Classifying Malicious Documents on the Basis of Plain-Text Features: Problem, Solution, and Experiences
Year: 2022
Type: article
Abstract: Cyberattacks widely occur by using malicious documents. A malicious document is an electronic document containing malicious codes along with some plain-text data that is human-readable. In this paper, we propose a novel framework that takes advantage of such plaintext data to determine whether a given document is malicious. We extracted plaintext features from the corpus of electronic documents and utilized them to train a classification model for detecting malicious documents. Our extensive experimental results with different combinations of three well-known vectorization strategies and three popular classification methods on five types of electronic documents demonstrate that our framework provides high prediction accuracy in detecting malicious documents.
Source: Applied Sciences
DOI: 10.3390/app12084088
Authors: Jiwon Hong, Dongho Jeong, Sang-Wook Kim
Institution: Hanyang University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,408
OpenAlex ID: W4224253745