Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Amith Shekhar Chandrashekhar
BNM Institute of Technology
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
An intelligence framework for threat detection and response in cloud–IoT-assisted enterprise environments Amith Shekhar Chandrashekhar; Sarala D V; Ambuja K; Rajani Kallhalli Channarayappa; Karanam Sunil Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11046

Abstract

Cloud-internet of things (IoT)-enabled enterprise environments have become an integral part of modern infrastructures, but their increasing interconnectedness makes them vulnerable to sophisticated and rapidly evolving cyber threats. Existing methods for intrusion detection and threat intelligence often suffer from limitations such as high false alarms, low adaptability to new attacks, and computational overhead. To address these challenges, this paper presents an intelligent hybrid framework for threat detection and response in cloud-IoT-enabled enterprises. The proposed system adopts a two-stage architecture: an autoencoder (AE)-based anomaly detector serves as the first security layer to identify deviations from normal traffic behavior, while a convolutional neural network-long short-term memory (CNN-LSTM) model with an attention mechanism serves as the second layer to classify known attack categories with high accuracy. A response mechanism is further integrated to log events, assign severity scores, apply automated protections, and generate real-time alerts, transforming detection into proactive prevention. The system has been evaluated on the benchmark CSE-CIC-IDS2018 dataset, where the anomaly detector achieved an accuracy of 98.4% with a false positive rate of 2%, while the CNN-LSTM-Attention intrusion classifier achieved an accuracy of 99.42%.
Co-Authors Ambuja K Karanam Sunil Kumar Rajani Kallhalli Channarayappa Sarala D V