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Malware file production grows rather actively, which is explained by the development of digital structures. The proliferation of cyber trends poses severe security challenges due to the increasing complexity of attacks. These files could be difficult to detect when they share characteristics with normal files or if they are altered. Internet of Things (IoT) networks put a probability of vulnerability akin to Mirai malware to cyberattacks. There is a need to develop complex procedures for top security since it is important for such networks. This paper presents a new framework of preprocessing techniques, feature selection, and classification for predicting Mirai malware IoT security attacks. The preprocessing part uses the Global–Local Depth Normalization (GLDN) of features for dissolving noise and for better normalization of feature depths to enhance the learning factor. Practical feature selection is performed by using a combination of Gooseneck Barnacle Optimization (GBO) and Human Memory Optimization (HMO). This hybrid makes an intelligent dimensionality reduction decision determined by choosing appropriate features from among the set by the right balance between exploration and exploitation using biologically inspired optimization algorithms. For classification, there is proposed a Stereoscopic Scalable Quantum Convolutional Neural Network (sQCNN) that applies quantum computation principles to enhance computational scalability at the quantum level. The Banyan Tree Growth Optimization (BTGO) algorithm can optimize the classifier with high accuracy and attack detection immunity. The concept of Banyan tree growth in a hierarchical structure is similar to the classifier structure. Experiments conducted on the N-BaIoT dataset successfully prove the idea behind the proposed approach. The results propose that the new methods ensure better results over the traditional methods concerning the achieved accuracy of 99.67% and precision of 99.61%, while also incorporating reduced computational over- head. This new framework is a major step forward in defending IoT networks against current emerging threats, stressing the collaboration of preprocessing, feature selection, and quantum learning.

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Stereoscopic Scalable Quantum Convolutional Neural Networks with Banyan Tree Growth Optimization for Predicting IoT Security Attacks by Mirai Malware

  • 会议论文

  • 发表日期：

    2026年

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  • Ravi Kumar Suggala,
  • Khushi Kumari,
  • Mathi Gayathri,
  • Koppisetti Deepika Naga Sree,
  • Nekkalapudi Gayathri,
  • Suma Kadali

摘要

Malware file production grows rather actively, which is explained by the development of digital structures. The proliferation of cyber trends poses severe security challenges due to the increasing complexity of attacks. These files could be difficult to detect when they share characteristics with normal files or if they are altered. Internet of Things (IoT) networks put a probability of vulnerability akin to Mirai malware to cyberattacks. There is a need to develop complex procedures for top security since it is important for such networks. This paper presents a new framework of preprocessing techniques, feature selection, and classification for predicting Mirai malware IoT security attacks. The preprocessing part uses the Global–Local Depth Normalization (GLDN) of features for dissolving noise and for better normalization of feature depths to enhance the learning factor. Practical feature selection is performed by using a combination of Gooseneck Barnacle Optimization (GBO) and Human Memory Optimization (HMO). This hybrid makes an intelligent dimensionality reduction decision determined by choosing appropriate features from among the set by the right balance between exploration and exploitation using biologically inspired optimization algorithms. For classification, there is proposed a Stereoscopic Scalable Quantum Convolutional Neural Network (sQCNN) that applies quantum computation principles to enhance computational scalability at the quantum level. The Banyan Tree Growth Optimization (BTGO) algorithm can optimize the classifier with high accuracy and attack detection immunity. The concept of Banyan tree growth in a hierarchical structure is similar to the classifier structure. Experiments conducted on the N-BaIoT dataset successfully prove the idea behind the proposed approach. The results propose that the new methods ensure better results over the traditional methods concerning the achieved accuracy of 99.67% and precision of 99.61%, while also incorporating reduced computational over- head. This new framework is a major step forward in defending IoT networks against current emerging threats, stressing the collaboration of preprocessing, feature selection, and quantum learning.