As the construction of the Energy Internet has launched into a novel method, the dependence on energy and information networks has been greatly increased. The interaction of energy and information flow has become recurring, and the deep coupling of energy and information flow has been realized. As a result, security risk factors have increased substantially. Given that, this article from the perspective of energy and information flow, combined with the “Source-Grid-Load-Storage” coordination scenario of the energy Internet and the actual development needs of the information network, this article proposes a risk assessment for many security risks that threatens the stable construction of the energy Internet Index system and corresponding index analysis.
As an emerging product under the condition of informatization, the utilization of cloud platform in many industries has brought fundamental changes to the production and business model in related fields. The cloud platform provides rich and diverse utilization services to terminals through multi-dimensional integration of different IT resources. With the in-depth utilization of cloud platform, the security problems it faces are becoming more and more prominent. The traditional network security protection means have been difficult to effectively adapt to and deal with the security threats under the new situation of cloud platform utilization. As a prominent part of building cloud platform, the construction level of virtualization security protection system will have an intuitive impact on the security of cloud platform. At present, the virtualization security protection management system under cloud platform is facing direct threats from virtual machine deployment, virtual machine communication and virtual machine migration. Based on this, this paper studies the virtualization security protection management system of cloud platform from the perspective of virtualization security tech, so as to ameliorate the stability, reliability and security of cloud platform.
Mixed dish, which mixes different types of dishes in one plate, is a popular kind of food in East and Southeast Asia. Identifying the dish type in the mixed dish is essential for dietary tracking, which gains increasing research attention recently. Nevertheless, mixed dish detection is a challenging task because of large visual variances among dishes in different canteens, which is known as the domain shifting problem. Since collecting and annotating sufficient training samples in each canteen for model training is difficult, a more practical way is developing detection models that can adapt quickly to crosscanteen mixed-dish detection with less supervision information. To this end, we propose a novel framework called Weakly-supervised Mean Teacher Network (WMT-Net) that addresses this specific detection task in a weakly supervised manner, where bounding box annotations are not required in the target domain. The proposed WMT-Net constructs Mean Teacher learning by maintaining the image-level consistency between teacher and student modules. Specifically, WMT-Net firstly learns instance-level information from the source dataset in a fully supervised fashion for the student model. Then the whole architecture is optimized with weakly supervised learning: 1) weakly supervised training in student model to reduce the domain gap in global semantics between source data and target data, 2) image-level consistency to align the imagelevel predictions between teacher model and student model. Experimental results on mixed-dish dataset show that even the proposed WMT-Net is trained in a weakly supervised fashion on the target domain, the performances attained by WMT-Net are very close to the model trained in a fully supervised fashion, which verify the effectiveness of WMT-Net. In addition, the proposed WMT-Net also achieves 44.6% mAP on Pascal VOC to Clipart cross-domain detection, which improves 7.2% mAP compared with the state-of-thearts method and further demonstrates its generalization capabilities.
With the gradual expansion of network scale, network security problems caused by intrusion attacks and Trojan horse viruses follow. Network attacks are highly targeted and diverse. With a large number of Internet of Things devices in the power system connected to the Internet, the interaction of heterogeneous information and the rapid change of network structure drive the dynamic development of the Internet of Things environment. It further expands the attack surface that may be threatened, and constantly generates new weaknesses and threats. In this paper, a rule-based reasoning method for multi-source knowledge in the security of the Internet of Things was proposed. First, a description logic-based language to represent the classes in the model was adopted. Reasoning rules were designed to supplement the semantic representation ability of the description language, which is to realize the reasoning of implicit facts from multi-source heterogeneous knowledge and data in the security field of the Internet of Things. Compared with the entropy method, the model is proved to be effective in predicting the actual network security situation and has certain practical guiding significance for the actual network security management.
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