Machine Learning for Web Security: Strategies to Detect and Prevent Malicious Activities

Abstract

The proposed concept is entirely focused on the criminal underworld that exists on the internet. The project's primary goal is to identify the viruses that hackers insert onto computers in order to obtain full access. Because websites are open source, virus can be injected by hackers. It was discovered that 70 of the top 100 most popular web sites either hosted malware themselves or linked to other websites that did. Previously, a number of software programs, such as Barracuda and K9 protection, were created to prevent this by using blacklisting to ban the website. However, it is ineffective since the user must manually add the URL to the blacklist each time they come across a new malicious website. Phishing websites resemble useful websites that ask the user to download the things your computer requires. As a result, we fall into a trap and give hackers a simple way to access our systems. Despite the existence of anti-virus software, hackers purposefully evade detection. Therefore, it's essential to identify these kinds of rogue websites that release malware in order to protect our sensitive data. Machine learning computations can be used to perform this on a regular basis. Thus, this emerged to be the central notion driving this project. We shall learn how to recognize phishing websites in this case. These websites are made using actual websites as a model. Preventing fraud and phishing websites is our aim. When creating the model, machine learning methods are employed to achieve high levels of efficiency and accuracy. The primary benefit is that the program is entirely automatic and requires no manual labor when utilizing machine learning's online learning. Python and its libraries make the solution simple to solve.