Deep Learning for Cyber Security: Deephoax Detection
Sy. Yuliani Yakub, Rivo Juicer Wowor, David Agustriawan, Irmawati Irmawati, Dinar Ajeng Kristiyanti, P.M. Winarno, Rosilah Binti Hassan, Roshayu Mohamad, Randy Zahran Razzaq · 2025
The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats.