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The oil and gas (O&G) industry is the engine of the global economy. Oil and gas production passes through axes related to exploration, research, extraction, transportation, and finally the final manufacturing of energy products. All these stages permeate some risks that threaten both the human factor and the material factor. The oil industry merged with the fourth technological industry 4.0, which included multiple technologies and systems, the most important of which is the cyber-physical system (CPS), which some researchers have named petroleum cyber-physical systems if it is embodied within this industry. CPSs are collaborative systems formed of autonomous and smart devices that can handle data flows and activities while maintaining integrated physical objects. Several risks confront the energy field, with the potential to interrupt critical supply lines, hurt the environment, and trigger a financial catastrophe. In the field of O&G, there are very few scientific studies that are exposed to risks in a complementary and comprehensive manner, including only those that focus on cyber-attacks and their causes. There is a lack of comprehension and in-depth studies of all types of threats in all their aspects that surround cyber-physical systems within this field. Some risk classifications are based on internal and external risks, while others are based on the influencing and causative aspects in a general way. This study deals with the classification of risks: 1) classification of risk for the global industry of O&G. 2) in terms of the fact that the cyber-physical system is the most important component in the O&G industry and that these risks are either physical, cyber, or related to permissibility and authorization in the O&G field. A security approach is also presented that leads to mitigating the impact of risks in oil and gas zones. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid filter-based ensemble multi-class classification model for distributed privacy-preserving applications. The conventional privacy-preserving multi-class learning models have limited capacity to enhance the true positive rate, mainly due to computational time and memory constraints, as well as the static nature of metrics for parameter optimization and multi-class perturbation processes. In this research, we develop the proposed model on large medical and market databases with the aim of enhancing multi-party data confidentiality through a security framework during the privacy-preserving process. Moreover, we also introduce a secure multi-party data perturbation process to improve computational efficiency and privacy-preserving performance. Experimental results were evaluated on different real-time privacy-preserving datasets, such as medical and market datasets, using different statistical metrics. The evaluation results demonstrate that the proposed multi-party-based multi-class privacy-preserving model performs statistically better than conventional approaches. [ABSTRACT FROM AUTHOR]

WSNs (Wireless Sensor Networks) are becoming more and more capable with each passing day. Widespread usage of WSNs is helping mitigate many dangers. Protecting these networks from risks need proper security measures. This paper offered a security strategy that makes use of IEKC (Improved Elliptic Key Cryptography). Data encryption and decryption are accomplished via IKEC. A major feature of this technique is that node's source codes maintain Private Keys which protect other keys in non-volatile memories. The suggested technique prevents replay, DoS and Sybil attacks. In this node, cluster chiefs are chosen using CSO (Chicken Swarm Optimization) and nodes are organised according to network size. According to the findings of the simulation, this security mechanism effectively counters different attacks with fewer resources, and it also significantly strengthens the network's resistance to node capture. Results from experiments show that the suggested model is effective regarding PDR (packet delivery ratio), end-to-end latency, throughputs, and energy consumptions.