Authors: Adinath Admane, Abhishek Andhale, Aaditya Assalkar, Om Bastapure, Aparna V. Mote
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The rapid growth of the internet and the increasing dependence on online services have led to a significant rise in cyber threats, with phishing attacks being a prevalent and pervasive threat. Phishing attacks often involve the use of deceptive techniques to trick users into divulging sensitive information, such as login credentials, credit card details, and personal information. Detecting and preventing phishing attacks in real-time has become a critical challenge for individuals, businesses, and organizations. This research paper presents an approach for online phishing detection using machine learning techniques. The primary objective is to develop a system that can automatically identify and classify phishing websites and emails, thereby enhancing cyber security and protecting users from falling victim to these malicious activities. Online phishing detection has gained significant importance, which will only grow with the amount of dependency on cyberspace, the proposed system provides an easy solution, during cases when the user is unsure about the authenticity of the website visited, they can try to copy the Uniform Resource Locator (URL) and paste the link into the online phishing detection system. Through the system process, it will help the user to identify whether given links were legitimate website or it is a phishing website. Therefore, the user will not be in a doubtful situation the whole day in wondering whether the information they gave in a certain website is safe or not. Providing complex decision with simplicity, the system will help the user to detect each variable of URL given accurate.
The rapid evolution of the internet has transformed the way we communicate, work, and conduct transactions. While this digital age has brought numerous benefits, it has also exposed individuals and organizations to various cyber security threats, with phishing being one of the most prevalent and damaging. Phishing attacks involve malicious actors attempting to deceive users into divulging sensitive information such as login credentials, financial data, or personal details by posing as trustworthy entities. These attacks can take the form of deceptive emails, fake websites, or social engineering tactics. As the sophistication of phishing attacks continues to grow, traditional methods of detection, such as signature-based approaches and rule-based filters, are becoming less effective. To combat this evolving threat landscape, there is a growing need for advanced, adaptive, and proactive solutions. Machine learning (ML) has emerged as a powerful tool in the fight against phishing, offering the potential to identify and thwart phishing attempts in real-time.
This paper explores the application of machine learning techniques for online phishing detection, emphasizing their role in enhancing security in today's digital ecosystem. We will delve into the key challenges posed by phishing attacks, outline the fundamentals of machine learning, and discuss how ML can be harnessed to build robust phishing detection systems. Additionally, we will examine various features and datasets commonly used for training and testing machine learning models in the context of phishing detection. Phishing detection is a critical cyber security problem that involves identifying and preventing fraudulent attempts to deceive individuals or organizations into revealing sensitive information, such as usernames, passwords, financial data, or personal information. Phishing attacks typically occur through various online channels, including email, social media, websites, and messaging platforms.
The primary goal of phishing detection is to differentiate between legitimate communications and phishing attempts. Implementing and maintaining phishing detection systems can be resource-intensive for organizations. This includes costs related to hardware, software, training, and ongoing updates to stay effective against new threats. Integrate real-time threat intelligence feeds that provide information on the latest phishing tactics and indicators of compromise. Use sandboxing techniques to analyse suspicious email attachments and URLs in a controlled environment. Provide ongoing and interactive cyber security training to users to keep them informed about the latest phishing trends and attack techniques. Conduct simulated phishing campaigns to assess user susceptibility and offer targeted training to those who fall for them
II. LITERATURE REVIEW
Machine learning has made significant strides in improving online phishing detection, providing a valuable layer of defense against cyber threats. However, it is important to recognize that no single solution is foolproof, and a holistic approach to cyber security that includes user education, regular model updates, and continuous monitoring is essential to combat the ever-changing landscape of online phishing attacks
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Copyright © 2023 Adinath Admane, Abhishek Andhale, Aaditya Assalkar, Om Bastapure, Aparna V. Mote. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.