

45
        # Network features
46
        if 'network' in sample:
47
            network = self.extract_network_features(sample['network'])
48
            features.extend(network)
49

50
        return np.array(features)
51

52
    def extract_static_features(self, sample):
53
        """Extract static file features"""
54
        return [
55
            sample.get('file_size', 0),
56
            sample.get('entropy', 0),
57
            len(sample.get('imports', [])),
58
            len(sample.get('exports', [])),
59
            sample.get('has_digital_signature', 0),
60
            sample.get('is_packed', 0),
61
            sample.get('unusual_sections', 0),
62
            sample.get('resource_entropy', 0)
63
        ]
64

65
    def predict_zero_day(self, sample):
66
        """Ensemble prediction for zero-day detection"""
67
        features = self.extract_features(sample)
68
        features_scaled = self.scaler.transform([features])
69

70
        predictions = {}
71
        confidence_scores = {}
72

73
        # Get predictions from each model
74
        for name, model in self.models.items():
75
            if name == 'isolation_forest':
76
                # Anomaly detection model
77
                score = model.decision_function(features_scaled)[0]
78
                predictions[name] = 1 if score < 0 else 0
79
                confidence_scores[name] = abs(score)
80
            else:
81
                # Classification models
82
                pred = model.predict(features_scaled)[0]
83
                prob = model.predict_proba(features_scaled)[0]
84
                predictions[name] = pred
85
                confidence_scores[name] = max(prob)
86

87
        # Ensemble decision
88
        final_prediction = np.mean(list(predictions.values())) > 0.5
89
        final_confidence = np.mean(list(confidence_scores.values()))
90

91
        return {
92
            'is_zero_day': final_prediction,
93
            'confidence': final_confidence,
94
            'model_predictions': predictions,
95
            'feature_importance': self.get_feature_importance(features)
96
        }

1
<!-- Heuristic Detection Rules -->
2
<group name="zero_day,heuristic">
3
  <!-- Reflective DLL Injection -->
4
  <rule id="500050" level="13">
5
    <if_sid>61603</if_sid>
6
    <field name="win.eventdata.image" type="pcre2">\\rundll32\.exe$</field>
7
    <field name="win.eventdata.commandLine" type="pcre2" negate="yes">\.dll</field>
8
    <description>Zero-Day Heuristic: Rundll32 without DLL parameter</description>
9
    <mitre>
10
      <id>T1055.001</id>
11
    </mitre>
12
  </rule>
13

14
  <!-- Living off the Land -->
15
  <rule id="500051" level="12">
16
    <if_sid>61603</if_sid>
17
    <field name="win.eventdata.parentImage" type="pcre2">\\(winword|excel|powerpnt)\.exe$</field>
18
    <field name="win.eventdata.image" type="pcre2">\\(certutil|bitsadmin|wmic)\.exe$</field>
19
    <description>Zero-Day Heuristic: Office spawning system utility</description>
20
    <mitre>
21
      <id>T1218</id>
22
    </mitre>
23
  </rule>
24

25
  <!-- Privilege Escalation Attempt -->
26
  <rule id="500052" level="14">
27
    <if_sid>61617</if_sid>
28
    <field name="win.eventdata.sourceImage" type="pcre2" negate="yes">\\system32\\</field>
29
    <field name="win.eventdata.targetImage" type="pcre2">\\(lsass|winlogon|services)\.exe$</field>
30
    <field name="win.eventdata.grantedAccess" type="pcre2">0x1[0-9A-F]{3}</field>
31
    <description>Zero-Day Heuristic: Non-system process accessing critical process</description>
32
    <mitre>
33
      <id>T1003</id>
34
    </mitre>
35
  </rule>
36
</group>

1
class HeuristicScoring:
2
    def __init__(self):
3
        self.heuristic_weights = {
4
            'process_anomaly': 3,
5
            'network_anomaly': 2,
6
            'file_anomaly': 2,
7
            'registry_anomaly': 2,
8
            'api_anomaly': 3,
9
            'memory_anomaly': 4,
10
            'persistence': 3,
11
            'evasion': 4
12
        }
13

14
    def calculate_threat_score(self, indicators):
15
        """Calculate composite threat score from heuristics"""
16
        score = 0
17
        triggered_heuristics = []
18

19
        for indicator in indicators:
20
            category = indicator['category']
21
            weight = self.heuristic_weights.get(category, 1)
22

