  Security issues are threatened in various types of networks, especially in
the Internet of Things (IoT) environment that requires early detection. IoT is
the network of real-time devices like home automation systems and can be
controlled by open-source android devices, which can be an open ground for
attackers. Attackers can access the network credentials, initiate a different
kind of security breach, and compromises network control. Therefore, timely
detecting the increasing number of sophisticated malware attacks is the
challenge to ensure the credibility of network protection. In this regard, we
have developed a new malware detection framework, Deep Squeezed-Boosted and
Ensemble Learning (DSBEL), comprised of novel Squeezed-Boosted Boundary-Region
Split-Transform-Merge (SB-BR-STM) CNN and ensemble learning. The proposed STM
block employs multi-path dilated convolutional, Boundary, and regional
operations to capture the homogenous and heterogeneous global malicious
patterns. Moreover, diverse feature maps are achieved using transfer learning
and multi-path-based squeezing and boosting at initial and final levels to
learn minute pattern variations. Finally, the boosted discriminative features
are extracted from the developed deep SB-BR-STM CNN and provided to the
ensemble classifiers (SVM, MLP, and AdabooSTM1) to improve the hybrid learning
generalization. The performance analysis of the proposed DSBEL framework and
SB-BR-STM CNN against the existing techniques have been evaluated by the
IOT_Malware dataset on standard performance measures. Evaluation results show
progressive performance as 98.50% accuracy, 97.12% F1-Score, 91.91% MCC, 95.97
% Recall, and 98.42 % Precision. The proposed malware analysis framework is
robust and helpful for the timely detection of malicious activity and suggests
future strategies
