Quantum Guard: Pioneering Quantum-Based Malware Defense for IoT Devices

In the burgeoning landscape of the Internet of Things (IoT), ensuring the integrity and security of embedded devices is paramount. With the increasing frequency of cyberattacks targeting these typically low-powered devices, there is an urgent need for improved defensive infrastructure. Traditional malware detection methods such as signature-based, heuristic-based, and specification-based techniques are hampered by limitations in scanning speed, microprocessor capabilities, and energy efficiency. These classical approaches encompassing static, dynamic, and hybrid analyses struggle to keep pace with the demands of modern resource-constrained IoT devices. Addressing these challenges, this article introduces ‘Quantum Guard,’ a trans-formative quantum-based malware detection framework that utilizes a hybrid approach. Here, the IoT device handles initial data probing by inspecting potential threats, while the quantum processor runs Grover's algorithm with a quantum oracle for intensive search tasks to accurately identify and mark specific malware classes. This innovative approach significantly enhances the speed and efficiency of malware detection across diverse types by employing quantum superposition to enable simultaneous scanning of multiple memory locations. The integration of a quantum oracle facilitates precise targeting of malware signatures, while the amplitude amplification algorithm improves the probability of detecting and accurately classifying signatures within unstructured datasets. Performance analysis shows that ‘Quantum Guard’ significantly outperforms traditional methods, offering a scalable and efficient solution with a space complexity of O(log <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N)$</tex> and a time complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O$</tex>(VN). This prototype not only allows for effective detection and protection against various malware types, including elusive polymorphic and metamorphic variants, but also integrates quantum cryptographic techniques to introduce a unique layer of security inherently resistant to emerging computational threats.

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Published
2024-11-06
DOI
https://doi.org/10.1109/dsc63325.2024.00032
Primary Topic
Advanced Malware Detection Techniques
Type
article
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article

Quantum Guard: Pioneering Quantum-Based Malware Defense for IoT Devices

Muhammad Naveed Aman, Biplab Sikdar, Mansoor Ali Khan
Advanced Malware Detection Techniques
article

Quantum Guard: Pioneering Quantum-Based Malware Defense for IoT Devices

Muhammad Naveed Aman, Biplab Sikdar, Mansoor Ali Khan
article en

Abstract

In the burgeoning landscape of the Internet of Things (IoT), ensuring the integrity and security of embedded devices is paramount. With the increasing frequency of cyberattacks targeting these typically low-powered devices, there is an urgent need for improved defensive infrastructure. Traditional malware detection methods such as signature-based, heuristic-based, and specification-based techniques are hampered by limitations in scanning speed, microprocessor capabilities, and energy efficiency. These classical approaches encompassing static, dynamic, and hybrid analyses struggle to keep pace with the demands of modern resource-constrained IoT devices. Addressing these challenges, this article introduces ‘Quantum Guard,’ a trans-formative quantum-based malware detection framework that utilizes a hybrid approach. Here, the IoT device handles initial data probing by inspecting potential threats, while the quantum processor runs Grover's algorithm with a quantum oracle for intensive search tasks to accurately identify and mark specific malware classes. This innovative approach significantly enhances the speed and efficiency of malware detection across diverse types by employing quantum superposition to enable simultaneous scanning of multiple memory locations. The integration of a quantum oracle facilitates precise targeting of malware signatures, while the amplitude amplification algorithm improves the probability of detecting and accurately classifying signatures within unstructured datasets. Performance analysis shows that ‘Quantum Guard’ significantly outperforms traditional methods, offering a scalable and efficient solution with a space complexity of O(log <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N)$</tex> and a time complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O$</tex>(VN). This prototype not only allows for effective detection and protection against various malware types, including elusive polymorphic and metamorphic variants, but also integrates quantum cryptographic techniques to introduce a unique layer of security inherently resistant to emerging computational threats.

University of Nebraska–Lincoln (US), National University of Singapore (SG)
Openalex Percentile: Top 76%
Advanced Malware Detection Techniques
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