| Title | Defences Against web Application Attacks and Detecting Phishing Links Using Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Hashim, Aya, Medani, Razan, Attia, Tahani Abdalla |
| Conference Name | 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) |
| Keywords | Apache access Logs, application security, attacks, Chained Attacks, Classification algorithms, cyber security, Deep Learning, logistic regression, long short term memory networks, machine learning, machine learning algorithms, phishing, PHP and MySQL, pubcrawl, Random Forest, Resiliency, Scalability, server, support vector machine, Support vector machines, Training, Training data, Web application |
| Abstract | In recent years web applications that are hacked every day estimated to be 30 000, and in most cases, web developers or website owners do not even have enough knowledge about what is happening on their sites. Web hackers can use many attacks to gain entry or compromise legitimate web applications, they can also deceive people by using phishing sites to collect their sensitive and private information. In response to this, the need is raised to take proper measures to understand the risks and be aware of the vulnerabilities that may affect the website and hence the normal business flow. In the scope of this study, mitigations against the most common web application attacks are set, and the web administrator is provided with ways to detect phishing links which is a social engineering attack, the study also demonstrates the generation of web application logs that simplifies the process of analyzing the actions of abnormal users to show when behavior is out of bounds, out of scope, or against the rules. The methods of mitigation are accomplished by secure coding techniques and the methods for phishing link detection are performed by various machine learning algorithms and deep learning techniques. The developed application has been tested and evaluated against various attack scenarios, the outcomes obtained from the test process showed that the website had successfully mitigated these dangerous web application attacks, and for the detection of phishing links part, a comparison is made between different algorithms to find the best one, and the outcome of the best model gave 98% accuracy. |
| DOI | 10.1109/ICCCEEE49695.2021.9429609 |
| Citation Key | hashim_defences_2021 |
Defences Against web Application Attacks and Detecting Phishing Links Using Machine Learning