23
            # Apply weight and severity
24
            severity_multiplier = {
25
                'LOW': 0.5,
26
                'MEDIUM': 1.0,
27
                'HIGH': 1.5,
28
                'CRITICAL': 2.0
29
            }.get(indicator['severity'], 1.0)
30

31
            indicator_score = weight * severity_multiplier
32
            score += indicator_score
33

34
            triggered_heuristics.append({
35
                'heuristic': indicator['name'],
36
                'category': category,
37
                'contribution': indicator_score
38
            })
39

40
        # Normalize score to 0-100
41
        normalized_score = min(score * 10, 100)
42

43
        return {
44
            'threat_score': normalized_score,
45
            'threat_level': self.get_threat_level(normalized_score),
46
            'triggered_heuristics': triggered_heuristics,
47
            'recommendation': self.get_recommendation(normalized_score)
48
        }

1
# Sandbox integration for zero-day analysis
2
class SandboxIntegration:
3
    def __init__(self, sandbox_api):
4
        self.sandbox = sandbox_api
5
        self.detonation_environments = [
6
            'Windows 10 x64',
7
            'Windows Server 2019',
8
            'Ubuntu 20.04',
9
            'macOS Big Sur'
10
        ]
11

12
    def analyze_suspicious_file(self, file_path, urgency='normal'):
13
        """Submit file for sandbox analysis"""
14
        submission = {
15
            'file': file_path,
16
            'environments': self.detonation_environments,
17
            'analysis_timeout': 300 if urgency == 'high' else 600,
18
            'network_routing': 'internet',
19
            'anti_evasion': True
20
        }
21

22
        # Submit to sandbox
23
        task_id = self.sandbox.submit(submission)
24

25
        # Get results
26
        results = self.sandbox.get_results(task_id)
27

28
        return self.process_sandbox_results(results)
29

30
    def process_sandbox_results(self, results):
31
        """Extract zero-day indicators from sandbox results"""
32
        indicators = {
33
            'network': [],
34
            'file': [],
35
            'registry': [],
36
            'process': [],
37
            'verdict': 'unknown'
38
        }
39

40
        # Process behavioral indicators
41
        for behavior in results['behaviors']:
42
            if behavior['severity'] >= 7:
43
                category = behavior['category']
44
                indicators[category].append({
45
                    'description': behavior['description'],
46
                    'iocs': behavior.get('iocs', []),
47
                    'ttp': behavior.get('mitre_techniques', [])
48
                })
49

50
        # Determine verdict
51
        if results['threat_score'] >= 80:
52
            indicators['verdict'] = 'malicious'
53
        elif results['threat_score'] >= 50:
54
            indicators['verdict'] = 'suspicious'
55
        else:
56
            indicators['verdict'] = 'clean'
57

58
        return indicators

1
# Threat hunting queries for zero-days
2
class ZeroDayHunter:
3
    def __init__(self, wazuh_api):
4
        self.api = wazuh_api
5
        self.hunting_queries = {
6
            'rare_processes': self.hunt_rare_processes,
7
            'unusual_parents': self.hunt_unusual_parents,
8
            'network_anomalies': self.hunt_network_anomalies,
9
            'persistence_mechanisms': self.hunt_persistence,
10
            'credential_access': self.hunt_credential_access
11
        }
12

13
    def hunt_rare_processes(self, timeframe='24h'):
14
        """Find processes that rarely execute"""
15
        query = """
16
        SELECT
17
            process_name,
18
            COUNT(*) as exec_count,
19
            COUNT(DISTINCT agent_id) as unique_hosts,
20
            AVG(cpu_usage) as avg_cpu,
21
            MAX(memory_usage) as max_memory
22
        FROM process_events
23
        WHERE timestamp > NOW() - INTERVAL %s
24
        GROUP BY process_name
25
        HAVING exec_count < 10
26
        AND unique_hosts < 3
27
        ORDER BY exec_count ASC
28
        """
29

30
        results = self.api.query(query, [timeframe])
31

32
        # Analyze rare processes for suspicious patterns
33
        suspicious = []
34
        for proc in results:
35
            risk_score = self.calculate_rarity_risk(proc)
36
            if risk_score > 0.7:
37
                suspicious.append({
38
                    'process': proc['process_name'],
39
                    'risk_score': risk_score,
40
                    'hosts': proc['unique_hosts'],
41
                    'recommendation': 'Investigate process origin and purpose'
42
                })
43

