A learning model to detect maliciousness of portable executable using integrated feature set
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TLDR
A machine learning based solution to classify a sample as benign or malware with high accuracy and low computation overhead is proposed and empirical evidence indicates 98.4% classification accuracy in the 10-fold cross validation for the proposed integrated feature set.About:
This article is published in Journal of King Saud University - Computer and Information Sciences.The article was published on 2017-01-31 and is currently open access. It has received 82 citations till now. The article focuses on the topics: Malware & Naive Bayes classifier.read more
Citations
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Journal ArticleDOI
Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm
TL;DR: It was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage, and low FPR indicates that LA has a low probability of predicting goodware wrongly.
Proceedings ArticleDOI
An Efficient Approach For Malware Detection Using PE Header Specifications
Tina Rezaei,Ali Hamze +1 more
TL;DR: To identify malware programs, features extracted based on the header and PE file structure are used to train several machine learning models and the proposed method identifies malware programs with 95.59% accuracy using only nine features.
Journal ArticleDOI
Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM
M. A. Ajay Kumara,C. D. Jaidhar +1 more
TL;DR: An advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS.
Journal ArticleDOI
A Survey of Malware Detection Techniques based on Machine Learning
TL;DR: A survey of available researches utilizing the heuristic technique based on machine learning to counter cyber-attacks is provided, which has proven its success in several areas based on the processing of huge amounts of data.
Journal ArticleDOI
Windows Malware Detector Using Convolutional Neural Network Based on Visualization Images
Shiva Darshan S.L,Jaidhar C D +1 more
TL;DR: A Convolutional Neural Network based Windows malware detector has been proposed that uses the execution time behavioural features of the Portable Executable (PE) files to detect and classify obscure malware.
References
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Proceedings ArticleDOI
Data mining methods for detection of new malicious executables
TL;DR: This work presents a data mining framework that detects new, previously unseen malicious executables accurately and automatically and more than doubles the current detection rates for new malicious executable.
Journal ArticleDOI
A survey on automated dynamic malware-analysis techniques and tools
TL;DR: An overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples and analysis programs that employ these techniques to assist human analysts in assessing whether a given sample deserves closer manual inspection due to its unknown malicious behavior is provided.