A learning model to detect maliciousness of portable executable using integrated feature set

Published in

Journal of King Saud University: Computer and I...(2017)

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TL;DR

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.

Authors

Ajit Kumar

1 papers

K. Kuppusamy

1 papers

G. Aghila

1 papers

References33 items

Structural analysis of binary executable headers for malware detection optimization

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

A Real-Time PE-Malware Detection System Based on CHI-Square Test and PE-File Features

Machine Learning Based Malware Detection

Building a machine learning classifier for malware detection

A learning model to detect maliciousness of portable executable using integrated feature set

Published in

Journal of King Saud University: Computer and I...(2017)

External Links:

Generate Graph DownloadPDF

TL;DR

Authors

Ajit Kumar

1 papers

K. Kuppusamy

1 papers

G. Aghila

1 papers

References33 items

Structural analysis of binary executable headers for malware detection optimization

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

A Real-Time PE-Malware Detection System Based on CHI-Square Test and PE-File Features

Machine Learning Based Malware Detection

Building a machine learning classifier for malware detection

A Malware Detection Scheme Based on Mining Format Information

A Static Malware Detection System Using Data Mining Methods

Exploring Discriminatory Features for Automated Malware Classification

Malware detection based on evolving clustering method for classification

A survey on automated dynamic malware-analysis techniques and tools

Scattered Feature Space for Malware Analysis

Malware detection using assembly and API call sequences

Scikit-learn: Machine Learning in Python

Header information in malware families and impact on automated classifiers

Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection

A Trojan Horse Detection Technology Based on Behavior Analysis

Malware detection based on mining API calls

Idea: Opcode-Sequence-Based Malware Detection

PE-Miner: Mining Structural Information to Detect Malicious Executables in Realtime

Detecting Unknown Malicious Executables Using Portable Executable Headers

A heuristic approach for detection of obfuscated malware

Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

Unknown Malcode Detection Using OPCODE Representation

Classification of packed executables for accurate computer virus detection

An intelligent PE-malware detection system based on association mining

Opcodes as predictor for malware

N-gram-based detection of new malicious code

Data mining methods for detection of new malicious executables

A receiver operating characteristic partial area index for highly sensitive diagnostic tests.

On the statistical analysis of ROC curves.

Analyzing a Portion of the ROC Curve

A mathematical theory of communication

Peering Inside the PE: A Tour of the Win32 Portable Executable File Format

Field of Study

Computer Science

Journal Information

Name

J. King Saud Univ. Comput. Inf. Sci.

Page

243-257

Volume

Venue Information

Name

Journal of King Saud University: Computer and Information Sciences

Type

journal

URL

https://www.journals.elsevier.com/journal-of-king-saud-university-computer-and-information-sciences/

Alternate Names

Journal of King Saud University - Computer and Information Sciences
J King Saud Univ Comput Inf Sci
J King Saud Univ Comput Inf Sci