Information Security and Machine Learning: Encryption Allocation Based on Recognizing of Text Patterns

SANTOS, PEDRO HENRIQUE JUNIO; COSTA, CARLOS AUGUSTO DE FARIA; CARVALHO, LUIZ MELK DE; Souza, Flávio Henrique Batista de; Rodrigues, Diva de Souza e Silva

doi:10.14488/ijcieom2020_full_0007_37130

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“…This relevant point exponentially increases the demand for data security studies, since the world population has become totally dependent on connections from cloud systems and software, due to the imposed social isolation scenario. The fact that the world has become totally dependent on communication technologies has not exempted it from attacks and intrusion attempts (Lallie et al, 2020;Santos et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Against Malwares: Analysis On Windows Systems Based in Pattern Recognition Algorithms

Senna¹

2021

Proceedings of ‏The 3rd International Conference on Research in Science, Engineering and Technology

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In 2020, due to the pandemic by COVID-19, the use of digital resources increased considerably, unprecedented for providers and developers of digital services. An issue that accompanies such a process is the security and identification of malware, which spreads at a speed never seen before. Thus, the objective of this paper is to demonstrate an evaluation, based on an experimental process, of pattern recognition algorithms (Multilayer Perceptron-MLP; Naive Bayes, K-Nearest Neighbor and Decision Tree) for the recognition of malware patterns in Portable Executable (PE) Files Structures. As a methodology for experimentation, three steps were taken: collection of malware samples in a repository known as Virusshare (around 285 malware samples -from 6 different families -for Windows® from 2012 to 2019) and PE files (initially 549 were randomly selected PE files from "System32" and "Program and Files") totaling 834 samples; 2,500 features were defined to compose the analysis dataset; finally, the test methodologies were defined according to the pattern recognition algorithm (e.g. in MLP, the number of hidden layers and the number of neurons in these hidden layers were varied). As a result, after 100 execution of each algorithm configuration, an accuracy ranging from 64.5% (Naive Bayes) to 95% (MLP) was obtained.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Against Malwares: Analysis On Windows Systems Based in Pattern Recognition Algorithms

Senna¹

2021

Proceedings of ‏The 3rd International Conference on Research in Science, Engineering and Technology

View full text Add to dashboard Cite

show abstract

Information Security and Machine Learning: Encryption Allocation Based on Recognizing of Text Patterns

Cited by 1 publication

References 0 publications

Machine Learning Against Malwares: Analysis On Windows Systems Based in Pattern Recognition Algorithms

Machine Learning Against Malwares: Analysis On Windows Systems Based in Pattern Recognition Algorithms

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