Published Paper Details:

With the rapid growth of technology, many people are currently working remotely and consume social media more often. Over the last few years, the Web has seen a massive growth in the number and kinds of web services. Web facilities such as online banking, gaming, and social networking have promptly evolved as people perform routine tasks. As a result, a large amount of information is uploaded on a daily basis to the Web. As these web services drive increased opportunities for people to interact, they equally offer new opportunities for criminals. URLs are launch pads for any web attacks such that any malicious intention user can steal the identity of the authorized person by sending the malicious URL. Malicious URLs are a keystone of Internet illegitimate activities. It is a form of cyber-attack, which has an adverse effect on people where the user is directed to fake websites and duped to reveal their sensitive and personal information which includes passwords of accounts, bank details, atm pin-card details etc. Hence protecting sensitive information from malwares or web phishing is difficult Aim of the phishers is to acquire critical information like username, password and bank account details. Cyber security persons are now looking for trustworthy and steady detection techniques for phishing websites detection. Hence protecting sensitive information from malwares or web phishing is difficult. By using Machine learning algorithms, we will be identifying phishing attacks and report their positives and negatives. The proposed approach is that which classifies URLs automatically by using Machine learning algorithm called logistic regression that uses binary classification.