NDLI: Malware Detection by Data Mining Techniques Based on Positionally Dependent Features

Content Provider	IEEE Xplore Digital Library
Author	Komashinskiy, D. Kotenko, I.
Copyright Year	2010
Abstract	The challenges being thrown to modern world by the need to counteract against malicious software (malware) are going on to increase own importance. This fact stays actual, in spite of obvious great results in improving the efficacy of procedures of malware propagation detection, analysis and updating the bases of signatures and detection rules. The important aspect of this problem is looking for more reliable heuristic detection methods. These methods focus on recognition of new (unknown before) malicious programs which can not be detected by using traditional signature- and rule-based detection techniques, oriented on search for concrete malware samples and families. Virtually, just these heuristic methods provide counteraction against targeted and zero-day attacks, since the rate of detecting such relatively new types of threats by traditional techniques is not enough. The presented paper is devoted to using Data Mining methods for constructing heuristic malware detectors. The approach described below differs from others by focusing on processing static positionally dependent features which consider the specificities of object’s file format of potential malware containers. The paper describes the realization and investigation of the common methodology for design of Data Mining-based malware detectors’ using positionally dependent static information.
Starting Page	617
Ending Page	623
File Size	276464
Page Count	7
File Format	PDF
ISBN	9781424456727
ISSN	10666192
e-ISBN	9781424456734
DOI	10.1109/PDP.2010.30
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-17
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Phase detection Detectors Computer security Informatics Automation Electronic mail Concrete Containers Design methodology static and dynamic detection classification positionally dependent features feature selection malware
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A New Android Malware Detection Approach Using Bayesian Classification

Android malware detection from Google Play meta-data: Selection of important features

A feature selection method for malware detection

The analysis of feature selection methods and classification algorithms in permission based Android malware detection

Smartphone malware detection model based on artificial immune system

Malicious Executables Classification Based on Behavioral Factor Analysis

Malware Analysis with Multiple Features

Android Malware Detection Using Parallel Machine Learning Classifiers

A Simhash-Based Integrative Features Extraction Algorithm for Malware Detection

Malware Detection by Data Mining Techniques Based on Positionally Dependent Features

Similar Documents

A New Android Malware Detection Approach Using Bayesian Classification

Android malware detection from Google Play meta-data: Selection of important features

A feature selection method for malware detection

The analysis of feature selection methods and classification algorithms in permission based Android malware detection

Smartphone malware detection model based on artificial immune system

Malicious Executables Classification Based on Behavioral Factor Analysis

Malware Analysis with Multiple Features

Android Malware Detection Using Parallel Machine Learning Classifiers

A Simhash-Based Integrative Features Extraction Algorithm for Malware Detection

Malware Detection by Data Mining Techniques Based on Positionally Dependent Features