Assessment of Predictors to Access Anti-Psychotic Medications among patients with Schizophrenia in some selected Hospitals of Jigawa State, Nigeria

Ado Shehu, Attahir Sa’adAyuba, Ummukulsum Mustapha, Muftahu Sa’adu, Barr. Baffa Alasan, Emmanuel Ejembi Anyebe, Usman Sanusi Usman, Hayat Gomaa, Kabiru Sabitu, Saleh Ngaski Garba, Zulkiflu Musa Argungun , Usman Yahaya , Murtala Hassan Hassan , Aliyu Muhammad Maigoro


Keywords: Cloud computing, Cybersecurity, Distributed Denial of Service (DDoS), Load Balancing


Background: Businesses have embraced the burgeoning technology known as cloud computing. This entails the provision of various services over the Internet. Connectivity, availability and security are three major requirements that cloud service providers should always ensure. The cloud computing environment faces several difficult security concerns one of which is the distributed denial of service attacks. Attacks that cause a denial of service puts availability at risk. Conventional methods of mitigating DDoS assaults fall short due to noticeable server downtime and poor quality of service, users still experienced server downtime and performance issues. Also, existing load balancing algorithm used to mitigate DDoS attack all had a common problem which is single point of failure.

Method: This study proposes a load balancing approach to mitigate DDoS attack while eliminating the problem of single point failure associated with the conventional approaches. This is achieved by using cloud automation tools and providing effective configurations for the servers and monitoring tool. The proposed design was implemented, and 20 scenarios of DDoS attacks were used to investigate the effectiveness of the design. Results: Upon testing, the results show a reduction in the duration of server downtime whenever an attack happens. The measured time is with respect to the server downtime duration. From previous cases of DDoS attack that some organization experienced, the duration from the attack detection to the attack mitigation was about 20 mins. But with the proposed algorithm, the duration from attack detection to mitigation falls within 5 mins.