Initial aiphsd skeleton and blueprint v1 7007966996010148688

ai_module/analyzer.py

@@ -5,167 +5,57 @@
 
 class SecurityAnalyzer:
     THREAT_TACTICS = {
-        "ransomware": {
-            "tactics": ["Data Encryption", "Exfiltration", "Inhibition of Recovery"],
-            "actions": [
-                {"priority": "HIGH", "task": "Isolate affected clusters", "action": "ISOLATE"},
-                {"priority": "HIGH", "task": "Disable VSS deletion scripts", "action": "BLOCK"}
-            ],
-            "default_severity": "CRITICAL"
-        },
-        "botnet": {
-            "tactics": ["Command and Control", "Resource Hijacking"],
-            "actions": [
-                {"priority": "MEDIUM", "task": "Block C2 IP at firewall", "action": "FW_BLOCK"},
-                {"priority": "LOW", "task": "Identify infected endpoints", "action": "SCAN"}
-            ],
-            "default_severity": "HIGH"
-        },
-        "phishing": {
-            "tactics": ["Credential Access", "Initial Access"],
-            "actions": [
-                {"priority": "HIGH", "task": "Reset credentials for affected users", "action": "RESET_PW"},
-                {"priority": "MEDIUM", "task": "Purge email from inboxes", "action": "PURGE_MAIL"}
-            ],
-            "default_severity": "MEDIUM"
-        },
-        "exploit": {
-            "tactics": ["Execution", "Privilege Escalation"],
-            "actions": [
-                {"priority": "HIGH", "task": "Apply emergency patches", "action": "PATCH"},
-                {"priority": "MEDIUM", "task": "Restrict access to vulnerable service", "action": "RESTRICT"}
-            ],
-            "default_severity": "HIGH"
-        }
+        "ransomware": {"tactics": ["Data Encryption"], "actions": [{"priority": "HIGH", "task": "Isolate Cluster", "action": "ISOLATE"}], "default_severity": "CRITICAL"},
+        "botnet": {"tactics": ["C2"], "actions": [{"priority": "MEDIUM", "task": "Block IP", "action": "FW_BLOCK"}], "default_severity": "HIGH"}
     }
 
     def __init__(self, model_name: str = "AIP-GPT-2"):
         self.model_name = model_name
-        self.version = "1.1.0"
-
-    def normalize_log(self, raw_log: Dict) -> Dict:
-        """Converts raw logs into a unified schema for analysis."""
-        return {
-            "timestamp": raw_log.get("timestamp", datetime.datetime.now().isoformat()),
-            "source": raw_log.get("source_ip", raw_log.get("ip", "0.0.0.0")),
-            "event_type": raw_log.get("type", "unknown"),
-            "raw_message": raw_log.get("msg", ""),
-            "normalized": True
-        }
-
-    def calculate_risk_score(self, threats: List[Dict], internal_anomalies: List[Dict]) -> float:
-        """Calculates a composite risk score (0-100)."""
-        base_threat = sum(t.get("severity_score", 0) for t in threats)
-        internal_factor = len(internal_anomalies) * 2.5
-        score = min(100.0, base_threat + internal_factor)
-        return round(score, 2)
+        self.version = "1.2.0"
 
     def generate_summary(self, alerts: List[Dict]) -> str:
-        """Simulates natural language summarization for the dashboard."""
+        """Enhanced summary with simulated predictive integration."""
         if not alerts:
-            return "All systems stable. No significant threats detected in the last 24 hours."
+            return "All systems stable. Julia-Engine forecast: Low threat probability for next 48h."
 
         count = len(alerts)
         critical = [a for a in alerts if a.get('severity', '').lower() in ['high', 'critical']]
-        summary = f"AI Analysis: {count} active alerts detected. "
-        if critical:
-            summary += f"Critical focus on {critical[0].get('title')}. "
-        summary += "Automated correlation indicates regional shift in attack patterns."
-        return summary
 
-    def autonomous_threat_hunter(self, internal_telemetry: List[Dict], osint_data: List[Dict]) -> List[Dict]:
-        """Advanced AI Threat Hunter correlating telemetry with OSINT using IOC matching."""
-        print("AI Threat Hunter: Commencing deep correlation...")
-        correlated = []
+        # Simulating data from Julia/Fortran modules
+        prediction = "Julia-Engine indicates a 15% increase in ransomware activity globally."
+        risk_sim = "Fortran-Sim: Critical infrastructure risk factor at 0.12 (STABLE)."
 
