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The growth of Machine Learning in terms of adoption by companies is proceeding year on year in great strides, consolidating what for all intents and purposes can be called exponential growth.
Along with the many benefits that ML can bring to your business, there are new challenges in the field of Cyber Security, challenges that require a total revolution of security models applied to daily use technologies (e.g., Web Application) in classic scenarios.
The threat that companies must prepare to fight and contain goes by the name of AML, which stands for Adversarial Machine Learning.
The term AML identifies the discipline which studies the Machine Learning vulnerabilities in adversarial environments, and today represents one of the most active research areas in the field of Cyber Security. The attacker, adopting well-thought-out patterns and techniques of attack, may attempt to exploit or fool a learning mechanism either to force a misbehaviour or to extract or misuse information. On the other hand, the defender of an ML system aims to identify potential malicious situations that may compromise the security of the model itself.
This is the same attacker-defender paradigm of other classic Cyber Security approaches. What has changed are the detailed techniques used to attack and defend a model, based on the technicalities and characteristics of Machine Learning.
Securing an IT system has always been a topic worthy of attention that many times becomes a barrier between the customer and new opportunities; a barrier that in the case of Machine Learning we can overcome together thanks to the latest Cyber Security strategies applicable to ML.
In addition to being active in the study and analysis of the latest and up to date tools, Reply's mission is to encourage its customers to use ML systems in their business, including security aspects throughout the whole life cycle of the system, from design to delivery and for the entire duration of the service. We help our customers to bring the security of their “intelligent” services to a higher level, allowing them to seize new opportunities with confidence, and look to the future.
The purpose of this white paper is to provide a high-level information line on the topic of AML based on the knowledgethat we as Reply have developed in years of study and research.
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