{"title":"Conglomerate Stratum Model for Categorization of Malware Family in Image Processing","authors":"Rupali Komatwar, Manesh Kokare","doi":"10.26599/fie.2023.9270016","DOIUrl":null,"url":null,"abstract":"In recent years, there has been an enormous increase in the volume of malware generation and the classification of malware samples plays a crucial role in building and maintaining security. Hence, there is a need to explore new approaches to overcome the limitations of malware classification such as pre-combustion, peculiarity eradication, and categorization. To overcome these issues, this paper proposes a novel Conglomerate Stratum Model (CSM), which categorizes them into groups and identifies their respective families based on their behavior. Initially, the precombustion process used Triad Seeped Technique (TST) in which the image is first regularized by applying ripples. Secondly, we introduced a Quatrain Layer Method (QLM) to upgrade the robustness of malware image features in peculiarity eradication. Then the specific output of the quatrain layer is given to Acclimatized Patronage Scheme (APS) for categorization, and this process effectively classifies the malware types with greater accuracy. The results demonstrate that our model can achieve 99.41% accuracy in classifying malware samples. Also, the values of sensitivity, precision, negative predictive, and recall are higher than 0.9 with the false-negative rate of 0.04, and the false-positive rate 0.003 proving the model to be optimistic. The experimental comparison demonstrates its superior performance concerning state-of-the-art techniques.","PeriodicalId":37623,"journal":{"name":"Fuzzy Information and Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fuzzy Information and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26599/fie.2023.9270016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, there has been an enormous increase in the volume of malware generation and the classification of malware samples plays a crucial role in building and maintaining security. Hence, there is a need to explore new approaches to overcome the limitations of malware classification such as pre-combustion, peculiarity eradication, and categorization. To overcome these issues, this paper proposes a novel Conglomerate Stratum Model (CSM), which categorizes them into groups and identifies their respective families based on their behavior. Initially, the precombustion process used Triad Seeped Technique (TST) in which the image is first regularized by applying ripples. Secondly, we introduced a Quatrain Layer Method (QLM) to upgrade the robustness of malware image features in peculiarity eradication. Then the specific output of the quatrain layer is given to Acclimatized Patronage Scheme (APS) for categorization, and this process effectively classifies the malware types with greater accuracy. The results demonstrate that our model can achieve 99.41% accuracy in classifying malware samples. Also, the values of sensitivity, precision, negative predictive, and recall are higher than 0.9 with the false-negative rate of 0.04, and the false-positive rate 0.003 proving the model to be optimistic. The experimental comparison demonstrates its superior performance concerning state-of-the-art techniques.
期刊介绍:
Fuzzy Information and Engineering—An International Journal wants to provide a unified communication platform for researchers in a wide area of topics from pure and applied mathematics, computer science, engineering, and other related fields. While also accepting fundamental work, the journal focuses on applications. Research papers, short communications, and reviews are welcome. Technical topics within the scope include: (1) Fuzzy Information a. Fuzzy information theory and information systems b. Fuzzy clustering and classification c. Fuzzy information processing d. Hardware and software co-design e. Fuzzy computer f. Fuzzy database and data mining g. Fuzzy image processing and pattern recognition h. Fuzzy information granulation i. Knowledge acquisition and representation in fuzzy information (2) Fuzzy Sets and Systems a. Fuzzy sets b. Fuzzy analysis c. Fuzzy topology and fuzzy mapping d. Fuzzy equation e. Fuzzy programming and optimal f. Fuzzy probability and statistic g. Fuzzy logic and algebra h. General systems i. Fuzzy socioeconomic system j. Fuzzy decision support system k. Fuzzy expert system (3) Soft Computing a. Soft computing theory and foundation b. Nerve cell algorithms c. Genetic algorithms d. Fuzzy approximation algorithms e. Computing with words and Quantum computation (4) Fuzzy Engineering a. Fuzzy control b. Fuzzy system engineering c. Fuzzy knowledge engineering d. Fuzzy management engineering e. Fuzzy design f. Fuzzy industrial engineering g. Fuzzy system modeling (5) Fuzzy Operations Research [...] (6) Artificial Intelligence [...] (7) Others [...]