ADAPTIVE MACHINE LEARNING FOR LOW-OVERHEAD ANOMALY DETECTION IN RESIDENTIAL NETWORKS: A COMPREHENSIVE SECURITY FRAMEWORK

This article presents a novel framework for enhancing residential network security through machine learning-enhanced anomaly detection, addressing the growingManojava Bharadwaj Bhagavathula

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Publication Details

Journal
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
Published
2025-02-05
DOI
https://doi.org/10.34218/ijcet_16_01_136
Primary Topic
Network Security and Intrusion Detection
Type
article
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article

ADAPTIVE MACHINE LEARNING FOR LOW-OVERHEAD ANOMALY DETECTION IN RESIDENTIAL NETWORKS: A COMPREHENSIVE SECURITY FRAMEWORK

Manojava Bharadwaj Bhagavathula
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
Network Security and Intrusion Detection
article

ADAPTIVE MACHINE LEARNING FOR LOW-OVERHEAD ANOMALY DETECTION IN RESIDENTIAL NETWORKS: A COMPREHENSIVE SECURITY FRAMEWORK

Manojava Bharadwaj Bhagavathula
article en

Abstract

This article presents a novel framework for enhancing residential network security through machine learning-enhanced anomaly detection, addressing the growingManojava Bharadwaj Bhagavathula

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGYVol. 16(1)
University of Pittsburgh (US)
Openalex Percentile: Top 98%
Network Security and Intrusion Detection
DOIOpen Access PDFOpenAlex
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ADAPTIVE MACHINE LEARNING FOR LOW-OVERHEAD ANOMALY DETECTION IN RESIDENTIAL NETWORKS: A COMPREHENSIVE SECURITY FRAMEWORK — Manojava Bharadwaj Bhagavathula · INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (2025) | TGRS Research Map | TGRS