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As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research introduces a novel browser extension designed to effectively thwart keylogger spyware attacks. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any malicious activities. Upon detection, the extension empowers users with the immediate choice to terminate the suspicious process or validate its authenticity, thereby placing crucial real-time control in the hands of the end user. The methodology used guarantees the extension's mobility and adaptability across various platforms and devices. This paper extensively details the development of the browser extension, from its first conceptual design to its rigorous performance evaluation. The results show that the extension considerably strengthens end-user protection against cyber risks, resulting in a safer web browsing experience. The research substantiates the extension's efficacy and significant potential in reinforcing online security standards, demonstrating its ability to make web surfing safer through extensive analysis and testing. 

Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing literature, monotonicity is usually enforced by restricting function classes or model parameters and the inverse transformation is often approximated by root-finding algorithms as a closed-form inverse is unavailable. In this paper, we introduce a new integral-based approach termed: Atomic Unrestricted Time Machine (AUTM), equipped with unrestricted integrands and easy-to-compute explicit inverse. AUTM offers a versatile and efficient way to the design of normalizing flows with explicit inverse and unrestricted function classes or parameters. Theoretically, we present a constructive proof that AUTM is universal: all monotonic normalizing flows can be viewed as limits of AUTM flows. We provide a concrete example to show how to approximate any given monotonic normalizing flow using AUTM flows with guaranteed convergence. Our result implies that AUTM can be used to transform an existing flow into a new one equipped with explicit inverse and unrestricted parameters. The performance of the new approach is evaluated on high dimensional density estimation, variational inference and image generation.