深入研究机器学习和深度学习方法在网络安全领域的重要性:详尽分析

Ramsha Khalid, Muhammad Naqi Raza
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引用次数: 0

摘要

:在当今的数字时代,个人广泛接触虚拟环境,这些环境承载着大量的公共和私人服务以及社交平台。因此,保护这些环境免受数据泄露和系统中断等潜在网络威胁变得至关重要。网络安全包括一整套技术、组织和管理措施,旨在防止未经授权访问或滥用电子信息和通信系统。其目标包括确保业务连续性、保障敏感数据的保密性和完整性,以及保护消费者免受各种形式的网络入侵。本文深入探讨了网络安全实践领域,这些实践旨在强化计算机系统,使其免受黑客攻击和数据泄露等各种威胁。本文探讨了人工智能在这一领域的关键作用,对机器学习和深度学习技术的应用提出了见解。此外,它还综合了相关文献的主要研究成果,探讨了这些先进方法在网络安全方面的功效和影响。研究结果强调了机器学习和深度学习技术在强化计算机系统、防止未经授权的访问和降低恶意软件带来的风险方面做出的巨大贡献。这些方法通过预测和理解与潜在网络威胁相关的行为模式和流量,有助于采取积极主动的措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Thorough Examination of the Importance of Machine Learning and Deep Learning Methodologies in the Realm of Cybersecurity: An Exhaustive Analysis
: In today's digital age, individuals extensively engage with virtual environments hosting a plethora of public and private services alongside social platforms. As a consequence, safeguarding these environments from potential cyber threats such as data breaches and system disruptions becomes paramount. Cybersecurity encompasses a suite of technical, organizational, and managerial measures aimed at thwarting unauthorized access or misuse of electronic information and communication systems. Its objectives include ensuring operational continuity, safeguarding the confidentiality and integrity of sensitive data, and shielding consumers from various forms of cyber intrusions. This paper delves into the realm of cybersecurity practices devised to fortify computer systems against diverse threats including hacking and data breaches. It examines the pivotal role of artificial intelligence within this domain, offering insights into the utilization of machine learning and deep learning techniques. Moreover, it synthesizes key findings from relevant literature exploring the efficacy and impact of these advanced methodologies in cybersecurity. Findings underscore the substantial contributions of machine learning and deep learning techniques in fortifying computer systems against unauthorized access and mitigating the risks posed by malicious software. These methodologies facilitate proactive measures by predicting and comprehending the behavioral patterns and traffic associated with potential cyber threats.
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