Dark Reading is part of the Informa Tech Division of Informa PLC
This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.
Black Hat USA 2022 Attendee ReportBlack Hat attendees are not sleeping well. Between concerns about attacks against cloud services, ransomware, and the growing risks to the global supply chain, these security pros have a lot to be worried about. Read our 2022 report to hear what they're concerned about now.
Incident Readiness and Building Response Playbook
In this special report, learn the Xs and Os of any good security incident readiness and response playbook.
Tweet This
1 Comments
User Rank: Ninja
6/29/2019 | 2:32:45 PM
One thing I would say about AI, the term is not being used correctly. It is machine learning and not AI. ML is a subcomponent of AI. By definition:
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics. - Wikipedia.org.
When we refer to AI, it means the system is self aware and it is able to make decisions without the intervention of a human (it thinks like a human). It can provide an instant response to a threat because it has taken information from numerous resources, created a prioritized depth chart with varying threat percentages from a list of past models and threats. This analysis helps the system determine if it is the same threat experienced by others or a zero day attack. Then it looks into a resolution DB (Deep Learning or Machine Learning) or it identifies areas on the internet as to how to deal with the threat, it communicates that with the human element and rectifys the problem using ML/DL experiences.
I think individuals are mixing the concepts up and not really understanding the differences between the two, a chart has been provided to help individuals understand the differnt between the three areas.