Browser extensions boost user experience on the web. Similarly to smartphone app stores, browsers like Chrome distribute browser extensions via their Web Store, enabling a thriving market of third- party developed extensions. The Web Store incorporates a user review system to help users decide which extensions to install. Unfortunately, the open nature of the review system is subject to reputation manipulation. As browser vendors fight reputation manipulation, attackers employ more sophisticated methods to stay under the radar. Focusing on fake reviews, we identify several techniques used by attackers: fake accounts, disjoint sets of fake accounts for different extensions, automation of generated reviews, and focusing on reviews rather than ratings. Thus, the goal of the project is to investigate different NLP Models for Malware analysis