This research aims to analyse the most frequent vulnerabilities of Cameroonian governmental web portals and to suggest a suitable monitoring infrastructure. In fact, the technological evolution and the need to intensify the local communication enabled companies and governmental institutions to set up web portals for e-governance, e-education, eadvertising, e-payments, e-registration, e-commerce etc. These e-services manipulate quite sensitive data of individuals and governmental institutions. Attackers are therefore attracted to launch malicious actions to take control over these web systems. After analysing several Cameroonian web portals, one of our main findings is that, unfortunately, administrators of such systems lack expertise and even are not aware of the use of diverse systems for monitoring and securing their infrastructures, exposing therefore the life of users. As a consequence, several attacks perpetrated against web portals are increasingly being registered since 2012. We have analysed 55 web portals. Three groups of vulnerabilities have been identified and categorised. As a solution approach, we propose a distributed monitoring architecture for national web portals. The architecture aims to gather automatically information and analyse them for assistance in security decisionmaking. This work constitutes a considerable step towards preventive measures for strengthening cybersecurity in Cameroon.
In common Internet environments, most of the websites or services constrain the user account creation. Since the Internet is accessible by all and offers more and more services, a user has several accounts on the web. The difficulty in controlling their accounts does not leave indifferent to the users of the web. Hence the use of easy or insecure passwords. This is why we are victims of attacks and forgetting our passwords. Large companies such as Facebook, Google, etc., offer authorization and authentication mechanisms using the Oauth and OpenID protocol, which requires the opening of an account. To be independent of a social network or a site, it would be important to develop a model to make a statistical analysis between the attributes of the profiles of the same user and to create an account. Using the same password for all its different accounts could be an approach but avoiding the proliferation of data by proposing a model of identity analysis would be even more interesting. That is why this article proposes a centralized account management model by making a comparative and statistical study of the identity attributes and proposing a single account to the user to manage all its different accounts. So, we have a horizontal analysis between the attributes of the identity categories and a vertical analysis between these categories. This study allowed us to find a threshold to conclude that an account belongs to a user.
Recommendation systems are a type of systems that are able to help users finding relevant and personalized content in a wide variety of possibilities. To help computers perform recommendations, there are several approaches used nowadays such as the Content-based approach, the Collaborative filtering approach and the Hybrid recommendation approach. However, these approaches are sometimes inappropriate for use cases where there is no prior large datasets of users’ feedbacks or ratings needed for training Machine Learning models. Thus, in this work, we proposed a novel approach based on the combination of Fuzzy Logic and the k-Nearest neighbor algorithm (KNN). The proposed approach can be applied without any prior collected feedbacks of users and performs good recommendations. Moreover, our proposal uses Fuzzy Logic to infer values based on inputs and a set of rules. Furthermore, the KNN uses the output values of the Fuzzy Logic system to do some retrieval tasks based on existing distance measures. In order to evaluate our approach, we considered an expert system of food recommendation for people suffering from the two deadliest diseases in Cameroon: HIV/AIDS and Malaria. The obtained results are closed to the recommendation made by nutritionists. These results demonstrate how effective our approach can be used to solve a real nutrition problem for people suffering from Malaria or HIV/AIDS. Furthermore, this approach can be extended to other fields and even be used to perform any recommendation task where there is no prior collected user’s feedback or ratings by using the proposed approach as a framework.
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