feat(docs): add cheatseet for ML07

Top10MLSummary.md

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+## ML01:2023 Input Manipulation Attack
+
+Input Manipulation Attacks involve changing input data to trick models, with Adversarial Attacks being a key tactic. Prevention methods include training models with deceptive examples (Adversarial Training), using robust models resistant to manipulation, and employing input validation to detect and reject potentially harmful inputs.
+
+
+## ML02:2023 Data Poisoning Attack
+Data poisoning attacks involve manipulating training data to influence model behavior negatively. Prevention methods include thorough validation and verification of training data, secure storage practices, data separation, access controls, monitoring, auditing, model validation with separate sets, model ensembles, and anomaly detection to identify abnormal behavior.
+
+
+
+## ML03:2023 Model Inversion Attack
+
+Model inversion attacks involve extracting information from models by reverse-engineering them. Prevention includes restricting access, validating inputs, ensuring model transparency, regular monitoring, and retraining models. Vigilant implementation of these measures is crucial to safeguard against such attacks.
+
+
+## ML04:2023 Membership Inference Attack
+
+Membership inference attacks involve manipulating a model's training data to expose sensitive information. Prevention methods include training models on randomized data, obfuscating predictions with noise or differential privacy, regularization techniques, reducing training data size, and testing and monitoring for anomalies to thwart such attacks.
+
+
+## ML05:2023 Model Theft
+
+Model theft attacks involve unauthorized access to a model's parameters. Prevention methods include encryption of sensitive information, strict access controls, regular backups, code obfuscation, watermarking, legal protection, and monitoring/auditing to detect and prevent theft attempts.
+
+## ML06:2023 AI Supply Chain Attacks
+
+AI Supply Chain Attacks involve tampering with machine learning libraries or models used by a system, including associated data. Prevention involves verifying package signatures, using secure repositories like Anaconda, keeping packages updated, employing virtual environments, conducting code reviews, utilizing package verification tools like PEP 476, Secure Package Install, and educating developers on the risks.
+
+
+## ML07:2023 Transfer Learning Attack
+
+Transfer learning attacks involve training a model on one task and fine-tuning it on another to cause undesirable behavior. Prevention methods include monitoring and updating training datasets regularly, using secure and trusted datasets, implementing model isolation, employing differential privacy, and conducting regular security audits to identify and address vulnerabilities.
+
+
+## ML08:2023 Model Skewing
+
+Model skewing attacks involve manipulating the distribution of training data to induce undesirable model behavior. Prevention strategies include implementing robust access controls, verifying the authenticity of feedback data, employing data validation and cleaning techniques, implementing anomaly detection, regularly monitoring model performance, and continuously training the model with updated and verified data.
+
+
+## ML09:2023 Output Integrity Attack
+
+In an Output Integrity Attack, an attacker aims to manipulate a machine learning model's output to cause harm. Prevention methods include using cryptographic techniques for result authenticity verification, securing communication channels, input validation, maintaining tamper-evident logs, regular software updates, and monitoring and auditing for suspicious activities.
+
+
+## ML10:2023 Model Poisoning
+
+Model poisoning attacks involve manipulating a model's parameters to induce undesirable behavior. Prevention methods include regularization techniques to mitigate overfitting, designing robust model architectures and activation functions, and employing cryptographic techniques to secure model parameters from unauthorized access or manipulation.