-        # Simple IOC extraction (IPs/Ports) from OSINT
-        for intel in osint_data:
-            intel_title = intel.get('title', '').lower()
-            intel_body = intel.get('body', '').lower()
-
-            # Find tactic category
-            category = next((cat for cat in self.THREAT_TACTICS if cat in intel_title), None)
-
-            for telemetry in internal_telemetry:
-                tele_msg = telemetry.get('msg', '').lower()
-                confidence = 0.0
-                reasons = []
-
-                # Port Correlation
-                ports = re.findall(r'port\s+(\d+)', tele_msg + " " + intel_title + " " + intel_body)
-                if len(ports) > 1 and ports[0] == ports[1]:
-                    confidence += 0.6
-                    reasons.append(f"Matching port activity ({ports[0]})")
+        summary = f"AI Analysis: {count} active alerts. Most critical: {critical[0].get('title') if critical else 'N/A'}. "
+        summary += f"\nPREDICTIVE: {prediction} \nSIMULATION: {risk_sim}"
+        return summary
 
-                # Keyword Correlation
-                if category and category in tele_msg:
-                    confidence += 0.4
-                    reasons.append(f"Matched threat category: {category}")
+    def query_response(self, query: str) -> Dict:
+        """Detailed query response."""
+        return {
+            "query": query,
+            "status": "Complete",
+            "summary": f"Deep-dive analysis for '{query}' completed. Pattern match in Segment 4.",
+            "source_attribution": [{"name": "Global OSINT", "type": "External", "relevance": "High"}],
+            "timestamp": datetime.datetime.now().isoformat()
+        }
 
-                if confidence >= 0.5:
-                    tactic_info = self.THREAT_TACTICS.get(category, {})
+    def autonomous_threat_hunter(self, internal_telemetry: List[Dict], osint_data: List[Dict]) -> List[Dict]:
+        """Correlate telemetry with OSINT."""
+        correlated = []
+        for telemetry in internal_telemetry:
+            for intel in osint_data:
+                if "port" in telemetry.get("msg", "").lower() and "ransomware" in intel.get("title", "").lower():
                     correlated.append({
                         "type": "CORRELATED_THREAT",
-                        "category": category or "unknown",
-                        "severity": tactic_info.get("default_severity", "MEDIUM"),
-                        "confidence_score": round(min(0.99, confidence), 2),
-                        "reason": "; ".join(reasons),
-                        "actions": tactic_info.get("actions", []),
-                        "timestamp": datetime.datetime.now().isoformat()
+                        "severity": "HIGH",
+                        "reason": f"Port anomaly matches global ransomware intel.",
+                        "actions": [{"priority": "HIGH", "task": "Isolate Endpoint", "action": "ISOLATE"}]
                     })
-
         return correlated
 
-    def analyze_malware_sample(self, file_hash: str, sandbox_output: str) -> Dict:
-        """Generates AI analysis report from malware sandbox execution."""
-        risk_score = random.uniform(70, 99)
-        return {
-            "file_hash": file_hash,
-            "ai_risk_score": round(risk_score, 2),
-            "threat_classification": "Trojan.Downloader",
-            "capabilities": ["Network Communication", "Registry Modification", "Anti-Debugging"],
-            "recommendation": "Isolate affected hosts immediately and rotate credentials for service-account-01.",
-            "report_id": f"MAL-REP-{random.randint(1000, 9999)}"
-        }
-
-    def query_response(self, query: str) -> Dict:
-        """Detailed AI query response structure based on 'AI Query Results' design."""
-        # Simulate query parsing
-        threat_types = ["Data Exfiltration", "System Infiltration", "Lateral Movement", "Resource Hijacking"]
-        confidence_scores = {tt: round(random.uniform(10, 95), 1) for tt in threat_types}
-
-        # Sort by confidence
-        sorted_scores = sorted(confidence_scores.items(), key=lambda x: x[1], reverse=True)
-
-        return {
-            "query": query,
-            "status": "Complete",
-            "runtime": "1.2s",
-            "summary": f"My analysis of '{query}' identifies a high-confidence correlation between global ransomware trends and recent internal credential anomalies.",
-            "threat_matrix": [{"type": t, "confidence": c} for t, c in sorted_scores],
-            "source_attribution": [
-                {"name": "Global OSINT Feed", "type": "External", "relevance": "High"},
-                {"name": "Intranet Log Analyzer", "type": "Internal", "relevance": "High"},
-                {"name": "User Behavior AI", "type": "AI Module", "relevance": "Medium"}
-            ],
-            "action_items": [
-                {"priority": "HIGH", "task": "Isolate Cluster Alpha in Segment 4", "action": "ISOLATE"},
-                {"priority": "MEDIUM", "task": "Reset credentials for user 'svc_backup_01'", "action": "RESET"},
-                {"priority": "LOW", "task": "Enable enhanced logging for Port 8443", "action": "LOG_UP"}
-            ],
-            "timestamp": datetime.datetime.now().isoformat(),
-            "model": self.model_name
-        }
+    def calculate_risk_score(self, threats: List[Dict], internal_anomalies: List[Dict]) -> float:
+        return round(min(100.0, len(threats) * 15.0 + len(internal_anomalies) * 2.5), 2)
 
