Abstract:Internet of Things (IoT) enables smart campuses more convenient for cloud services. The availability of cloud resources to its users appears as a fundamental challenge. The existing research presents several auto-scaling techniques to scale the resources with the increase in users' demands. However, still, the cloud users of auto-scaled servers experience service disruption, delayed responses, and the occurrence of service bursts. The prevailing burst management framework exhibits limitations in the context of… Show more
“…After adjusting the sample weight, the sampling training process using the improved LM algorithm is shown in Figure 3. From Figure 4, when the unimproved neural network is trained 100 times, there is still a big gap from the error of 10 -2 , and the neural network combined by the SOM method and the LM method, convergence is reached after 20 trainings [52][53][54][55][56][57][58] . Using the above combination of SOM method and LM method, the training process is shown in Figures…”
To study smart data collection and network error analysis, this paper proposes intelligent data collection and network error analysis based on artificial intelligence. It examines the establishment of an enterprise-level information security situation awareness system and proposes specific information security models, architectures, and implementation methods. By designing and deploying the system, businesses can effectively detect information security threats, receive threats, filter risks, control threats, and comprehensively improve businesses' ability to detect security threats and security attacks. Test results: Through this platform, it is possible to manually intervene in the unknown threat of large data analysis in the system, and professionals can perform a detailed analysis to determine the means, goals and objectives of the attack and restore the complete picture. Intruder through artificial intelligence combined with big data knowledge and intrusion. Dimensional human characteristics. Including similar Trojans and malicious servers with different application forms, encodings, and attack principles, they "track" intruders by their general characteristics, constantly detect unknown threats, and ultimately ensure the accuracy of unknown threat detection, creating a local threat intelligence analytics platform.
Practice has shown that the intelligent acquisition of large data by artificial intelligence can effectively analyze network failures.Povzetek: S pomočjo umetne inteligence je narejena analiza napak v omrežjih in zbiranje podatkov.
“…After adjusting the sample weight, the sampling training process using the improved LM algorithm is shown in Figure 3. From Figure 4, when the unimproved neural network is trained 100 times, there is still a big gap from the error of 10 -2 , and the neural network combined by the SOM method and the LM method, convergence is reached after 20 trainings [52][53][54][55][56][57][58] . Using the above combination of SOM method and LM method, the training process is shown in Figures…”
To study smart data collection and network error analysis, this paper proposes intelligent data collection and network error analysis based on artificial intelligence. It examines the establishment of an enterprise-level information security situation awareness system and proposes specific information security models, architectures, and implementation methods. By designing and deploying the system, businesses can effectively detect information security threats, receive threats, filter risks, control threats, and comprehensively improve businesses' ability to detect security threats and security attacks. Test results: Through this platform, it is possible to manually intervene in the unknown threat of large data analysis in the system, and professionals can perform a detailed analysis to determine the means, goals and objectives of the attack and restore the complete picture. Intruder through artificial intelligence combined with big data knowledge and intrusion. Dimensional human characteristics. Including similar Trojans and malicious servers with different application forms, encodings, and attack principles, they "track" intruders by their general characteristics, constantly detect unknown threats, and ultimately ensure the accuracy of unknown threat detection, creating a local threat intelligence analytics platform.
Practice has shown that the intelligent acquisition of large data by artificial intelligence can effectively analyze network failures.Povzetek: S pomočjo umetne inteligence je narejena analiza napak v omrežjih in zbiranje podatkov.
“…The above control policy architecture diagram shows that the security access control policy designed in this paper consists of three layers, namely, application layer, platform layer, and sensing layer [13] . Among them, the application layer is mainly responsible for sensing the security posture of the heterogeneous cloud platform and unified operation management for accessing users according to the current sensing results.…”
The current conventional illegal access behavior security control strategy for heterogeneous cloud resources mainly realizes the access control of resources by conceiving the digital identity of access units, which leads to the low security of access control due to the low encryption of heterogeneous cloud resources. In this regard, the heterogeneous cloud resource illegal access behavior security control strategy based on life cycle characteristics is proposed. By analyzing the user access history behavior data, the trust value of user access operation is calculated. And the access control policy is constructed based on the encryption of user's operation behavior as well as resources by using a two-layer encryption algorithm. In the experiment, the designed illegal access behavior security control policy is tested for security type. The final result can prove that when using the proposed method for access control of heterogeneous cloud resources, the speed of intercepting illegal users is faster, and when facing various types of attacks, this method has a low success rate and high access control security.
“…In the event that the vertical threshold is exceeded by new requests, cost estimation server builds log of the current load estimations of horizontal and vertical scales and servers demands from new users. Study simulates 1000 smart campus user requests, adopts a cutting-edge ensemble with bagging approach, and effectively manages a class imbalance scenario [44].…”
Section: Cloud Computingmentioning
confidence: 99%
“…After being designed with operational economies in mind, the ESS model is incorporated into mixed integer linear programming (MILP). The method is tested in a campus MG and put into use with ESSs and PV arrays [44].…”
According to the United Nations, global sustainability in terms of social, economic, and environmental issues must be achieved by 2030. SDGs 4 and 9 are related to education and strengthen the attainment of quality education and infrastructure innovation. Resilient infrastructure plays a significant role in strengthening the campus in terms of education, management, placement and environment. These all aspects come under the smart campus. Smart campus 4.0 is the amalgamation of multitude industry 4.0 enabling technologies for delivering smart and innovative facilities with the aspect of sustainability. The previous studies have proved that the sustainable development goals (SDGs) can be achieved with the amalgamation of industry 4.0 enabling technologies in the campus such as cloud computing, artificial intelligence (AI), Internet of things (IoT), edge/fog computing, blockchain, robot process automation (RPA), drones, augmented reality (AR), virtual reality (VR), big data, digital twin, and metaverse. The main objective of this study to provide the detailed discussion of all industry 4.0 enabling technologies in single research related to smart campus. The findings observed are IoT-Based Drone system is intended to ground patrolling, and a cloud server to develop a smart campus energy monitoring system. AI for campus placement prediction model; cloud and Edge computing architecture to build an intelligent air-quality monitoring system. The novelty of the study, it has discussed all industry 4.0 enabling technologies for a smart campus with challenges, recommendations, and future directions.
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