# IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN

arXiv: https://arxiv.org/abs/2202.04121 · HTML: https://mlsanity.com/paper/2202.04121/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN](https://mlsanity.com/paper/2012.05073/) (2020)
- [Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder](https://mlsanity.com/paper/2108.11720/) (2021)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2009.08864/) (2020)
- [A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI](https://mlsanity.com/paper/2201.05373/) (2022)
- [IoT Malware Network Traffic Classification using Visual Representation and Deep Learning](https://mlsanity.com/paper/2010.01712/) (2020)
- [Malware Classification Using Deep Boosted Learning](https://mlsanity.com/paper/2107.04008/) (2021)
- [Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique](https://mlsanity.com/paper/1910.10958/) (2019)
- [A Comprehensive Study on Learning-Based PE Malware Family Classification Methods](https://mlsanity.com/paper/2110.15552/) (2021)

## Built on by (most cited first, 10 of 10)

- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN](https://mlsanity.com/paper/2211.16571/) (2022)
- [A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection](https://mlsanity.com/paper/2212.08008/) (2022)
- [COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images](https://mlsanity.com/paper/2302.02619/) (2023)
- [CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis](https://mlsanity.com/paper/2508.17128/) (2025)
- [Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework](https://mlsanity.com/paper/2212.02477/) (2022)
- [Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges](https://mlsanity.com/paper/2410.13894/) (2024)
- [A Tumor Aware DenseNet Swin Hybrid Learning with Boosted and Hierarchical Feature Spaces for Large-Scale Brain MRI Classification](https://mlsanity.com/paper/2601.18330/) (2026)
- [Particle Multi-Axis Transformer for Jet Tagging](https://mlsanity.com/paper/2406.06638/) (2024)
- [Split Learning-Enabled Framework for Secure and Light-weight Internet of Medical Things Systems](https://mlsanity.com/paper/2511.00336/) (2025)

## Abstract (arXiv)

Interaction between devices, people, and the Internet has given birth to a new digital communication model, the Internet of Things (IoT). The seamless network of these smart devices is the core of this IoT model. However, on the other hand, integrating smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch an attack to compromise the devices using malware proliferation techniques. Therefore, malware detection is considered a lifeline for the survival of IoT devices against cyberattacks. This study proposes a novel IoT Malware Detection Architecture (iMDA) using squeezing and boosting dilated convolutional neural network (CNN). The proposed architecture exploits the concepts of edge and smoothing, multi-path dilated convolutional operations, channel squeezing, and boosting in CNN. Edge and smoothing operations are employed with split-transform-merge (STM) blocks to extract local structure and minor contrast variation in the malware images. STM blocks performed multi-path dilated convolutional operations, which helped recognize the global structure of malware patterns. Additionally, channel squeezing and merging helped to get the prominent reduced and diverse feature maps, respectively. Channel squeezing and boosting are applied with the help of STM block at the initial, middle and final levels to capture the texture variation along with the depth for the sake of malware pattern hunting. The proposed architecture has shown substantial performance compared with the customized CNN models. The proposed iMDA has achieved Accuracy: 97.93%, F1-Score: 0.9394, Precision: 0.9864, MCC: 0. 8796, Recall: 0.8873, AUC-PR: 0.9689 and AUC-ROC: 0.9938.