Reviewing effectivity in security approaches towards strengthening internet architecture

The usage of existing Internet architecture is shrouded by various security loopholes and hence is highly ineffective towards resisting potential threats over internet. Hence, it is claimed that future internet architecture has been evolved as a solution to address this security gaps of existing internet architecture. Therefore, this paper initiates its discussion by reviewing the existing practices of web security in conventional internet architecture and has also discussed about some recent solutions towards mitigating potentially reported threats e.g. cross-site scripting, SQL inject, and distributed denial-of-service. The paper has also discussed some of the recent research contribution towards security solution considering future internet architecture. The proposed manuscripts contributes to showcase the true effectiveness of existing approaches with respect to advantages and limitation of existing approaches along with explicit highlights of existing research problems that requires immediate attention

SQL Injection Vulnerability Detection Using Deep Learning: A Feature-based Approach

SQL injection (SQLi), a well-known exploitation technique, is a serious risk factor for database-driven web applications that are used to manage the core business functions of organizations. SQLi enables an unauthorized user to get access to sensitive information of the database, and subsequently, to the application’s administrative privileges. Therefore, the detection of SQLi is crucial for businesses to prevent financial losses. There are different rules and learning-based solutions to help with detection, and pattern recognition through support vector machines (SVMs) and random forest (RF) have recently become popular in detecting SQLi. However, these classifiers ensure 97.33% accuracy with our dataset. In this paper, we propose a deep learning-based solution for detecting SQLi in web applications. The solution employs both correlation and chi-squared methods to rank the features from the dataset. Feed-forward network approach has been applied not only in feature selection but also in the detection process. Our solution provides 98.04% accuracy over 1,850+ recorded datasets, where it proves its superior efficiency among other existing machine learning solutions