This paper presents a routing performance analysis of structured P2P overlay network. Due to the rapid development and hectic life, sharing data wirelessly is essential. P2P allows participating peers move freely by joining and leaving the network at any convenience time. Therefore, it exists constraint when one measuring the network performance. Moreover, the design of structured overlay networks is fragmented and with various design. P2P networks need to have a reliable routing protocol. In order to analyse the routing performance, this work simulates three structured overlay protocols-Chord, Pastry and Kademlia using OMNeT++ with INET and OverSim module. The result shows that Pastry is the best among others with 100% routing efficiency. However, Kademlia leads with 12.76% and 18.78% better than Chord and Pastry in lookup hop count and lookup success latency respectively. Hence, Pastry and Kamelia architectures will have a better choice for implementing structured overlay P2P network.

VoIP application usage has increased from time to time and makes our daily life more convenient. VoIP application has features to make a phone call, send a text message and share the file through the apps for free. However, most of the users did not seem aware of VoIP security features such as authentication ability, password encryption ability, or voice or audio and text communication encryption ability. It is essential to ensure the VoIP used is secure from password decrypter and eavesdrops the user conversation. Thus, the first objective of this research was to study and investigate VoIP application consist of Kakao Talk, Telegram, Facebook Messenger and WhatsApp for both Android and web application. The second objective was to evaluate the four VoIP application identified based on authentication requirement, password encryption, voice or audio encryption communication, and text encryption communication. There were two mobile phones used. One acts as a client and a personal computer act as an attacker. Wireshark and packet capture were run in personal computer and mobile phone to monitoring and scanning the network traffic while both devices connected in the same WLAN. The experiment implements MITM, interception, and sniffing attacks. This research project has identified Facebook Messenger and WhatsApp web application do not provide secure password ability.

Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. Hence, it is important to detect Android malware since it could expose a great threat to the users. However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. Therefore, there is a need to discover the significant features in a minimal amount to assist with machine learning detection. Consequently, this study proposes the Pearson correlation coefficient (PMCC), a coefficient that measures the linear relationship between all features. Afterwards, this study adopts the heatmap method to visualize the PMCC value in the color of the heat version. For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. This experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA).

People in this new era of modernization nowadays take Internet as one of the vital thing for daily activities. Internet is not only for adults, it is also a needs for people of all ages. However, network vulnerabilities exist in all network that are connec ted to the Internet. The network mostly are exposed to the malicious software or mostly known as malware. In fact, this malware is growing rapidly and giving a bad impact to the human intervention. The number of attack are increasing rapidly and it comes i n various way just to exploit the victims. There are various type of malware attack. For instance, viruses, worms, spyware, rootkits, Trojan horse and botnet are considered as noteworthy threat for the computer network. Some people giving full confidence on the security of data transmission to the network. However, other can access the personal information without them realizing it. The objective of this paper is to detect malware attack using honeypot Dionaea. Malicious file launched was detected by the honeypot and the file was analyzed by using the sandbox tool, Virus Total. This paper found that honeypot Dionaea is helpful in detecting various types of malware attack.