 if __name__ == "__main__":
     analyzer = SecurityAnalyzer()
-    print("--- Advanced Threat Hunter Test ---")
-    tel = [{"msg": "High traffic on port 4444. Ransomware patterns detected."}]
-    osint = [{"title": "Ransomware-Alpha active on port 4444", "body": "Targeting port 4444 specifically."}]
-    results = analyzer.autonomous_threat_hunter(tel, osint)
-    import json
-    print(json.dumps(results, indent=2))
-
-    print("\n--- Detailed Query Response Test ---")
-    query_res = analyzer.query_response("Anomalous traffic in Sector 7")
-    print(json.dumps(query_res, indent=2))
+    print(analyzer.generate_summary([{"title": "Unauthorized Access", "severity": "high"}]))

backend/go/cmd/main.go

@@ -3,6 +3,7 @@ package main
 import (
 	"github.com/gin-gonic/gin"
 	"net/http"
+	"time"
 )
 
 func main() {
@@ -12,12 +13,29 @@ func main() {
 	{
 		api.GET("/threats", func(c *gin.Context) {
 			c.JSON(http.StatusOK, []gin.H{
-				{"id": 301, "name": "Go-Exploit-Delta", "risk_score": 88.2, "type": "exploit"},
+				{
+					"id": 301,
+					"name": "Go-Exploit-Delta",
+					"type": "exploit",
+					"source": "CERT-GO",
+					"risk_score": 88.2,
+					"location": "Global",
+					"description": "Go-based detection of delta exploit.",
+					"timestamp": time.Now().Format(time.RFC3339),
+				},
 			})
 		})
 		api.GET("/alerts", func(c *gin.Context) {
 			c.JSON(http.StatusOK, []gin.H{
-				{"id": 401, "title": "Go Alert: Kernel Anomaly", "severity": "critical"},
+				{
+					"id": 401,
+					"title": "Go Alert: Kernel Anomaly",
+					"severity": "critical",
+					"message": "High-severity anomaly detected via Go agent.",
+					"device_id": 1,
+					"tenant_id": "TENANT-GO",
+					"timestamp": time.Now().Format(time.RFC3339),
+				},
 			})
 		})
 	}

backend/nodejs/src/routes/threats.js

@@ -3,7 +3,16 @@ const router = express.Router();
 
 router.get('/', (req, res) => {
   res.json([
-    { id: 101, name: "Node-Ransom-Alpha", type: "ransomware", source: "OSINT", risk_score: 92.5, location: "USA", timestamp: new Date() }
+    {
+      id: 101,
+      name: "Node-Ransom-Alpha",
+      type: "ransomware",
+      source: "OSINT",
+      risk_score: 92.5,
+      location: "USA",
+      description: "Node.js detected alpha variant active.",
+      timestamp: new Date().toISOString()
+    }
   ]);
 });
 

backend/python/app/api/__init__.py

@@ -10,6 +10,8 @@
 from .compliance import router as compliance_router
 from .updater import router as updater_router
 from .cti import router as cti_router
+from .search import router as search_router
+from .settings import router as settings_router
 
 router = APIRouter()
 router.include_router(auth_router, prefix="/auth", tags=["Authentication"])
@@ -23,3 +25,5 @@
 router.include_router(compliance_router, prefix="/compliance", tags=["Compliance Monitoring"])
 router.include_router(updater_router, prefix="/updater", tags=["Auto-Updater"])
 router.include_router(cti_router, prefix="/cti", tags=["Threat Intelligence Sharing"])
+router.include_router(search_router, prefix="/search", tags=["Global Search"])
+router.include_router(settings_router, prefix="/settings", tags=["Platform Settings"])

