What are the challenges set by malware?

What are the challenges set by malware?

Many important problems in malware analysis have been shown to be undecidable. These problems include virus detection, detecting unpacking execution, matching malware samples against a set of given templates, and detecting trigger-based behavior.

Can ml predict future?

Researchers have tried various ways to help computers predict what might happen next. Existing approaches train a machine-learning model frame by frame to spot patterns in sequences of actions. The AI can make guesses about the future without having to learn anything about the progression of time, says Vlontzos.

Can a malware classification be done by machine learning?

Malware classification is a widely used task that, as you probably know, can be accomplished by machine learning models quite efficiently. In this article, I have decided to focus on an interesting malware classification method based on Convolutional Neural Networks.

How is machine learning used in Computer Science?

Machine Learning is a subfield of computer science that aims to give computers the ability to learn from data instead of being explicitly programmed, thus leveraging the petabytes of data that exists on the internet nowadays to make decisions, and do tasks that are somewhere impossible or just complicated and time consuming for us humans.

Why is it important to detect and prevent malware?

Nowadays, there are countless types of malware attempting to damage companies’ information systems. Thus, it is essential to detect and prevent them to avoid any risk. Malware classification is a widely used task that, as you probably know, can be accomplished by machine learning models quite efficiently.

What kind of subfield is machine learning?

Machine Learning is a subfield that mixes many domains of mathematics mainly Statistics and Probabilities and Linear Algebra and Computation (Algorithms, Data Processing, Numerical Calculations).