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Growing client population, ever-increasing service demand, and complexity of services are the driving factors for the mobile operators for a paradigm shift in their core technology and radio access networks. 5G mobile network is the result of this paradigm shift and currently under deployment in many developed countries such as United States, United Kingdom, South Korea, Japan, and China-to name a few. However, most of the Least Developed Countries (LDCs) have very recently been implemented 4G mobile networks for which the overall role out phase is still not complete. In this paper, we investigate how feasible it is for LDCs to emphasize on a possible deployment of 5G networks at the moment. At first, we take a holistic approach to show the major technical challenges LDCs are likely to face while deploying the 5G mobile networks. Then we argue that various security aspects of 5G networks are an ongoing issue and LDCs are not technologically competent to handle many security glitches of 5G networks. At the same time, we show that most of the use cases of 5G networks are not applicable in the context of many LDCs (at least at the present time). Finally, this paper concludes that the start of the 5G network deployment in LDCs would take much longer time than expected.

The threats of botnets are becoming a growing concern infecting more and more computers every day. Although botnets can be detected from their behavioral patterns, it is becoming more challenging to differenti-ate the behavior between the malicious traffic and the legitimate trafficas with the advancement of the technologies the malicious traffics are fol-lowing the similar behavioral patterns of benign traffics. The detectionof malicious traffic largely depends on the traffic features that are beingused to feed in the detection process. Selecting the best features for effec-tive botnet detection is the main contribution of this paper. At the verybeginning, we show the impact of different features on botnet detectionprocess. Then we propose several heuristics to select the best featuresfrom a handful of possible features. Some proposed heuristics are trulyfeature-based and some are group-based, thus generating different accu-racy levels. We also analyze time complexity of each heuristic and providea detailed performance analysis. As working with all combinations of alarge number of features is not feasible, some heuristics work by groupingthe features based on their similarity in patterns and checking all combi-nations within the groups of small number of features which improves thetime complexity by a large margin. Through experiments we show the efficacy of the proposed feature selection heuristics. The result shows thatsome heuristics outperform state-of-the-art feature selection algorithms.

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