{"title":"An Abnormal External Link Detection Algorithm Based on Multi-Modal Fusion","authors":"Zhiqiang Wu","doi":"10.4018/ijisp.337894","DOIUrl":null,"url":null,"abstract":"Website link detection is an important means to ensure the security of the external chain. In the past, it was mainly realized through blacklisting and feature engineering-based machine learning, which has the problems of slow detection speed and weak model generalization ability. The development of neural networks has brought a new solution to the security detection of the external chain of the website. To address the performance bottleneck caused by the variable content length of web pages, this article introduces an innovative approach: a website external link security detection algorithm based on multi-modal fusion. It extracts text, dynamic script, and image features separately, and constructs a deep fusion model that combines these multi-modal features. Compared with the previous research results, the proposed method is superior to the traditional single-mode method, and can quickly and accurately identify malicious web pages. The accuracy and F1 value are improved by 2.7% and 0.026.","PeriodicalId":44332,"journal":{"name":"International Journal of Information Security and Privacy","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Security and Privacy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijisp.337894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Website link detection is an important means to ensure the security of the external chain. In the past, it was mainly realized through blacklisting and feature engineering-based machine learning, which has the problems of slow detection speed and weak model generalization ability. The development of neural networks has brought a new solution to the security detection of the external chain of the website. To address the performance bottleneck caused by the variable content length of web pages, this article introduces an innovative approach: a website external link security detection algorithm based on multi-modal fusion. It extracts text, dynamic script, and image features separately, and constructs a deep fusion model that combines these multi-modal features. Compared with the previous research results, the proposed method is superior to the traditional single-mode method, and can quickly and accurately identify malicious web pages. The accuracy and F1 value are improved by 2.7% and 0.026.
期刊介绍:
As information technology and the Internet become more and more ubiquitous and pervasive in our daily lives, there is an essential need for a more thorough understanding of information security and privacy issues and concerns. The International Journal of Information Security and Privacy (IJISP) creates and fosters a forum where research in the theory and practice of information security and privacy is advanced. IJISP publishes high quality papers dealing with a wide range of issues, ranging from technical, legal, regulatory, organizational, managerial, cultural, ethical and human aspects of information security and privacy, through a balanced mix of theoretical and empirical research articles, case studies, book reviews, tutorials, and editorials. This journal encourages submission of manuscripts that present research frameworks, methods, methodologies, theory development and validation, case studies, simulation results and analysis, technological architectures, infrastructure issues in design, and implementation and maintenance of secure and privacy preserving initiatives.