Optimization of Neural Network Architecture using Lions Optimization Algorithm

Malik, Kaptan; Srivastava, Kanishk; Sharma, Krishna

doi:10.1109/iciccs48265.2020.9121072

2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) 2020

DOI: 10.1109/iciccs48265.2020.9121072

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Optimization of Neural Network Architecture using Lions Optimization Algorithm

Kaptan Malik

Kanishk Srivastava

Krishna Sharma

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2024

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Cited by 2 publications

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Efficient Detection of QR Code Image-Based Attacks in Industries Through Lightweight Deep Learning Models and Monarch Butterfly Optimization Algorithm

Sathyaraj,

Siva Kumar,

Sabitha

et al. 2024

Advances in Logistics, Operations, and Management Science

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In the industrial landscape, cyber threats challenge information security, especially in industrial cybersecurity. Attack vectors like malware, phishing, and ransomware complicate data protection in manufacturing. Data breaches risk critical information and financial implications. QR codes, convenient for tasks like inventory management, introduce vulnerabilities. Traditional detection struggles with data volume and evolving hacking techniques. Recent advancements explore lightweight deep learning for industrial cybersecurity, focusing on QR codes. This research introduces a hybrid approach, using multi-objective optimization to enhance QR code-based cyber-attack detection. QR code images are trained with advanced CNN models like MobileNetV2 and ShuffleNet. The monarch butterfly optimization algorithm strategically selects impactful features. In practical use, the hybrid model achieved 99.82% accuracy, surpassing traditional CNN models. It proves effective for industrial cybersecurity, addressing vulnerabilities in QR code usage.

show abstract

Efficient Detection of QR Code Image-Based Attacks in Industries Through Lightweight Deep Learning Models and Monarch Butterfly Optimization Algorithm

Sathyaraj,

Siva Kumar,

Sabitha

et al. 2024

Advances in Logistics, Operations, and Management Science

View full text Add to dashboard Cite

show abstract

Enhancing Cybersecurity by Deep Learning Models for QR Code Image-Based Attack Detection Using Lion Optimization Algorithm

Jambulingam,

Ganesan,

Chandrasekar

et al. 2024

Advances in Computer and Electrical Engineering

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In today's digital landscape, the protection of sensitive data against rising cyber threats is paramount. Traditional methods for detecting cyber-attacks often fall short due to their limitations in accuracy and efficiency. This study proposes a pioneering solution: a hybrid approach integrating lightweight deep learning models like MobileNetV2 and ShuffleNet V2 with the Lion Optimization algorithm for feature selection. By leveraging these advanced technologies, the proposed method addresses the shortcomings of conventional approaches. It significantly enhances both the accuracy and efficiency of cyber-attack detection processes. Notably, the hybrid model achieves an impressive 99.82% accuracy in classifying and detecting various types of cyber-attacks, surpassing the performance of traditional CNN models.

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Optimization of Neural Network Architecture using Lions Optimization Algorithm

Cited by 2 publications

References 10 publications

Efficient Detection of QR Code Image-Based Attacks in Industries Through Lightweight Deep Learning Models and Monarch Butterfly Optimization Algorithm

Efficient Detection of QR Code Image-Based Attacks in Industries Through Lightweight Deep Learning Models and Monarch Butterfly Optimization Algorithm

Enhancing Cybersecurity by Deep Learning Models for QR Code Image-Based Attack Detection Using Lion Optimization Algorithm

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