backend/python/app/api/search.py

@@ -0,0 +1,30 @@
+from fastapi import APIRouter, Depends
+from typing import Dict, List
+from datetime import datetime
+from .auth import get_current_user, User
+from osint.web_searcher import WebSearcher
+
+router = APIRouter()
+web_searcher = WebSearcher()
+
+@router.get("/")
+def global_search(query: str, current_user: User = Depends(get_current_user)):
+    """Aggregates results from OSINT and Internal sources."""
+    print(f"[SEARCH] [{current_user.tenant_id}] Searching for: {query}")
+
+    # 1. External OSINT Search
+    osint_results = web_searcher.search_threat(query, limit=3)
+
+    # 2. Internal Context (Simulated)
+    internal_hits = [
+        {"source": "Internal Logs", "match": f"Telemetry entry matching '{query}' found in srv-web-01."},
+        {"source": "Alerts", "match": f"Resolved alert for {query} on {datetime.now().date()}"}
+    ]
+
+    return {
+        "query": query,
+        "tenant_id": current_user.tenant_id,
+        "external_intel": osint_results,
+        "internal_context": internal_hits,
+        "timestamp": datetime.now()
+    }

backend/python/app/api/settings.py

@@ -0,0 +1,20 @@
+from fastapi import APIRouter, Depends
+from pydantic import BaseModel
+from .auth import get_current_user, User
+
+router = APIRouter()
+
+class PlatformSettings(BaseModel):
+    enable_ai_remediation: bool = True
+    realtime_osint: bool = True
+    rbac_role_default: str = "Analyst"
+    audit_retention_days: int = 90
+
+@router.get("/", response_model=PlatformSettings)
+def get_settings(current_user: User = Depends(get_current_user)):
+    return PlatformSettings()
+
+@router.post("/")
+def update_settings(settings: PlatformSettings, current_user: User = Depends(get_current_user)):
+    # In a real scenario, this would persist to the DB/Redis
+    return {"status": "SUCCESS", "updated_by": current_user.username}

backend/python/app/services/sandbox.py

@@ -3,49 +3,20 @@
 from typing import Dict, List, Optional
 
 class MalwareSandboxService:
-    def __init__(self):
-        self.common_behaviors = [
-            {"type": "NETWORK", "action": "Connection to C2 server at 103.22.45.11 on Port 4444", "risk": 0.9},
-            {"type": "REGISTRY", "action": "Created persistence key: HKLM\\Software\\Microsoft\\Windows\\CurrentVersion\\Run\\svchost_update", "risk": 0.8},
-            {"type": "FILESYSTEM", "action": "Dropped encrypted payload in C:\\Windows\\Temp\\payload.bin", "risk": 0.7},
-            {"type": "PROCESS", "action": "Injected shellcode into explorer.exe", "risk": 0.95},
-            {"type": "ANTI_DEBUG", "action": "Detected IsDebuggerPresent check", "risk": 0.5}
-        ]
-
-    def analyze_sample(self, file_name: str, file_hash: str) -> Dict:
-        """Simulates a full behavioral analysis in a sandbox environment."""
-        print(f"[SANDBOX] Analyzing sample: {file_name} ({file_hash})...")
-
-        # Randomly select behaviors to simulate different malware types
-        num_behaviors = random.randint(2, 4)
-        detected_behaviors = random.sample(self.common_behaviors, num_behaviors)
-
-        # Calculate risk score based on behaviors
-        raw_score = sum(b["risk"] for b in detected_behaviors) / num_behaviors * 100
-        risk_score = round(min(99.0, raw_score + random.uniform(-5, 5)), 1)
+    def analyze_sample(self, file_name: str, file_hash: str, tenant_id: str) -> Dict:
+        """Simulates behavioral analysis with strict tenant isolation."""
+        print(f"[SANDBOX] [{tenant_id}] Analyzing sample: {file_name}...")
 
+        risk_score = round(random.uniform(10, 99), 1)
         return {
             "analysis_id": f"MAL-{random.randint(1000, 9999)}",
-            "file_metadata": {
-                "name": file_name,
-                "hash": file_hash,
-                "timestamp": datetime.datetime.now().isoformat()
-            },
+            "tenant_id": tenant_id, # Enforce isolation
             "risk_assessment": {
                 "score": risk_score,
-                "level": "CRITICAL" if risk_score > 80 else ("HIGH" if risk_score > 60 else "MEDIUM"),
-                "classification": "Trojan.Agent.AIP" if risk_score > 70 else "Spyware.Generic"
+                "level": "CRITICAL" if risk_score > 80 else "MEDIUM"
             },
-            "behaviors": detected_behaviors,
-            "mitre_att&ck_mappings": [
-                {"tactic": "Persistence", "technique": "T1547.001"},
-                {"tactic": "Command and Control", "technique": "T1071.001"}
-            ],
-            "recommendations": [
-                "Isolate all endpoints that have executed this file.",
-                "Block C2 IP at the firewall.",
-                "Rotate administrative credentials."
-            ]
+            "behaviors": [{"type": "NETWORK", "action": "Beaconing detected"}],
+            "timestamp": datetime.datetime.now().isoformat()
         }
 
 sandbox_service = MalwareSandboxService()