Biblio

In this paper, an intruder detection system has been built with an implementation of convolutional neural network (CNN) using raspberry pi, Microsoft's Azure and Twilio cloud systems. The CNN algorithm which is stored in the cloud is implemented to basically classify input data as either intruder or user. By using the raspberry pi as the middleware and raspberry pi camera for image acquisition, efficient execution of the learning and classification operations are performed using higher resources that cloud computing offers. The cloud system is also programmed to alert designated users via multimedia messaging services (MMS) when intruders or users are detected. Furthermore, our work has demonstrated that, though convolutional neural network could impose high computing demands on a processor, the input data could be obtained with low-cost modules and middleware which are of low processing power while subjecting the actual learning algorithm execution to the cloud system.

Digital Forensics is an area of Forensics Science that uses the application of scientific method toward crime investigation. The thwarting of forensic evidence is known as anti-forensics, the aim of which is ambiguous in the sense that it could be bad or good. The aim of this project is to simulate digital crimes scenario and carry out forensic and anti-forensic analysis to enhance security. This project uses several forensics and anti-forensic tools and techniques to carry out this work. The data analyzed were gotten from result of the simulation. The results reveal that although it might be difficult to investigate digital crime but with the help of sophisticated forensic tools/anti-forensics tools it can be accomplished.