Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices

Brij B. Gupta; Akshat Gaurav; Arcangelo Castiglione; Varsha Arya; Kwok Tai Chui

doi:10.1109/ICCE-Taiwan66881.2025.11208152

Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices

Brij B. Gupta
, Akshat Gaurav
, Arcangelo Castiglione
, Varsha Arya
, Kwok Tai Chui

Electronic Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Attacks on consumer electronic devices by malware create major security concerns that call for effective detection systems. In this context, this work presents a hybrid attention-driven artificial intelligence model using improved squeeze-excitation (ISE) for feature enhancement and improved CBMA (I-CBMA) for feature selection. Using a Transformer Encoder for deep feature learning and lightweight linear layers to preserve computational performance. The proposed model has an accuracy of 99.89%, which is more than standard deep learning models.

Original language	English
Title of host publication	ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan
Subtitle of host publication	Generative AI in Innovative Consumer Technology, Proceedings
Pages	125-126
Number of pages	2
ISBN (Electronic)	9798331587413
DOIs	https://doi.org/10.1109/ICCE-Taiwan66881.2025.11208152
Publication status	Published - 2025
Event	12th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2025 - Kaohsiung, Taiwan, Province of China Duration: 16 Jul 2025 → 18 Jul 2025

Publication series

Name	ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings

Conference

Conference	12th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2025
Country/Territory	Taiwan, Province of China
City	Kaohsiung
Period	16/07/25 → 18/07/25

Keywords

Consumer Electronic Security
Feature Selection
Hybrid Attention Mechanism
Malware Detection
Transformer Encoder

Access to Document

10.1109/ICCE-Taiwan66881.2025.11208152

Cite this

Gupta, B. B., Gaurav, A., Castiglione, A., Arya, V., & Chui, K. T. (2025). Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices. In ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings (pp. 125-126). (ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings). https://doi.org/10.1109/ICCE-Taiwan66881.2025.11208152

Gupta, Brij B. ; Gaurav, Akshat ; Castiglione, Arcangelo et al. / Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices. ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings. 2025. pp. 125-126 (ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings).

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title = "Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices",

abstract = "Attacks on consumer electronic devices by malware create major security concerns that call for effective detection systems. In this context, this work presents a hybrid attention-driven artificial intelligence model using improved squeeze-excitation (ISE) for feature enhancement and improved CBMA (I-CBMA) for feature selection. Using a Transformer Encoder for deep feature learning and lightweight linear layers to preserve computational performance. The proposed model has an accuracy of 99.89\%, which is more than standard deep learning models.",

keywords = "Consumer Electronic Security, Feature Selection, Hybrid Attention Mechanism, Malware Detection, Transformer Encoder",

author = "Gupta, \{Brij B.\} and Akshat Gaurav and Arcangelo Castiglione and Varsha Arya and Chui, \{Kwok Tai\}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 12th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2025 ; Conference date: 16-07-2025 Through 18-07-2025",

year = "2025",

doi = "10.1109/ICCE-Taiwan66881.2025.11208152",

language = "English",

series = "ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings",

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}

Gupta, BB, Gaurav, A, Castiglione, A, Arya, V & Chui, KT 2025, Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices. in ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings. ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings, pp. 125-126, 12th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2025, Kaohsiung, Taiwan, Province of China, 16/07/25. https://doi.org/10.1109/ICCE-Taiwan66881.2025.11208152

Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices. / Gupta, Brij B.; Gaurav, Akshat; Castiglione, Arcangelo et al.
ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings. 2025. p. 125-126 (ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Castiglione, Arcangelo

AU - Arya, Varsha

AU - Chui, Kwok Tai

PY - 2025

Y1 - 2025

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AB - Attacks on consumer electronic devices by malware create major security concerns that call for effective detection systems. In this context, this work presents a hybrid attention-driven artificial intelligence model using improved squeeze-excitation (ISE) for feature enhancement and improved CBMA (I-CBMA) for feature selection. Using a Transformer Encoder for deep feature learning and lightweight linear layers to preserve computational performance. The proposed model has an accuracy of 99.89%, which is more than standard deep learning models.

KW - Consumer Electronic Security

KW - Feature Selection

KW - Hybrid Attention Mechanism

KW - Malware Detection

KW - Transformer Encoder

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U2 - 10.1109/ICCE-Taiwan66881.2025.11208152

DO - 10.1109/ICCE-Taiwan66881.2025.11208152

M3 - Conference contribution

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Gupta BB, Gaurav A, Castiglione A, Arya V, Chui KT. Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices. In ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings. 2025. p. 125-126. (ICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings). doi: 10.1109/ICCE-Taiwan66881.2025.11208152

Hybrid Attention-Driven AI Model for Malware Attack Detection in Consumer Electronic Devices

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