Integrating Natural Language Processing with Cybersecurity Protocols: Real-Time Analysis of Malicious Intent in Social Engineering Attack

Sridevi Kakolu 1, 2, *, Muhammad Ashraf Faheem 3, 4 and Muhammad Aslam 3, 5

1 Boardwalk Pipelines, Houston, Texas, USA.
2 Jawaharlal Nehru Technological University, Hyderabad, India.
3 Speridian Technologies, Lahore, Pakistan.
4 Lahore Leads University, Lahore, Pakistan.
5 University of Punjab, Lahore, Pakistan
 
Review
International Journal of Science and Research Archive, 2020, 01(01), 082–095.
Article DOI: 10.30574/ijsra.2020.1.1.0020
Publication history: 
Received on 25 September 2020; revised on 20 December 2020; accepted on 24 December 2020
 
Abstract: 
Natural Language Processing (NLP) is a great way of supplementing cybersecurity. It has come into its own when fighting social engineering attacks, which feed on human weaknesses. We explore the possibilities of NLP when detecting malicious intent, using linguistic analysis to identify the threat (and mitigate the risk) in advance. With sentiment analysis, keyword detection, and contextual understanding, NLP can alert people about potentially harmful communications before a security incident. Including machine learning models makes NLP flexible enough to change with emerging attack patterns. To illustrate the usage of NLP, several industries are presented in the form of real-world case studies of successful NLP applications, with substantial accuracy improvement in the detection of threats and huge savings in cost. Although limited data and ethical and privacy concerns may be faced, the merits of leveraging NLP on cybersecurity are vast. Organizations can use a stronger security posture, lower operational costs, and faster threat response. In the future, as NLP technology improves, cybersecurity systems will become more adept at fighting language-based threats that are becoming more sophisticated.
 
Keywords: 
Natural Language Processing (NLP); Cybersecurity; Threat Detection; Social Engineering; Phishing Attacks
 
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0