  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.