44
        return suspicious

1
<!-- Zero-Day Response Configuration -->
2
<ossec_config>
3
  <!-- Network Isolation -->
4
  <active-response>
5
    <command>isolate-zero-day</command>
6
    <location>local</location>
7
    <rules_id>500001,500011,500031</rules_id>
8
    <timeout>0</timeout>
9
  </active-response>
10

11
  <!-- Process Termination -->
12
  <active-response>
13
    <command>kill-suspicious-process</command>
14
    <location>local</location>
15
    <rules_id>500002,500003,500050</rules_id>
16
  </active-response>
17

18
  <!-- Memory Dump Collection -->
19
  <active-response>
20
    <command>collect-memory-dump</command>
21
    <location>local</location>
22
    <rules_id>500010,500011</rules_id>
23
  </active-response>
24

25
  <!-- Sandbox Submission -->
26
  <active-response>
27
    <command>submit-to-sandbox</command>
28
    <location>server</location>
29
    <rules_id>500030,500031,500032</rules_id>
30
  </active-response>
31
</ossec_config>

1
def collect_zero_day_forensics(alert):
2
    """Collect forensic data for zero-day analysis"""
3
    forensics = {
4
        'alert_id': alert['id'],
5
        'timestamp': datetime.now(),
6
        'artifacts': []
7
    }
8

9
    # Collect process memory
10
    if 'process' in alert:
11
        memory_dump = dump_process_memory(alert['process']['pid'])
12
        forensics['artifacts'].append({
13
            'type': 'memory_dump',
14
            'path': memory_dump,
15
            'size': os.path.getsize(memory_dump)
16
        })
17

18
    # Collect network traffic
19
    pcap = capture_network_traffic(
20
        duration=300,
21
        filter=f"host {alert['source_ip']}"
22
    )
23
    forensics['artifacts'].append({
24
        'type': 'network_capture',
25
        'path': pcap
26
    })
27

28
    # Collect system artifacts
29
    artifacts = collect_system_artifacts(alert['agent_id'])
30
    forensics['artifacts'].extend(artifacts)
31

32
    # Generate report
33
    report = generate_forensic_report(forensics)
34

35
    return forensics

1
{
2
  "zero_day_metrics": {
3
    "detection_performance": {
4
      "precision": 0.92,
5
      "recall": 0.88,
6
      "f1_score": 0.9,
7
      "false_positive_rate": 0.08
8
    },
9
    "detection_methods": {
10
      "behavioral_analysis": {
11
        "detections": 156,
12
        "accuracy": 0.89
13
      },
14
      "machine_learning": {
15
        "detections": 203,
16
        "accuracy": 0.94
17
      },
18
      "heuristics": {
19
        "detections": 178,
20
        "accuracy": 0.87
21
      },
22
      "sandboxing": {
23
        "detections": 89,
24
        "accuracy": 0.96
25
      }
26
    },
27
    "time_to_detect": {
28
      "average": "3.7 minutes",
29
      "median": "2.1 minutes",
30
      "p95": "8.3 minutes"
31
    },
32
    "prevented_incidents": 47,
33
    "estimated_damage_prevented": "$8.3M"
34
  }
35
}

1
def establish_behavioral_baseline():
2
    """Create baseline for zero-day detection"""
3
    baseline = {
4
        'processes': collect_process_baseline(days=30),
5
        'network': collect_network_baseline(days=30),
6
        'file_operations': collect_file_baseline(days=30),
7
        'api_calls': collect_api_baseline(days=30)
8
    }
9

10
    # Calculate statistical properties
11
    for category, data in baseline.items():
12
        baseline[category] = {
13
            'mean': calculate_mean(data),
14
            'std': calculate_std(data),
15
            'percentiles': calculate_percentiles(data, [50, 90, 95, 99])
16
        }
17

18
    return baseline

1
def update_detection_models():
2
    """Keep detection models current"""
3
    # Retrain ML models weekly
4
    if datetime.now().weekday() == 0:  # Monday
5
        retrain_ml_models()
6

7
    # Update heuristics based on new threats
8
    update_heuristic_rules()
9

10
    # Refresh threat intelligence
11
    refresh_threat_intel_feeds()
12

13
    # Validate detection accuracy
14
    validate_detection_performance()