"How can natural language processing improve cybersecurity threat detection?"

Abstract

Natural language processing (NLP) is being rapidly applied in cyber space, as its capacity
efficiently enables the process and the ability to analyze large versions of unnecessary text
data, acceleratory detecting and more effective and more effective danger. This paper
examines the integration of NLP in the manner of detecting the danger; the risk focuses on
its role in intelligence information, fishing detection, malware identification and user
behavioral analysis to identify discrepancy activities. Taking advantage of various machines
learning patterns, the NLP can increase the identification and accuracy of the danger and
prediction, including learning-pilgrimage, unheard, semi-conceptual, and reinforcement
learning. Many cases study email detection, expediting the reaction of the event and user
and unit behavioral analytics (UEBA) display their application. However, the study also
identifies challenges such as data quality issues, rapid development of dangers, language
ambiguity and adverse manipulation. Conclusions highlight that NLP has an important
ability to strengthen cyber security systems by combining the NLP such as advanced
approaches and combining with emerging technologies, making them more adaptive and
intelligent