The Evolution of Distributed Denial of Service (DDoS) Attacks: NLP-Based Detection and Strategic Management Countermeasures in Modern Networks
DOI:
https://doi.org/10.5281/zenodo.13232838ARK:
https://n2t.net/ark:/40704/JETBM.v1n4a04Keywords:
Distributed Denial of Service (DDoS), Natural Language Processing (NLP), Cyber Attacks, Network Security, Threat Detection, incident Response, Business Continuity, Crisis Management, Botnets, Cybersecurity StrategyAbstract
Distributed Denial of Service (DDoS) attacks have evolved significantly over the years, becoming more sophisticated and challenging to mitigate. As organizations increasingly rely on modern networks for critical operations, the need for advanced detection and strategic management countermeasures has become paramount. This paper explores the evolution of DDoS attacks, focusing on the integration of Natural Language Processing (NLP) for enhanced detection and the implementation of strategic management practices to mitigate these threats. Through a comprehensive review of existing literature, case studies, and emerging technologies, the paper provides insights into the current state of DDoS defense mechanisms and proposes a framework for integrating NLP and management strategies to protect modern networks from these pervasive threats.
This study highlights how NLP techniques can analyze communication patterns within network traffic to identify early indicators of DDoS attacks, offering a proactive approach to threat detection. Additionally, the paper emphasizes the role of strategic management in ensuring a comprehensive response, from incident detection to business continuity planning. By examining the interplay between technical and managerial approaches, the paper seeks to provide a holistic solution to the growing challenge of DDoS attacks in increasingly complex network environments.
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