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Microstrip antennas have become a part and parcel of today's wireless communication world because of their low profile, low cost and ease of fabrication in the circuit boards. But poor performances like narrow bandwidth, low power handling capability, low gain etc. confine their application in some cases. 5th generation (5G) wireless communication will suffer from path loss severely, as high frequency bands will be used. To manage this problem, high gain antenna is required. So, this research is mainly devoted to design a high gain 2 × 2 microstrip patch array antenna. The structure of the antenna is designed and simulated using CST Microwave Studio and operates at 28 GHz 5G band. Rogers RT-Duroid 5880LZ is used as the substrate which has a relative permittivity of 1.96. The return loss, gain, bandwidth, VSWR and efficiency of the designed 2 × 2 array antenna is 87 dB, 14 dBi, 1.14 GHz, 1 and almost 93% respectively.

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Integrating intelligence into intrusion detection tools has received much attention in the last years. The goal is to improve the detection capability within SIEM and IDS systems in order to cope with the increasing number of attacks using sophisticated and complex methods to infiltrate systems. Current SIEM and IDS systems have many processes involved, which work together to collect, analyze, detect, and send notification of failures in real time. Event normalization, for example, requires significant processing power to handle network events. So, adding heavy deep learning models will invoke additional resources for the SIEM or IDS tool. This paper presents a majority system based on reliability approach that combines simple feedforward neural networks, as weak learners, and produces high detection capability with low computation resources. The experimental results show that the model is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of complex resource-intensive deep learning models.

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