Forgetting the stipulated schedule or time of lectures due to one reason or the other has always been a major concern for students resulting in the students missing classes and thereby lagging behind in what the lecturer has taught the other students. The main purpose of developing the Lecture time table reminder system is to minimize this problem to an acceptable level. Lecture Timetable Reminder System is a mobile application developed on the Android platform for the students of Computer Science, 500level, Federal University of Technology, Akure, with the service of reminding them about their lectures. The major tools used in developing this application are Java, Eclipse, Android Software Development Kit (SDK), PHP and MySQL. Java is the programming language that was used and Eclipse is the Integrated Development Environment (IDE) and Android SDK is a virtual device emulator. Then PHP was used to produce a response customized for each user's request to the application, MySQL is the database.
This study on a security system for detecting denial of service (DDoS) and masquerade attacks on social networks specifically describes how a Convolutional Neural Network (CNN) algorithm was employed. The dataset used for this research is the CICIDS2017 dataset, which contains benign data (no attack present) and the most up-to-date, frequent attacks which resemble true, real-world data. The feature extraction method used was recursive feature elimination (RFE), which reduced 77 columns of the dataset to 10 columns. This research was motivated by the limitation of Alguliyev and Abdullayeva 2019, which focused on the prediction of DDoS attack occurrence by getting related texts in social media. It has a limited attack class that focuses solely on DDoS attacks, and it does not perform social media network prediction in general. The objective of this research is to develop a security system for detecting DDoS and masquerade attacks and evaluate the detection model on social media networks. The system was tested on Facebook and Instagram. The result of the training accuracy that we derived from this research is 99.53%, while the testing accuracy is 99.52%. The result of this research is compared with previous studies’ results. This study recommends that the model implemented can be enhanced more effectively by comparing the accuracy of alternative deep learning algorithms to that of the CNN utilized in the current prediction model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.