Cross-site scripting (XSS) attack is a kind of code injection that allows an attacker to inject malicious scripts code into a trusted web application. When a user tries to request the injected web page, he is not aware that the malicious script code might be affecting his computer. Nowadays, attackers are targeting the web applications that holding a sensitive data (e.g., bank transaction, e-mails, healthcare, and e-banking) to steal users' information and gain full access to the data which make the web applications to be more vulnerable. In this research, we applied three approaches to find a solution to this most challenging attacks issues. In the first approach, we implemented Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM) algorithms to discover and classify XSS attack. In the second approach, we implemented the Content Security Policy (CSP) approach to detect XSS attacks in real-time. In the last approach, we propose a new approach that combines the Web Application Firewall (WAF), Intrusion Detection System (IDS), and Intrusion Prevention System (IPS) to detect and prevent XSS attack in real-time. Our experiment results demonstrated the high performance of AI algorithms. The CSP approach shows the results for the detection system report in real-time. In the third approach, we got more expected system results that make our third model system a more powerful tool to address this research problem than the other two approaches.

Currently, most existing research on the security of augmented reality (AR) primarily focuses on user-to-user or user-to-infrastructure authentication, aiming to establish secure communication and prevent potential attacks, such as man-in-the-middle attacks, among others. AR techniques enable users to interact with virtual information and objects within the real environment. However, creating virtual objects can be time-consuming, and unfortunately, malicious individuals may unlawfully copy these objects and integrate them into their own AR scenarios. It is essential to authenticate whether AR objects and the associated content they interact with are legitimately integrated. This paper proposes a novel method for authenticating AR-delivered content using data-hiding techniques with the symmetric property. The proposed method utilizes data hiding to embed data within the content that AR objects interact with, enabling the identification of illegal and tampered AR objects. A scenario involving an AR e-book is defined, and two data-hiding methods are applied to this scenario. The experimental results demonstrate that both data-hiding methods effectively identify the tampered image page of an AR e-book under different tone versions, new trigger image(s) added, or new image(s) replacement.

In this paper, a novel reversible data hiding scheme for encrypted images based on interpolation technique and substitution is proposed. With our proposed scheme, the distribution of neighboring pixels in the encrypted image is relatively uniform, indicating our encryption system is able to offer a lower correlation among encrypted neighboring pixels within the coordinate system. Moreover, the stego images also do not release any information on either the cover image or hidden secret data. With the encryption and scrambling operation, the PSNR of the encrypted stego image is around 10.2594 dB, while the bpp offered by our proposed scheme is 0.06 higher than that of Zhong et al's scheme.

With the increasing integration of augmented reality (AR) in various applications, ensuring secure access and content authenticity has become a critical challenge. This paper proposes an innovative and robust authentication framework for protecting AR multimedia content through a hash-based data-hiding technique. Leveraging the Discrete Wavelet Transform (DWT) in the YCbCr color space, the method embeds multiple cryptographic hash signatures directly into the AR visual data. This design not only utilizes the symmetric property between two consecutive AR contents but also allows users to verify the connectivity between two AR digital contents by checking the embedded hash values. These embedded signatures support hierarchical, multi-level authentication, verifying not only the integrity and authenticity of individual AR objects but also their contextual relationships within the AR environment. The proposed system exhibits exceptional resilience to tampering, effectively identifying whether two consecutive e-pages in the AR content have been altered, while preserving high perceptual quality with PSNR values above 45 dB and SSIM scores consistently exceeding 0.98. This work presents a practical, real-time solution for enhancing AR content security, contributing significantly to the advancement of secure multimedia systems in next-generation interactive platforms.