Detection of Malicious Executables Using Rule Based Classification Algorithms

• Autorzy: Bhargava N. , Jain A. , Kumar A. , Le D.-N.

Identyfikatory

DOI

10.15439/2017KM04

• Konferencja: International Conference on Information Technology and Knowledge Management (1 ; 22-23.12.2017 ; New Delhi, India)
• Języki publikacji: EN

Abstrakty

EN

Machine Learning class rule has varied packages together with classification, clustering, will understand association rules furthermore and is capable of the method an enormous set of the information set as measure supervised or unsupervised learning data. The paper deals with statistics mining sort set of rules on virus dataset created records from varied anti-virus logs. The work deals with classifications of malicious code per their impact on user's system \& distinguishes threats on the muse in their connected severity; these threads are therefore named as malicious possible from varied sources, on various running structures. During this paper, the generated output is that the listing of records summarizing however because it ought to be the classifier algorithms are ready to predict the authentic magnificence of the days at a lower place the chosen take a look at module. The operating model deals with predicting the outliers of the threat datasets and predicts the optimum results supported analysis victimization the chosen rule. The work illustrates implementation of the algorithms corresponding to half, JRIP and RIDOR in additional economical manner because it relies on virus-log datasets to come up with A level of accuracy to the classification results.

Słowa kluczowe

EN

threat; rule-based classification; prediction of severity; malicious executable; danger; norm

PL

zagrożenie; klasyfikacja oparta na regułach; przewidywanie nasilenia; złośliwy plik wykonywalny; niebezpieczeństwo; norma

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 14
• Strony: 35--38
• Opis fizyczny: Bibliogr. 17 poz., tab., wykr., il.

Twórcy

autor

Bhargava N.

• School of system sc. &engg., MDS University Ajmer, India

autor

Jain A.

• MDS University Ajmer, India

autor

Kumar A.

• Computer Science Department ACERC, Ajmer

autor

Le D.-N.

• Deputy-Head, Faculty of Information Technology, Haiphong University Haiphong, Vietnam

Bibliografia

• 1. W. Nor Haizan W. Mohamed, Mohd Najib Mohd Salleh, Abdul Halim Omar “A Comparative Study of Reduced Error Pruning Method in Decision Tree Algorithms,2012 IEEE International Conference on Control System, Computing and Engineering”, 23–25 Nov. 2012, Penang, Malaysia.
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• 3. Thorsten Lehr, Jing Yuan, Dirk Zeumer, SupriyaJayadev, and Marylyn D RitchiRule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies Published online 2011 Mar 1. http://dx.doi.org/10.1186/1756-0381-4-4
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• 7. Neeraj Bhargava, Sonia Dayma, Abhishek Kumar, Pramod Singh IEEE Sponsored 3rd International Conference on Electronics and Communication Systems (ICECS 2016) An Approach for Classification using Simple CART Algorithm in Weka, MDS University Ajmer India.
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• 17. C. Developers, “Cuckoo sandbox - open source automated malwareanalysis,” 2016. [Online]. Available: https://media.blackhat.com/us-13/US-13-Bremer-Mo-Malware-Mo-Problems-Cuckoo-Sandbox-WP.pdf

Uwagi

1. Preface

2. Technical Session: First International Conference on Information Technology and Knowledge Management