论文标题
使用ML Technologies检测Covid-19的恶意URL
Detecting Malicious URLs of COVID-19 Pandemic using ML technologies
论文作者
论文摘要
在整个Covid-19爆发中，恶意袭击变得比以往任何时候都变得更加普遍和破坏。恶意入侵者是最近犯下的大多数网络犯罪的原因，这是越来越多的网络威胁的原因，包括身份和IP盗窃，金融犯罪和关键基础设施的网络攻击。在过去的十年中，机器学习（ML）通过解决了许多高度复杂且复杂的现实世界中的问题。本文提出了一种基于ML的分类技术，以检测由于COVID-19的大流行，目前被认为对IT使用者构成了威胁。我们使用了大量的开源数据，并使用开发的工具对其进行了预处理，以生成特征向量，并使用令人担忧的恶意威胁重量训练了ML模型。我们的ML模型已经进行了测试，没有熵，以预测Covid-19 URL的威胁因素。经验证据证明，我们的方法是减轻攻击生命周期早期相关威胁的有前途的机制。
Throughout the COVID-19 outbreak, malicious attacks have become more pervasive and damaging than ever. Malicious intruders have been responsible for most cybercrimes committed recently and are the cause for a growing number of cyber threats, including identity and IP thefts, financial crimes, and cyber-attacks to critical infrastructures. Machine learning (ML) has proven itself as a prominent field of study over the past decade by solving many highly complex and sophisticated real-world problems. This paper proposes an ML-based classification technique to detect the growing number of malicious URLs, due to the COVID-19 pandemic, which is currently considered a threat to IT users. We have used a large volume of Open Source data and preprocessed it using our developed tool to generate feature vectors and we trained the ML model using the apprehensive malicious threat weight. Our ML model has been tested, with and without entropy to forecast the threatening factors of COVID-19 URLs. The empirical evidence proves our methods to be a promising mechanism to mitigate COVID-19 related threats early in the attack lifecycle.
