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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 revealed practitioners' common feeling for better protection of machine learning systems in industrial applications.
An example of a physical security measure: a metal lock on the back of a personal computer to prevent hardware tampering. Computer security (also cybersecurity, digital security, or information technology (IT) security) is the protection of computer software, systems and networks from threats that can lead to unauthorized information disclosure, theft or damage to hardware, software, or data ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Robustness can encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz testing, are essential to showing robustness since this type of testing involves invalid or unexpected inputs. Alternatively, fault injection can be used to test robustness ...
By simulating cyber attacks, engineers can rigorously evaluate the effectiveness of existing security measures and uncover weaknesses before malicious actors exploit them. This hands-on testing approach not only identifies vulnerabilities but also helps organizations understand their risk landscape more comprehensively.
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