Abstract

Bycreating an AI-driven method using deep learning and statistical analysis tools, this study seeks to fill important security holes in conventional intrusion detection systems. Current signature-based systems miss new and complex cyberattacks, which have significant financial and operational consequences for companies. The suggested approach detects unusual network activity in real-time by combining statistical analysis with long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and statistical analysis. This study will create and test hybrid models that can identify both known and zero-day threats while reducing false positives using publicly accessible datasets like UNSW-NB15, CIC-IDS2017, and NSL-KDD. Expected results are a system for real-time threat detection that offers up to 75% quicker identification relative to conventional approaches and lowers false positive rates by about 80%. By tackling the rising difficulty of sophisticated cyber attacks in an ever more complicated digital environment, this study adds to the cybersecurity domain.

Library of Congress Subject Headings

Computer crimes--Prevention--Automation; Computer networks--Security measures; Deep learning (Machine learning); Artificial intelligence

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

Plan Codes

PROFST-MS

Download

Share

COinS