Informatics Publishing Limited

Ujwal Singh Vohra ¹, Shri Prakash Dwivedi ¹, H. L. Mandoria ¹

Affiliations
1 Department of Information Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, IN

Abstract

Day by day the researchers are trying to make such characters recognition system that can be able to detect the writing and languages of individuals, for this multilingual handwritten characters recognition is such system that playing a vital role for recognizing the characters written in different languages and in different styles. The research work presented in this thesis aims to do the study and analysis to recognize the multilingual handwritten characters with a high level of accuracy and for this purpose the classifier that we are using is support vector machine. Here in this work we have used the languages like Hindi and English and along with this we have taken special characters and numerals and tried to recognize them with our system.

Keywords

OCR, Handwritten Characters,Recognition,SVM.

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Krishna Kumar ¹, Hardwari Lal Mandoria ², Rajeev Singh ³, Shri Prakash Dwivedi ², Paras ⁴

Affiliations
1 Department of Information Technology, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, IN
2 Department of Information Technology, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, IN
3 Department of Computer Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, IN
4 Department of Electronics and Communication Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, IN

Abstract

The Generative Adversarial Networks (GANs) are playing a crucial role in deep-learning-based malware classification to overcome the dataset imbalance and unseen malware. The Generative AI is preferably used in many applications, such as improving image resolution and generating audio, video, and text. The cybercriminals are also using the Generative AI for generating the malware and deepfake videos to harm the targeted person or device. By generating the synthetic data, it makes the deep learning model more robust to detect such types of unseen and adversarial attacks. This work utilizes GANs for generating adversarial malware samples to train a classification and detection model, improving the model’s ability to identify sophisticated malware variants. The performance of the proposed Conditional Generative Adversarial Network (CGAN) model is evaluated on a multiclass malware grayscale image dataset and a binary class malware RGB image dataset. The performance of proposed model is compared with current state-of-the-art. Results indicate a significant improvement in classification accuracy and a reduction in training time and false positives, showcasing GAN’s potential in the dynamic cybersecurity landscape.

Keywords

Malware detection, Generative Adversarial Networks, deep learning, classification, adversarial learning, cybersecurity

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