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.

Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone requires a modification in its data transformation and data privacy mechanisms, and it is not yet fulfilling domain requirements. This paper aims to investigate recent privacy and security trends that are affecting the Internet of Drones (IoD). This study also highlights the need for a safe and secure drone network that is free from interceptions and intrusions. The proposed framework mitigates the cyber security threats by employing intelligent machine learning models in the design of IoT-aided drones by making them secure and adaptable. Finally, the proposed model is evaluated on a benchmark dataset and shows robust results.

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Protection of sensitive or classified data from unauthorized access, hackers and other personals is virtue. Storage of data is done in devices such as USB, external hard disk, laptops, I-Pad or at cloud. Cloud computing presents with both ups and downs. However, storing information elsewhere increases risk of being attacked by hackers. Besides, the risk of losing the device or being stolen is increased in case of storage in portable devices. There are array of mediums of communications and even emails used to send data or information but these technologies come along with severe weaknesses such as absence of confidentiality where the message sent can be altered and sent to the recipient. No proofs are shown to the recipient that the message received is altered. The recipient would not find out unless he or she checks with the sender. Without encrypted of data or message, sniffing tools and software can be used to hack and read the information since it is in plaintext. Therefore, an electronic mail authentication is proposed, namely Hybrid Encryption System (HES). The security of HES is protected using asymmetric and symmetric key algorithms. The asymmetric algorithm is RSA and symmetric algorithm is Advance Encryption Standard. With the combination for both algorithms in the HES may provide the confidentiality and authenticity to the electronic documents send from the sender to the recipient. In a nutshell, the HES will help users to protect their valuable documentation and data from illegal third party user.