sameersyed/Defence_FrameWork

1{2  "attack_types": {3    "adversarial_attacks": {4      "name": "Adversarial Attacks",5      "description": "Crafted inputs with imperceptible perturbations designed to fool ML models. Includes FGSM, PGD, C&W, DeepFool attacks.",6      "risk_level": "high",7      "attack_phase": "inference",8      "scenarios": ["image classification", "object detection", "nlp models", "autonomous vehicles", "facial recognition"],9      "keywords": ["adversarial", "perturbation", "fooling", "misclassification", "evasion", "fgsm", "pgd", "deepfool", "attack"],10      "defenses": ["adversarial_training", "input_validation", "defensive_distillation", "ensemble_defense"],11      "real_world_impact": "Can cause autonomous vehicles to misread stop signs, bypass facial recognition, or manipulate medical diagnosis systems."12    },13    "data_poisoning": {14      "name": "Data Poisoning",15      "description": "Malicious manipulation of training data to corrupt model behavior. Includes label flipping, backdoor injection, and feature poisoning.",16      "risk_level": "critical",17      "attack_phase": "training",18      "scenarios": ["training pipeline", "federated learning", "crowdsourced data", "continuous learning", "online learning"],19      "keywords": ["poisoning", "training data", "corrupt", "malicious data", "backdoor injection", "label flipping", "contaminated"],20      "defenses": ["data_validation", "anomaly_detection", "secure_pipeline", "robust_aggregation"],21      "real_world_impact": "Can cause models to learn incorrect patterns, create hidden backdoors, or systematically fail on specific inputs."22    },23    "model_inversion": {24      "name": "Model Inversion & Privacy Attacks",25      "description": "Extracting sensitive training data by querying the model. Includes membership inference and attribute inference attacks.",26      "risk_level": "high",27      "attack_phase": "inference",28      "scenarios": ["facial recognition", "medical diagnosis", "privacy-sensitive applications", "healthcare", "financial models"],29      "keywords": ["inversion", "privacy", "data extraction", "membership inference", "reconstruction", "privacy leak", "sensitive data"],30      "defenses": ["differential_privacy", "output_perturbation", "access_control", "confidence_masking"],31      "real_world_impact": "Can reveal whether specific individuals were in training data, reconstruct private images, or leak sensitive medical records."32    },33    "backdoor_attacks": {34      "name": "Backdoor/Trojan Attacks",35      "description": "Hidden triggers embedded in models that activate malicious behavior when specific patterns are present.",36      "risk_level": "critical",37      "attack_phase": "training",38      "scenarios": ["third-party models", "model marketplaces", "transfer learning", "pre-trained models", "model-as-a-service"],39      "keywords": ["backdoor", "trojan", "trigger", "hidden behavior", "malicious model", "neural trojan", "poisoned model"],40      "defenses": ["model_inspection", "activation_analysis", "fine_pruning", "neural_cleanse"],41      "real_world_impact": "Can cause models to misclassify specific inputs, bypass security systems, or execute malicious actions when triggered."42    },43    "model_extraction": {44      "name": "Model Extraction/Stealing",45      "description": "Replicating a model's functionality through repeated queries to steal intellectual property and bypass API costs.",46      "risk_level": "high",47      "attack_phase": "inference",48      "scenarios": ["api-based models", "cloud ml services", "proprietary models", "commercial ai", "saas platforms"],49      "keywords": ["extraction", "stealing", "steal", "clone", "replicate", "query attack", "querying", "repeatedly", "model theft", "copy"],50      "defenses": ["rate_limiting", "query_monitoring", "watermarking", "access_control", "api_protection"],51      "real_world_impact": "Can lead to intellectual property theft, revenue loss, and competitors replicating expensive models for free."52    },53    "physical_attacks": {54      "name": "Physical-World Attacks",55      "description": "Real-world manipulations using physical objects (stickers, patches) that affect model perception in the physical environment.",56      "risk_level": "high",57      "attack_phase": "inference",58      "scenarios": ["autonomous vehicles", "surveillance systems", "robotics", "traffic sign recognition", "object detection"],59      "keywords": ["physical", "real-world", "sticker", "patch", "stop sign", "traffic", "adversarial patch", "physical perturbation"],60      "defenses": ["robust_training", "multi_sensor_fusion", "anomaly_detection", "certified_defense"],61      "real_world_impact": "Can cause autonomous vehicles to ignore stop signs, evade surveillance cameras, or manipulate robotic systems."62    },63    "gradient_attacks": {64      "name": "Gradient-Based Attacks",65      "description": "Attacks that exploit gradient information to craft adversarial examples. Includes FGSM, PGD, and C&W attacks.",66      "risk_level": "high",67      "attack_phase": "inference",68      "scenarios": ["white-box attacks", "gradient access", "model transparency"],69      "keywords": ["gradient", "fgsm", "pgd", "carlini", "wagner", "white-box", "gradient descent"],70      "defenses": ["gradient_masking", "adversarial_training", "defensive_distillation"],71      "real_world_impact": "Highly effective when attacker has model access, can generate targeted misclassifications."72    },73    "evasion_attacks": {74      "name": "Evasion Attacks",75      "description": "Attacks designed to evade detection by security systems, spam filters, or malware detectors.",76      "risk_level": "high",77      "attack_phase": "inference",78      "scenarios": ["spam detection", "malware detection", "intrusion detection", "fraud detection"],79      "keywords": ["evasion", "bypass", "evade", "detection", "spam", "malware", "fraud"],80      "defenses": ["ensemble_defense", "anomaly_detection", "robust_features"],81      "real_world_impact": "Can allow malware to bypass antivirus, spam to reach inboxes, or fraudulent transactions to succeed."82    },83    "byzantine_attacks": {84      "name": "Byzantine Attacks",85      "description": "Malicious participants in federated/distributed learning sending corrupted updates to poison the global model.",86      "risk_level": "critical",87      "attack_phase": "training",88      "scenarios": ["federated learning", "distributed training", "collaborative learning", "edge computing"],89      "keywords": ["byzantine", "federated", "distributed", "malicious worker", "corrupted update"],90      "defenses": ["robust_aggregation", "byzantine_resilient", "secure_aggregation"],91      "real_world_impact": "Can corrupt federated learning models used in mobile keyboards, healthcare, or financial applications."92    },93    "sybil_attacks": {94      "name": "Sybil Attacks",95      "description": "Creating multiple fake identities to gain disproportionate influence in collaborative learning systems.",96      "risk_level": "high",97      "attack_phase": "training",98      "scenarios": ["federated learning", "crowdsourcing", "peer-to-peer learning", "voting systems"],99      "keywords": ["sybil", "fake identity", "multiple accounts", "identity fraud", "fake users"],100      "defenses": ["identity_verification", "reputation_system", "proof_of_work"],101      "real_world_impact": "Can manipulate crowdsourced training data, bias federated models, or game recommendation systems."102    }103  },104  "defenses": {105    "adversarial_training": {106      "name": "Adversarial Training",107      "description": "Training models with adversarial examples to improve robustness. Augment training data with perturbed samples.",108      "effectiveness": "high",109      "complexity": "medium",110      "implementation": "Generate adversarial examples using FGSM/PGD and include in training batches with correct labels."111    },112    "defensive_distillation": {113      "name": "Defensive Distillation",114      "description": "Training a student model on soft probabilities from a teacher model to smooth decision boundaries.",115      "effectiveness": "medium",116      "complexity": "medium",117      "implementation": "Train teacher model, extract soft labels at high temperature, train student model on soft labels."118    },119    "input_validation": {120      "name": "Input Validation & Sanitization",121      "description": "Detecting and filtering suspicious inputs before inference using statistical checks and anomaly detection.",122      "effectiveness": "medium",123      "complexity": "low",124      "implementation": "Check input statistics, detect out-of-distribution samples, apply preprocessing filters."125    },126    "data_validation": {127      "name": "Data Validation & Sanitization",128      "description": "Rigorous validation of training data sources with integrity checks and outlier removal.",129      "effectiveness": "high",130      "complexity": "medium",131      "implementation": "Use Isolation Forest, statistical tests, and data provenance tracking to detect poisoned samples."132    },133    "differential_privacy": {134      "name": "Differential Privacy",135      "description": "Adding calibrated noise to outputs/gradients to prevent information leakage while maintaining utility.",136      "effectiveness": "high",137      "complexity": "high",138      "implementation": "Apply DP-SGD during training, add Laplace/Gaussian noise to outputs, track privacy budget (epsilon)."139    },140    "model_inspection": {141      "name": "Model Inspection & Verification",142      "description": "Analyzing model weights, activations, and behavior for anomalies indicating backdoors or trojans.",143      "effectiveness": "medium",144      "complexity": "high",145      "implementation": "Use Neural Cleanse, activation clustering, or weight analysis to detect suspicious patterns."146    },147    "rate_limiting": {148      "name": "Rate Limiting & Query Monitoring",149      "description": "Restricting API access frequency and monitoring for suspicious query patterns indicating extraction attempts.",150      "effectiveness": "high",151      "complexity": "low",152      "implementation": "Implement per-user query limits, detect repeated similar queries, use CAPTCHA for suspicious behavior."153    },154    "robust_training": {155      "name": "Robust Training Techniques",156      "description": "Training with augmented data, diverse scenarios, and robust loss functions to improve generalization.",157      "effectiveness": "high",158      "complexity": "medium",159      "implementation": "Use data augmentation, mixup, cutout, and robust loss functions like Huber loss."160    },161    "access_control": {162      "name": "Access Control & Monitoring",163      "description": "Implementing strict authentication, authorization, and comprehensive audit logging.",164      "effectiveness": "medium",165      "complexity": "low",166      "implementation": "Use OAuth/JWT tokens, role-based access control, log all API calls with timestamps."167    },168    "anomaly_detection": {169      "name": "Anomaly Detection",170      "description": "Real-time monitoring for unusual patterns in inputs, outputs, or model behavior.",171      "effectiveness": "medium",172      "complexity": "medium",173      "implementation": "Use Isolation Forest, One-Class SVM, or autoencoders to detect out-of-distribution samples."174    },175    "secure_pipeline": {176      "name": "Secure Data Pipeline",177      "description": "End-to-end encryption and integrity verification for data flow from collection to training.",178      "effectiveness": "high",179      "complexity": "medium",180      "implementation": "Use TLS for data transfer, cryptographic hashing for integrity, secure enclaves for processing."181    },182    "output_perturbation": {183      "name": "Output Perturbation",184      "description": "Adding controlled noise to model outputs to prevent information leakage.",185      "effectiveness": "medium",186      "complexity": "low",187      "implementation": "Add Gaussian noise to confidence scores, round probabilities, or return top-k predictions only."188    },189    "activation_analysis": {190      "name": "Activation Analysis",191      "description": "Monitoring internal layer activations for backdoor detection and trigger identification.",192      "effectiveness": "medium",193      "complexity": "high",194      "implementation": "Cluster activations, detect outliers, use activation maximization to find triggers."195    },196    "fine_pruning": {197      "name": "Fine Pruning",198      "description": "Removing neurons with suspicious activation patterns to eliminate backdoors.",199      "effectiveness": "medium",200      "complexity": "medium",201      "implementation": "Identify neurons with abnormal activations, prune them, fine-tune model on clean data."202    },203    "watermarking": {204      "name": "Model Watermarking",205      "description": "Embedding unique signatures in models to detect theft and prove ownership.",206      "effectiveness": "medium",207      "complexity": "medium",208      "implementation": "Embed trigger-output pairs, use backdoor-based watermarks, or parameter-based signatures."209    },210    "multi_sensor_fusion": {211      "name": "Multi-Sensor Fusion",212      "description": "Combining multiple data sources (camera, lidar, radar) for robust decision-making.",213      "effectiveness": "high",214      "complexity": "high",215      "implementation": "Use sensor fusion algorithms, cross-validate predictions, detect inconsistencies across sensors."216    },217    "ensemble_defense": {218      "name": "Ensemble Defense",219      "description": "Using multiple models with different architectures to make robust predictions.",220      "effectiveness": "high",221      "complexity": "medium",222      "implementation": "Train diverse models, use majority voting or weighted averaging, detect prediction disagreements."223    },224    "gradient_masking": {225      "name": "Gradient Masking",226      "description": "Obfuscating gradients to prevent gradient-based attacks (use with caution - can be bypassed).",227      "effectiveness": "low",228      "complexity": "medium",229      "implementation": "Use non-differentiable operations, add stochasticity, but combine with other defenses."230    },231    "robust_aggregation": {232      "name": "Robust Aggregation",233      "description": "Byzantine-resilient aggregation methods for federated learning.",234      "effectiveness": "high",235      "complexity": "high",236      "implementation": "Use Krum, trimmed mean, median aggregation, or coordinate-wise median for federated updates."237    },238    "neural_cleanse": {239      "name": "Neural Cleanse",240      "description": "Detecting and removing backdoor triggers through reverse engineering.",241      "effectiveness": "high",242      "complexity": "high",243      "implementation": "Optimize for minimal perturbations that cause misclassification, detect outlier triggers."244    },245    "certified_defense": {246      "name": "Certified Defenses",247      "description": "Provable robustness guarantees against adversarial perturbations.",248      "effectiveness": "high",249      "complexity": "high",250      "implementation": "Use randomized smoothing, interval bound propagation, or abstract interpretation."251    },252    "confidence_masking": {253      "name": "Confidence Masking",254      "description": "Hiding confidence scores to prevent membership inference attacks.",255      "effectiveness": "medium",256      "complexity": "low",257      "implementation": "Return only predicted class, threshold confidence scores, or add noise to probabilities."258    },259    "api_protection": {260      "name": "API Protection",261      "description": "Comprehensive API security including authentication, rate limiting, and monitoring.",262      "effectiveness": "high",263      "complexity": "medium",264      "implementation": "Use API keys, implement request throttling, log queries, detect extraction patterns."265    },266    "identity_verification": {267      "name": "Identity Verification",268      "description": "Strong authentication to prevent Sybil attacks in collaborative systems.",269      "effectiveness": "high",270      "complexity": "medium",271      "implementation": "Use multi-factor authentication, device fingerprinting, or proof-of-identity mechanisms."272    },273    "reputation_system": {274      "name": "Reputation System",275      "description": "Track participant behavior and weight contributions by reputation scores.",276      "effectiveness": "medium",277      "complexity": "medium",278      "implementation": "Assign reputation scores, penalize suspicious behavior, weight aggregation by reputation."279    }280  },281  "model_types": {282    "cnn": ["image classification", "object detection", "computer vision"],283    "rnn": ["text generation", "sentiment analysis", "time series"],284    "transformer": ["nlp", "language model", "translation", "chatbot"],285    "gan": ["image generation", "deepfake", "synthetic data"],286    "reinforcement": ["game playing", "robotics", "autonomous systems"]287  }288}289