Machine Learning and Images for Malware Detection and Classification
| Autor: | Christos Kalloniatis, Konstantinos Kosmidis |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Feature engineering Artificial neural network business.industry Computer science 02 engineering and technology computer.software_genre Machine learning Field (computer science) 03 medical and health sciences Identification (information) 030104 developmental biology Software 0202 electrical engineering electronic engineering information engineering Malware 020201 artificial intelligence & image processing Artificial intelligence Malware analysis business Cluster analysis computer |
| Zdroj: | PCI |
| DOI: | 10.1145/3139367.3139400 |
| Popis: | Detecting malicious code with exact match on collected datasets is becoming a large-scale identification problem due to the existence of new malware variants. Being able to promptly and accurately identify new attacks enables security experts to respond effectively.My proposal is to develop an automated framework for identification of unknown vulnerabilities by leveraging current neural network techniques. This has a significant and immediate value for the security field, as current anti-virus software is typically able to recognize the malware type only after its infection, and preventive measures are limited.Artificial Intelligence plays a major role in automatic malware classification: numerous machine-learning methods, both supervised and unsupervised, have been researched to try classifying malware into families based on features acquired by static and dynamic analysis.The value of automated identification is clear, as feature engineering is both a time-consuming and time-sensitive task, with new malware studied while being observed in the wild. |
| Databáze: | OpenAIRE |
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