Biblio

People are increasingly sharing their details online as internet usage grows. Therefore, fraudsters have access to a massive amount of information and financial activities. The attackers create web pages that seem like reputable sites and transmit the malevolent content to victims to get them to provide subtle information. Prevailing phishing security measures are inadequate for detecting new phishing assaults. To accomplish this aim, objective to meet for this research is to analyses and compare phishing website and legitimate by analyzing the data collected from open-source platforms through a survey. Another objective for this research is to propose a method to detect fake sites using Decision Tree and Random Forest approaches. Microsoft Form has been utilized to carry out the survey with 30 participants. Majority of the participants have poor awareness and phishing attack and does not obverse the features of interface before accessing the search browser. With the data collection, this survey supports the purpose of identifying the best phishing website detection where Decision Tree and Random Forest were trained and tested. In achieving high number of feature importance detection and accuracy rate, the result demonstrates that Random Forest has the best performance in phishing website detection compared to Decision Tree.

Phishing emails have affected users seriously due to the enormous increasing in numbers and exquisite camouflage. Users spend much more effort on distinguishing the email properties, therefore current phishing email detection system demands more creativity and consideration in filtering for users. The proposed research tries to adopt creative computing in detecting phishing emails for users through a combination of computing techniques and social engineering concepts. In order to achieve the proposed target, the fraud type is summarised in social engineering criteria through literature review; a semantic web database is established to extract and store information; a fuzzy logic control algorithm is constructed to allocate email categories. The proposed approach will help users to distinguish the categories of emails, furthermore, to give advice based on different categories allocation. For the purpose of illustrating the approach, a case study will be presented to simulate a phishing email receiving scenario.

Nowadays, our surrounding environment is more and more scattered with various types of sensors. Due to their intrinsic properties and representation formats, they form small islands isolated from each other. In order to increase interoperability and release their full capabilities, we propose to represent devices descriptions including data and service invocation with a common model allowing to compose mashups of heterogeneous sensors. Pushing this paradigm further, we also propose to augment service descriptions with a discovery protocol easing automatic assimilation of knowledge. In this work, we describe the architecture supporting what can be called a Semantic Sensor Web-of-Things. As proof of concept, we apply our proposal to the domain of smart buildings, composing a novel ontology covering heterogeneous sensing, actuation and service invocation. Our architecture also emphasizes on the energetic aspect and is optimized for constrained environments.