Unauthorized Access has been difficult to stop or prevent in the last few decades using username and password authentication only. For an individual, data breach might just be a simple case of espionage or the loss of private credentials, for an enterprise, this could mean the loss of billions of dollars. Preventing Unauthorized Access to Enterprise Systems Using a Location-based Logical Access Control proposes a framework that uses time and location in preventing and defending against data breaches. The framework was developed using Java with an Eclipse IDE. The database was designed using MySQL and locations were collected using Google Maps API. Users registered at different locations in a university campus were unable to access another’s account in the database because they were both outside the known location and tried to do this at off-work hours. Users were registered with username and password at specified locations. The users are then made to login from same and different locations with correct username and passwords. it was discovered that access to the database was only given when the username and password was correct and location was same as at registered or as allowed by an administrator. The system was found to protect against unauthorized access arising from stolen login credentials and unauthorized remote logins from malicious users.

One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.