At mlsec.dev, our mission is to provide a comprehensive resource for machine learning security. We strive to educate and inform our readers about the latest developments, best practices, and emerging threats in this rapidly evolving field. Our goal is to empower security professionals, data scientists, and machine learning engineers with the knowledge and tools they need to build secure and robust machine learning systems. We are committed to promoting a culture of security and accountability in the machine learning community, and to advancing the state of the art in machine learning security through research and collaboration.
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Machine Learning Security Cheat Sheet
This cheat sheet is designed to provide a quick reference guide for individuals who are new to machine learning security. It covers the basic concepts, topics, and categories related to machine learning security.
Introduction to Machine Learning Security
Machine learning security is a subfield of cybersecurity that focuses on protecting machine learning models and systems from attacks. It involves identifying and mitigating vulnerabilities in machine learning models and systems to prevent them from being exploited by attackers.
Machine Learning Security Concepts
Adversarial Machine Learning
Adversarial machine learning is the study of how machine learning models can be manipulated or attacked by malicious actors. Adversarial attacks can be used to manipulate the output of a machine learning model or to cause it to fail.
Model poisoning is a type of adversarial attack where an attacker injects malicious data into a machine learning model's training data to manipulate its output.
Model stealing is a type of attack where an attacker tries to steal a machine learning model by querying it and using the responses to recreate the model.
Model inversion is a type of attack where an attacker tries to infer sensitive information about the training data used to create a machine learning model by querying the model.
Differential privacy is a technique used to protect the privacy of individuals in a dataset. It involves adding noise to the data to prevent an attacker from identifying specific individuals in the dataset.
Federated learning is a technique used to train machine learning models on decentralized data. It involves training the model on data that is stored on multiple devices without transferring the data to a central location.
Homomorphic encryption is a technique used to encrypt data in a way that allows computations to be performed on the encrypted data without decrypting it.
Machine Learning Security Topics
Data poisoning is a type of attack where an attacker manipulates the training data used to create a machine learning model to cause it to produce incorrect results.
Model evasion is a type of attack where an attacker tries to manipulate a machine learning model's input to cause it to produce incorrect results.
Model extraction is a type of attack where an attacker tries to extract a machine learning model by querying it and using the responses to recreate the model.
Model tampering is a type of attack where an attacker tries to modify a machine learning model to cause it to produce incorrect results.
Privacy-Preserving Machine Learning
Privacy-preserving machine learning is a technique used to protect the privacy of individuals in a dataset while still allowing a machine learning model to be trained on the data.
Secure Multi-Party Computation
Secure multi-party computation is a technique used to perform computations on data that is distributed across multiple devices without revealing the data to any of the devices.
Trusted Execution Environments
Trusted execution environments are secure hardware environments that are used to protect sensitive data and computations from attackers.
Machine Learning Security Categories
Attack techniques are the methods used by attackers to exploit vulnerabilities in machine learning models and systems.
Defense techniques are the methods used to protect machine learning models and systems from attacks.
Tools and Frameworks
Tools and frameworks are software programs and libraries used to implement machine learning security techniques.
Research and Publications
Research and publications are academic papers and articles related to machine learning security.
Machine learning security is an important field that is becoming increasingly relevant as machine learning models and systems are used in more applications. This cheat sheet provides a quick reference guide to the basic concepts, topics, and categories related to machine learning security. By understanding these concepts, individuals can better protect their machine learning models and systems from attacks.
Common Terms, Definitions and Jargon1. Adversarial examples: Inputs to a machine learning model that are intentionally designed to cause the model to make incorrect predictions.
2. Adversarial training: A technique used to train machine learning models to be more robust against adversarial examples.
3. Attack surface: The set of all possible ways that an attacker can exploit a system or application.
4. Backdoor attacks: A type of attack where an attacker gains unauthorized access to a system or application by exploiting a hidden vulnerability.
5. Bias: A systematic error in a machine learning model that causes it to consistently make incorrect predictions for certain groups of data.
6. Black box attacks: A type of attack where an attacker does not have access to the internal workings of a machine learning model, but can still manipulate its inputs and outputs.
7. Botnets: A network of compromised computers that are controlled by a single attacker to perform malicious activities.
8. Class imbalance: A situation where the number of instances in each class of a machine learning dataset is not balanced, which can lead to biased models.
9. Clustering: A machine learning technique used to group similar data points together.
10. Convolutional neural networks (CNNs): A type of neural network commonly used for image and video recognition tasks.
11. Cross-site scripting (XSS): A type of web application vulnerability where an attacker injects malicious code into a website to steal user data or perform other malicious activities.
12. Cybersecurity: The practice of protecting computer systems and networks from unauthorized access, theft, damage, or disruption.
13. Data augmentation: A technique used to increase the size of a machine learning dataset by creating new data points from existing ones.
14. Data poisoning: A type of attack where an attacker manipulates the training data used to train a machine learning model to cause it to make incorrect predictions.
15. Decision trees: A machine learning technique used to make decisions based on a set of rules.
16. Deep learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data.
17. Denial-of-service (DoS) attacks: A type of attack where an attacker floods a system or network with traffic to make it unavailable to users.
18. Differential privacy: A technique used to protect the privacy of individuals in a dataset by adding noise to the data.
19. Encryption: The process of converting plaintext into ciphertext to protect it from unauthorized access.
20. Ensemble learning: A machine learning technique that combines multiple models to improve prediction accuracy.
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