Abstract
The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of penetration into the information systems where confidential information is processed is malware. An attacker injects malware into a computer system, after which he has full or partial access to critical information in the information system. This paper proposes an ensemble classification-based methodology for malware detection. The first-stage classification is performed by a stacked ensemble of dense (fully connected) and convolutional neural networks (CNN), while the final stage classification is performed by a meta-learner. For a meta-learner, we explore and compare 14 classifiers. For a baseline comparison, 13 machine learning methods are used: K-Nearest Neighbors, Linear Support Vector Machine (SVM), Radial basis function (RBF) SVM, Random Forest, AdaBoost, Decision Tree, ExtraTrees, Linear Discriminant Analysis, Logistic, Neural Net, Passive Classifier, Ridge Classifier and Stochastic Gradient Descent classifier. We present the results of experiments performed on the Classification of Malware with PE headers (ClaMP) dataset. The best performance is achieved by an ensemble of five dense and CNN neural networks, and the ExtraTrees classifier as a meta-learner.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference67 articles.
1. Cyber Security in the Age of COVID-19: A Timeline and Analysis of Cyber-Crime and Cyber-Attacks during the Pandemic;Lallie;arXiv,2020
2. Measuring the Cost of Cybercrime;Anderson,2013
3. The 2020 Cyber Security Reporthttps://pages.checkpoint.com/cyber-security-report-2020
4. Mobile Malware Evolutionhttps://securelist.com/mobile-malware-evolution-2018/89689/
5. The Great Bank Robberyhttps://www.kaspersky.com/about/press-releases/2015_the-great-bank-robbery-carbanak-cybergang-steals--1bn-from-100-financial-institutions-worldwide
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