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To a great extent, Pakistan and Turkey rely on imported fossil fuels to meet their energy demands. Pakistan is moving in the right direction, placing focus on renewable energy resources in its current infrastructure in order to address the energy shortage. Several projects (e.g., wind power and solar PV (photovoltaic) technologies) are operational or under development; they are intended to reduce energy challenges in Pakistan. The new government in Pakistan aims to increase the share of renewable energy in total power generation to 30% by 2030. On the other hand, Turkey surpasses Pakistan in renewable energy resources; for example, there are 186 operational wind energy power plants across the country. In addition, Turkey utilizes solar energy—mainly for residential usage. Turkey’s Vision 2023 energy agenda aims to supply 30% of their power demands from modern renewable energy resources. Turkey has implemented solar PV, solar buildings, wind power plants, geothermal energy resources, and biomass technology for heating, cooling and electricity generation. At present, Turkey’s supply to meet energy demands in the country is 56% fossil fuel energy resources and 44% renewable energy, including hydropower. Accessible details reveal that geothermal energy resources have been continuously neglected in Pakistan by the Ministry of Energy (power division); this is in contrast to the Turkish case, in which utilization of geothermal energy resources for heating and cooling purposes is efficient. With all the facts and figures under consideration, in this paper, comparative analyses are performed which reveal that the production of electricity from geothermal energy technologies is lower than the massive potential in both countries. Recommendations are made for important policies to promote renewable energy technologies, which could effectively support energy decentralization by providing electricity to rural areas and the national grid.

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As time is progressing, the number and the complexity of methods adopted for launching distributed denial of service (DDoS) attacks are changing. Therefore, we propose a methodology for the development of a generalized machine learning (ML)-based model for the detection of DDoS attacks. After exploring various attributes of the dataset chosen for this study, we propose an integrated feature selection (IFS) method which consists of three stages and integration of two different methods, that is, filter and embedded methods to select features which highly contribute to the detection of various types of DDoS attacks. We use light gradient boosting machine (LGBM) algorithm for training of the model for classification of benign and malicious flows. For ensuring satisfactory performance and generalized behavior of the developed model, we test it by passing records of unseen DDoS attack types. Several performance metrics are employed for the evaluation of the model. By comparing the performance of developed model against state-of-the-art models, we state an improvement of around 20% for almost all the reported metrics. We also show that the performance of the model improves if feature space is reduced by 77%. Furthermore, the generalized behavior of the developed model is justified by demonstrating a trade-off between high variance and high bias ML models.

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