Shivashankar Hiremath, Eeshan Shetty, A. J. Prakash, S. Sahoo, Kiran Kumar Patro, Kandala N. V. P. S. Rajesh, Paweł Pławiak
{"title":"利用机器学习进行网络安全数据分析的新方法","authors":"Shivashankar Hiremath, Eeshan Shetty, A. J. Prakash, S. Sahoo, Kiran Kumar Patro, Kandala N. V. P. S. Rajesh, Paweł Pławiak","doi":"10.3390/bdcc7040176","DOIUrl":null,"url":null,"abstract":"The internet has become an indispensable tool for organizations, permeating every facet of their operations. Virtually all companies leverage Internet services for diverse purposes, including the digital storage of data in databases and cloud platforms. Furthermore, the rising demand for software and applications has led to a widespread shift toward computer-based activities within the corporate landscape. However, this digital transformation has exposed the information technology (IT) infrastructures of these organizations to a heightened risk of cyber-attacks, endangering sensitive data. Consequently, organizations must identify and address vulnerabilities within their systems, with a primary focus on scrutinizing customer-facing websites and applications. This work aims to tackle this pressing issue by employing data analysis tools, such as Power BI, to assess vulnerabilities within a client’s application or website. Through a rigorous analysis of data, valuable insights and information will be provided, which are necessary to formulate effective remedial measures against potential attacks. Ultimately, the central goal of this research is to demonstrate that clients can establish a secure environment, shielding their digital assets from potential attackers.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"1 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Approach to Data Analysis Using Machine Learning for Cybersecurity\",\"authors\":\"Shivashankar Hiremath, Eeshan Shetty, A. J. Prakash, S. Sahoo, Kiran Kumar Patro, Kandala N. V. P. S. Rajesh, Paweł Pławiak\",\"doi\":\"10.3390/bdcc7040176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The internet has become an indispensable tool for organizations, permeating every facet of their operations. Virtually all companies leverage Internet services for diverse purposes, including the digital storage of data in databases and cloud platforms. Furthermore, the rising demand for software and applications has led to a widespread shift toward computer-based activities within the corporate landscape. However, this digital transformation has exposed the information technology (IT) infrastructures of these organizations to a heightened risk of cyber-attacks, endangering sensitive data. Consequently, organizations must identify and address vulnerabilities within their systems, with a primary focus on scrutinizing customer-facing websites and applications. This work aims to tackle this pressing issue by employing data analysis tools, such as Power BI, to assess vulnerabilities within a client’s application or website. Through a rigorous analysis of data, valuable insights and information will be provided, which are necessary to formulate effective remedial measures against potential attacks. Ultimately, the central goal of this research is to demonstrate that clients can establish a secure environment, shielding their digital assets from potential attackers.\",\"PeriodicalId\":36397,\"journal\":{\"name\":\"Big Data and Cognitive Computing\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Data and Cognitive Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/bdcc7040176\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data and Cognitive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/bdcc7040176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
互联网已成为企业不可或缺的工具,渗透到企业运营的方方面面。几乎所有公司都利用互联网服务来实现各种目的,包括在数据库和云平台中以数字方式存储数据。此外,对软件和应用程序的需求不断增长,导致企业内部普遍转向基于计算机的活动。然而,这种数字化转型使这些组织的信息技术(IT)基础设施面临更高的网络攻击风险,从而危及敏感数据。因此,企业必须识别并解决系统中的漏洞,重点是仔细检查面向客户的网站和应用程序。这项工作旨在利用 Power BI 等数据分析工具来评估客户应用程序或网站中的漏洞,从而解决这一紧迫问题。通过对数据的严格分析,将提供有价值的见解和信息,这些见解和信息是针对潜在攻击制定有效补救措施所必需的。最终,本研究的核心目标是证明客户可以建立一个安全的环境,保护其数字资产免受潜在攻击者的侵害。
A New Approach to Data Analysis Using Machine Learning for Cybersecurity
The internet has become an indispensable tool for organizations, permeating every facet of their operations. Virtually all companies leverage Internet services for diverse purposes, including the digital storage of data in databases and cloud platforms. Furthermore, the rising demand for software and applications has led to a widespread shift toward computer-based activities within the corporate landscape. However, this digital transformation has exposed the information technology (IT) infrastructures of these organizations to a heightened risk of cyber-attacks, endangering sensitive data. Consequently, organizations must identify and address vulnerabilities within their systems, with a primary focus on scrutinizing customer-facing websites and applications. This work aims to tackle this pressing issue by employing data analysis tools, such as Power BI, to assess vulnerabilities within a client’s application or website. Through a rigorous analysis of data, valuable insights and information will be provided, which are necessary to formulate effective remedial measures against potential attacks. Ultimately, the central goal of this research is to demonstrate that clients can establish a secure environment, shielding their digital assets from potential attackers.