Detecting Fake URLs and Preventing Malware Using Machine Learning

Increased rates of phishing attacks are one of the threats present in the increasingly connected society of the modern world especially for the youths. In this case, an ML techniques that involves patterns and characteristics to alert users of suspicious links and prevent them from being at risk, such as Naive Bayes, provide solutions. It also offers fairly good protection against the last kind of phishing exploits that employ misleading URLs as their implement, although the URLs are classified as being of either a malicious or a legitimate nature with the help of the Naive Bayes method. The primary purpose of the proposed work is to design a protective shield against phishing attacks to minimize people's exposure to such scams. The aim of the end applications is to build systems that are capable of capturing and blacklisting the dangerous links on their own to make the users safe when they are on the internet. The approach uses a novel machine learning algorithm which has been trained on several sets of real and fake URLs. It further analyzes the URL pattern, language used according to its database and prior defined pat terns in order ''to judge which connections are bad/worst and which are allowed/good.'' The above model is undoubtedly going to be elastic under all variations in the phishing strategies at the hands of a perfectly chosen training set. Essentially the program integrates with users' browsers, constantly analyzing URLs and notifying users promptly in the event that possible risks are detected. By enhancing users' confidence on their online interaction through the dependability of the application in identifying threats as evidenced in actual simulations based on different types of phishing threats. To ensure great safety with the network and safe data storage, the usage of the machine learning for malware identification and fake web link detection is required. This is in a way underlines how people might be prepared for how to go about their business online as cyber threats persist to evolve. The accuracy rate of the attained model is 94%.