Jebran Khan¹, Kashif Ahmad², Kyung-Ah Sohn^1,3,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159

Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme is to generate adversarial examples… More >

Mengmeng Ge¹, Xiangzhan Yu^1,*, Lin Ye^1,2, Jiantao Shi¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3393-3405, 2022, DOI:10.32604/cmc.2022.017220

Abstract With the rapid development of the Internet, the types of webpages are more abundant than in previous decades. However, it becomes severe that people are facing more and more significant network security risks and enormous losses caused by phishing webpages, which imitate the interface of real webpages and deceive the victims. To better identify and distinguish phishing webpages, a visual feature extraction method and a visual similarity algorithm are proposed. First, the visual feature extraction method improves the Vision-based Page Segmentation (VIPS) algorithm to extract the visual block and calculate its signature by perceptual hash technology. Second, the visual similarity… More >