Mobile computing has supplanted internet computing because of the proliferation of cloud-based applications and mobile devices (such as smartphones, palmtops, and tablets). As a result of this, workers bring their mobile devices to the workplace and use them for enterprise work. The policy of allowing the employees to work with their own personal mobile devices is called Bring Your Own Devices (BYOD). In this article, we discuss BYOD's background, prevalence, benefits, challenges, and possible security attacks. We then review contributions of academic researchers on BYOD. The Universiti Putra Malaysia online databases (such as IEEE Xplore digital library, Elsevier, Springer, ACM digital library) were used to search for peerreviewed academic publications and other relevant publications on BYOD. The Google Scholar search engine was also used. Our thorough review shows that security issues comprise the most significant challenge confronting BYOD policy and that very little has been done to tackle this security challenge. It is our hope that this review will provide a theoretical background for future research and enable researchers to identify researchable areas of BYOD.
Abstract-One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.
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