In practice, identification of criminal in Malaysia is done through thumbprint identification. However, this type of identification is constrained as most of criminal nowadays getting cleverer not to leave their thumbprint on the scene. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. However, because of limited software developed to automatically detect the similarity between photo in the footage and recorded photo of criminals, the law enforce thumbprint identification. In this paper, an automated facial recognition system for criminal database was proposed using known Principal Component Analysis approach. This system will be able to detect face and recognize face automatically. This will help the law enforcements to detect or recognize suspect of the case if no thumbprint present on the scene. The results show that about 80% of input photo can be matched with the template data.

Audio steganography is about hiding the secret message into the audio. It is a technique uses to secure the transmission of secret information or hide their existence. It also may provide confidentiality to secret message if the message is encrypted. To date most of the steganography software such as Mp3Stego and DeepSound use block cipher such as Advanced Encryption Standard or Data Encryption Standard to encrypt the secret message. It is a good practice for security. However, the encrypted message may become too long to embed in audio and cause distortion of cover audio if the secret message is too long. Hence, there is a need to encrypt the message with stream cipher before embedding the message into the audio. This is because stream cipher provides bit by bit encryption meanwhile block cipher provide a fixed length of bits encryption which result a longer output compare to stream cipher. Hence, an audio steganography with embedding text with Rivest Cipher 4 encryption cipher is design, develop and test in this project.

The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.

The key derivation function is a specific cryptographic algorithm that transforms private string and public strings into one or more cryptographic keys. The cryptographic keys are essential for protecting electronic data during transmission on the internet. This function is designed based on a computational extractor and pseudorandom expander and is typically constructed using various cryptography ciphers such as stream ciphers, keyed-hash message authentication codes, and block ciphers. Having secure and efficient key derivation function designs is essential in the development of numerous security systems. A vulnerable key derivation function could potentially give attackers the ability to compromise an otherwise secure cryptosystem. This research proposes a different approach by combining two different cryptography ciphers to develop key derivation functions. The findings demonstrate that a computational extractor utilizing keyed-hash message authentication codes and a pseudorandom expander using stream ciphers maintain the highest level of security while also providing efficiency benefits in terms of execution time compared to existing key derivation function schemes.