Machine Learning Models for Cybersecurity in the USA firms and develop models to enhance threat detection

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Abstract

In the context of global digitalization trends, the problem of the impact of cyberattacks on the company is significantly relevant. The rapid evolution and growth of the internet through the last decades led to more concern about cyber-attacks that are continuously increasing and changing. As a result, an effective intrusion detection system was required to protect data, and the discovery of machine learning is one of the most successful ways to address this problem. This article is devoted to the impact of cyberattacks on the US firms’ market value since it is an indicator of firm performance and how it can be solved by using machine learning technology. The paper’s central hypothesis is the assumption that a cyberattack announcement is supposed to change market reaction, which is predicted to be harmful since cybercrime incidents can lead to high implicit and explicit costs. The paper explores the effect of firm-specific and attack-specific characteristics of cyberattacks on the CAR (Cumulative Abnormal Returns) with the data of cyberattacks for US firms from 2011 to 2020. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. It discusses recent machine learning work with various network implementations, applications, algorithms, learning approaches, and datasets to develop an operational intrusion detection system in cybersecurity. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity in US firms.

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