Dr Adrian Tan

Communication network security has become increasingly vital in an era of rapidly developing technology, and protecting against unauthorized access is essential. This paper introduces a server-aided approach for secure key distribution to users participating in multiple sessions. This paper presents a system model in which each user is assigned a unique private key, enabling them to derive session keys from codewords broadcast by the server. These session keys are essential for facilitating the secure transmission of session messages within their respective sessions. The system model ensures that an eavesdropper cannot derive any session keys, despite having access to broadcast codewords, due to their lack of private keys. Our results show that our coding scheme is optimal by proving the necessary conditions for secure key distribution, indicating that secure key distribution is achievable if and only if the length of a user's private key is at least equal to the total size of session messages across all the sessions in which they participate. This paper further illustrates the proposed secure key distribution and session message transmission mechanism through examples, emphasizing the necessity of user-specific private keys tailored to the sessions in which users are involved.

In recent years, car theft cases are increasing at a drastic rate across the globe. Existing car security system can be hacked into and there is no alert system to notify the car owner. A face recognition-based car security system was proposed and developed, where only authentic user was allowed to have access to the car ignition system. Hardware and software designs were carried out to develop a car security system using microcontroller-based face recognition system. This system was developed using a Raspberry Pi 2 microcontroller. Incorporating with other hardware modules such as Pi NoIR camera, liquid crystal display (LCD) panel, modem router and Wi-Fi adapter, the face recognition system is capable of performing various tasks efficiently. The system uses Eigenfaces recognition algorithm, which was developed using OpenCV and Python scripts, to perform face detection and recognition. The performance of the Eigenfaces recognition system was improved with light emitting diode (LED) support lighting and increased input image database. A short message service (SMS) alert system was set up using a modem router to notify car owner in case of unauthorized user access. In addition, a web-based application was designed and developed to remotely control the car security access and to trigger the car alarm. Compared to other existing car security systems, this microcontroller-based face recognition system offers a secured environment for cars at lower power consumption and with remote monitoring and control features.