A comprehensive literature review on ransomware detection using deep learning

Er. Kritika
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引用次数: 0

Abstract

The manifold rise in ransomware attacks noted highest in 2023 posing a serious trepidation for cyber professionals to be active watchdogs of the early detection techniques. Ransomware is a type of malware often used to encrypt the confidential user files and network and demanding a hefty ransome to decrypt it. The emergence of modern day technologies like artificial intelligence making it unchallenging for the novice attackers to use service platform such as RaaS to conduct the ransomware attack and victimize gullible individuals and organisations often demanding ransom in millions and billions. There exists the need to mitigate strategies using frameworks to combat such threats like deep learning which uses neural network to process and learn new information and train models on preprocessed data. The paper delves into providing the literature review on ransomware detection using deep learning techniques.
利用深度学习进行勒索软件检测的综合文献综述
勒索软件攻击的大幅增加在2023年达到了最高水平,这让网络专业人士感到严重恐慌,他们必须积极监督早期检测技术。勒索软件是一种恶意软件,通常用于加密机密用户文件和网络,并要求高额赎金才能解密。人工智能等现代技术的出现,使得新手攻击者使用RaaS等服务平台进行勒索软件攻击,并使容易上当受骗的个人和组织受害,通常要求数百万甚至数十亿美元的赎金。目前有必要缓解使用框架来对抗此类威胁的策略,例如使用神经网络处理和学习新信息并在预处理数据上训练模型的深度学习。本文深入研究了使用深度学习技术进行勒索软件检测的文献综述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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