Malicious Software Family Classification using Machine Learning Multi-class Classifiers

San, Cho Cho; Thwin, Mie Mie Su; Htun, Naing Linn

doi:10.1007/978-981-13-2622-6_41

Cited by 13 publications

(9 citation statements)

References 6 publications

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“…Comparing our results to the results of the other baseline classification model [6] and other related work [9][10][11]. Our proposed model shows improvement in classification accuracy as shown in table 6.…”

Section: Resultsmentioning

confidence: 55%

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Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

El-Rahmany

Mohamed

Haggag

2021

JCOMSS

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Section: Resultsmentioning

confidence: 55%

“…Cho Cho San [10] proposes a classification system for malware detection. They classify 11 malicious families.…”

Section: Related Workmentioning

confidence: 99%

Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

El-Rahmany

Mohamed

Haggag

2021

JCOMSS

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“…In (San, et al 2019), a malware classification system was proposed for 11 families. To classify the malware samples authors used three machine learning algorithms: Random Forest, K-Nearest Neighb,or and Decision Table . Best accuracy was given by RF and KNN with a value of 95,8%.…”

Section: Related Workmentioning

confidence: 99%

Proposed Precautions for Newborn Malware Family Inspired from the COVID19 Epidemic Outbreak

Ouahab

Bouhorma

Elaachak

et al. 2021

Advances in Science, Technology &Amp; Innovation

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Malicious software attacks cause serious loss to computer users, from personal usage to industrial networks. For this reason, researchers focused more and more on analyzing and detecting malware. Approaches found in literature can well predict a new malware sample belonging to known families, but what about newborn families. In this paper, we perform malware classifiers based on two machine learning algorithms: Random forest and K-Nearest Neighbor. We used the malware visualization technique, so a malware binary is presented as a grayscale image. After that, we calculated the GIST descriptor features for all samples to be ready for the classification. Results obtained are, respectively, 97, 99%, and 98,21% for the two algorithms. Then, we study the behavior of our classifier in the case of new arrival family. Next, we get inspired from the COVID19 disease outbreak. So we proposed precautions to be made as security measures in case of new malware family appearance. With the goal to reduce damage causes by these kind of attacks.

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“…The paper [9], proposed a structure in characterizing eleven groups of malwares. This was accomplished by removing their unmistakable API columns from the report's scalable version of cuckoo sandbox.…”

Section: Related Workmentioning

confidence: 99%

A Mult-Task System for Detecting and Classifying Malware Signatures Using Random Forest Classifier

Taylor¹,

Ezekiel²,

Emmah³

2021

AIMS Research Publication

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The rapid increase in the use of information technology has made cyber-attacks a major concern in the use of internet by users globally. These attacks are carried out in different forms, some are carried out as phishing, man in the middle, malicious applications and so on. In this study we will focus on malware attack. Malicious applications have been a major challenge in the use of applications on windows operating system. These malicious attacks are being carried out in different forms. Some of these attacks are trojan, ransom, keylogger etc. The need to detect and classifier these malicious attacks in windows operating system is an important task. So therefore, this paper presents a smart system for detecting and classifying eight categories of malware attack on windows operating system using random forest classifier. The system starts by collecting signatures of malware attack on windows from Virus Share, Virus Sign and Github respiratory. The collected malware signatures went through the following stages of preprocessing (First stage, Second Stage, and Third Stage). The first stage has to do with creating a pandas. Dataframe using the malware signatures. The second stage has to with data cleaning and the third stage has to do with data transformation. The result of the Random Forest Classifier shows a promising performance in terms of accuracy, precision, f1-score, and recall. The result shows that the Random Forest Classifier has an accuracy of about 100% for each of the matrix evaluation. Keywords- Malware signatures, Random Forest Classifier, Windows operating System, Matrix Evaluation

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Malicious Software Family Classification using Machine Learning Multi-class Classifiers

Cited by 13 publications

References 6 publications

Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

Proposed Precautions for Newborn Malware Family Inspired from the COVID19 Epidemic Outbreak

A Mult-Task System for Detecting and Classifying Malware Signatures Using Random Forest Classifier

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