Biblio

To restrict unauthorized access to the data of the website. Most of the web-based systems nowadays require users to verify themselves before accessing the website is authentic information. In terms of security, it is very important to take different security measures for the protection of the authentic data of the website. However, most of the authentication systems which are used on the web today have several security flaws. This document is based on the security of the previous schemes. Compared to the previous approaches, this “spoofed proof stateless session model” method offers superior security assurance in a scenario in which an attacker has unauthorized access to the data of the website. The various protocol models are being developed and implemented on the web to analyze the performance. The aim was to secure the authentic database backups of the website and prevent them from SQL injection attacks by using the read-only properties for the database. This limits potential harm and provides users with reasonable security safeguards when an attacker has an unauthorized read-only access to the website's authentic database. This scheme provides robustness to the disclosure of authentic databases. Proven experimental results show the overheads due to the modified authentication method and the insecure model.

The trend of rapid development of mobile technologies has highlighted new challenges and threats regarding the information security by the using cookies in mobile learning (m-learning) systems. In order to overcome these challenges and threats, this paper has identified two main objectives. First, to give a review of most common types to cookies and second, to consider the challenges and threats regarding cookies with aspects that are directly related to issues of security and privacy. With these objectives is possible to bridge security gaps in m-learning systems. Moreover, the identified potential challenges and threats are discussed with the given proposals of pragmatic solutions for their mitigating or reducing. The findings of this research may help students to rise security awareness and security behavior in m-learning systems, and to better understand on-going security challenges and threats in m-learning systems.

In accordance to the annual report by the Cisco 2018, web applications are exposed to several security vulnerabilities that are exploited by hackers in various ways. It is becoming more and more frequent, specific and sophisticated. Of all the vulnerabilities, more than 40% of attempts are performed via cross-site scripting (XSS). A number of methods have been postulated to examine such vulnerabilities. Therefore, this paper attempted to address an overview of one such vulnerability: the cookies in the XSS. The objective is to present an overview of the cookies, it's type, vulnerability, policies, discovering, protecting and their mitigation via different tools/methods and via cryptography, artificial intelligence techniques etc. While some future issues, directions, challenges and future research challenges were also being discussed.

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.