What is malware classification?

What is malware classification?

Malware Classification is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted.

How classification works in data mining?

Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.

What is malimg dataset?

The Malimg dataset contains 9,339 malware images from 25 families, while Malicia has 11,668 malware binaries from 54 families. The Malimg dataset consists of images, and hence these samples require no pre-processing before ap- plying image-based analysis.

What is data mining malware?

Data mining techniques rely on data sets that contain some individual configurations for the malicious files and benign software to construct the classification methods for malware detection [13, 14].

What are the 5 types of malware?

What are the different types of Malware?

• Worms. Worms are spread via software vulnerabilities or phishing attacks.
• Viruses. Unlike worms, viruses need an already-infected active operating system or program to work.
• Bots & Botnets.
• Trojan Horses.
• Ransomware.
• Adware & Scams.
• Spyware.
• Spam & Phishing.

Why is malware classification important?

Distinguishing and classifying different types of malware from each other is important to better understanding how they can infect computers and devices, the threat level they pose and how to protect against them. …

How does machine learning detect malware?

In other words, a machine learning algorithm discovers and formalizes the principles that underlie the data it sees. With this knowledge, the algorithm can ‘reason’ the properties of previously unseen samples. In malware detection, a previously unseen sample could be a new file.

How is malware analysis done?

There are two ways to approach the malware analysis process — using static analysis or dynamic analysis. With static analysis, the malware sample is examined without detonating it, whereas, with dynamic analysis, the malware is actually executed in a controlled, isolated environment.

What are the different types of malware programs?

Malware is a program designed to gain access to computer systems, normally for the benefit of some third party, without the user’s permission. Malware includes computer viruses, worms, Trojan horses, ransomware, spyware and other malicious programs.

How is machine learning used for malware detection?

Today, machine learning boosts malware detection using various kinds of data on host, network and cloud-based anti-malware components. Machine Learning Methods for Malware Detection In this paper, we summarize our extensive experience using machine learning to build advanced protection for our customers.

How is data mining used to detect malware?

Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior. We proposed different classification methods in order to detect malware based on the feature and behavior of each malware.

How is dynamic analysis used to detect malware?

A dynamic analysis method has been presented for identifying the malware features. A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input.