AI-Driven Cybersecurity for Defense Networks: A Mathematical Approach with DDoS Attack Analysis

Neelesh Mungoli

Title:
AI-Driven Cybersecurity for Defense Networks: A Mathematical Approach with DDoS Attack Analysis

Authors:
Neelesh Mungoli

Cite This Article :

Neelesh Mungoli "AI-Driven Cybersecurity for Defense Networks: A Mathematical Approach with DDoS Attack Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-2, April 2025, pp.206-223, URL: https://www.ijtsrd.com/papers/ijtsrd76306.pdf

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Abstract :
This paper presents a novel machine-learning pipeline tailored for proactive cyber defense within high-stakes military networks, addressing the pressing need to detect and neutralize sophisticated threats such as Distributed Denial-of-Service (DDoS) attacks in near real-time. Our approach begins with a mathematically rigorous anomaly detection framework, constructed on the premise that normal network traffic follows an identifiable statistical distribution, deviations from which can serve as early indicators of malicious behavior. By exploiting deep neural architectures—specifically autoencoders enhanced with domain-specific heuristics—our pipeline learns complex traffic patterns, encompassing both high-volume and subtle “low-and-slow” attack methodologies. A core component of our methodology involves deriving explicit theoretical bounds for detection accuracy and false alarm rates, ensuring that defense operators can calibrate the system according to mission-critical thresholds.

Keywords :
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Publication Details:

Unique Identification Number : IJTSRD76306

Published In : Volume-9 | Issue-2, April 2025

Page Number(s) : 206-223

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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