随着数字经济的蓬勃发展,数据已经成为新时代的重要生产要素,并成为国家基础性战略资源.一方面,构建不同行业、领域规范化数据开发利用场景,提升各行业数据资源的价值,促进数字经济产业集群发展成为大势所趋;另一方面,数据安全和隐私保护问题愈发突出,数据流转过程中暴露面风险急剧加大,面临严峻的数据安全风险防护和合规监管要求形势.
Based on the status quo of email technology development,this paper summarizes and reviews the overall situation and hidden risks of email security in China.From the perspective of social engineering,it studies the methods,types and application techniques of mainstream phishing email attacks in recent years,and introduces the working principles of malicious Trojan horse,self-extracting file and dynamic link library side loading and other attack techniques commonly used in malicious attachments to emails.This paper analyzes the risks and hidden dangers of phishing email attacks aggravated by new information technologies such as generative AI robot and multi-fragment program coding confusion,and puts forward countermeasures and suggestions on how to prevent and deal with the new phishing email attacks based on theory and reality.
In recent years,with the development of big data and artificial intelligence technology,log data oriented intelligent fault prediction gradually attracts research attention.Conventional fault prediction methods based on log data only focus on a single requirement attribute,making it difficult to adapt to the multi-dimensional requirement prediction.To address this problem,this paper proposes Multi-Det,an intelligent fault prediction model with multi-channel fusion,which designs specific scheduling method that incorporates machine learning and deep learning.Through feature extraction and data fusion,the system model is well adapted to different user requirement attributes to optimize the accuracy and reliability of fault prediction in specific scenarios.To verify the effectiveness of Multi-Det,experimental comparisons of requirement parameters in multiple scenarios are carried out on public datasets.The results indicate that the proposed method can effectively adapt to different fault prediction requirements,intelligently adjust the prediction strategy in specific scenarios,and provide strong support for the maintenance and management of device in specialized fields.
Artificial intelligence(AI)technology is one of the most revolutionary emerging technologies in the world today.The development wave of AI big models triggered by ChatGPT leads a paradigm change in AI technology development,presenting a trend towards more intelligence,automation,efficiency and reliability.AI technology is widely and deeply applied in the power industry,which strongly promotes the digital development of power grid enterprises.However,the application of AI technology is a"Double-edged sword",which has the risks of data security,cyber attacks,technical defects and social engineering,and requires comprehensive,safe and reliable protection measures to better leverage the advantages and value of AI technology and promote the development of the power industry.
Conventional network security situation assessment provides a decision-making basis for enterprise security risk management.According to the basic principles of zero-trust security,this paper first analyzes the connotation,goal and significance of zero-trust security situation assessment,and then puts forward a zero-trust security access architecture based on continuous risk assessment.Around this architecture,it studies key technologies of zero-trust security situation assessment,focuses on context security assessment for user access,constructs and designs an extensible situation assessment index system as well as a quantitative assessment algorithm,provides continuous assessment capabilities for achieving dynamic fine-grained access control under the zero-trust paradigm,and finally summarizes the future development trend of zero-trust situation assessment.
