科技期刊钓鱼网站常用技术手段分析及防范措施

【目的】 分析科技期刊钓鱼网站常用技术手段,并提出防范措施。【方法】 总结科技期刊钓鱼网站屡禁不止的原因,分析钓鱼网站常用的技术手段,从科技期刊、作者、政府相关部门、互联网企业等方面提出防范科技期刊钓鱼网站的具体措施。【结果】 假冒科技期刊官方网站网址和利用漏洞攻击科技期刊官方网站是科技期刊钓鱼网站常用技术手段。为了遏制钓鱼网站的不法行为,科技期刊编辑部需重视网络安全建设,并努力提升官方网站的可见度;作者应提高防范意识;政府相关部门需提高监管水平;互联网企业应加大防范力度。【结论】 只要科技期刊、作者、政府相关部门、互联网企业等多方共同采取切实有效的防范措施,定会让钓鱼网站再无可乘之机。     

网络钓鱼的危害及防范措施

为了更好的保护消费者的钱财不受到侵害,列举了当今几种主要的网络钓鱼犯罪手法,并提出了一些参考性的防范方法,以期能给广大消费者提供借鉴和帮助.     

Relationship building and behavioral outcomes: Exploring the connection between relationship attitudes and key constituent behavior

The field of public relations historically has been charged with managing relationships between organizations and publics. Those who build relationships with key constituents, however, struggle with determining whether relationship building activities influence key constituent behavior. The current investigation tracked college students in an attempt to better understand the role that relationship attitudes play in retention. The results showed that those respondents who identify themselves as in relationship with the university were much more likely to return. The investigation then sought to determine which university‐student relationship attitudes best differentiate those students who say they are in relationship with the university from those who say they are not or are uncertain. Those results showed that community relationships were strongly related, while the personal and professional relationships were substantially related to respondent self‐identification of being in relationship with the institution. The ramifications for relationship building, directions for future research, and limitations to the current investigation were presented.     

Phishing scams detection via temporal graph attention network in Ethereum

Recently, phishing scams have become one of the most serious types of crime involved in Ethereum, the second-largest blockchain-based cryptocurrency platform. The existing phishing scams detection techniques for Ethereum mostly use traditional machine learning or network representation learning to mine the key information from the transaction network and identify phishing addresses. However, these methods typically crop the temporal transaction graph into snapshot sequences or construct temporal random wanderings to model the dynamic evolution of the topology of the transaction graph. In this paper, we propose PDTGA, a method that applies graph representation learning based on temporal graphs attention to improve the effectiveness of phishing scams detection in Ethereum. Specifically, we learn the functional representation of time directly and model the time signal through the interactions between the time encoding function and node features, edge features, and the topology of the graph. We collected a real-world Ethereum phishing scam dataset, containing over 250,000 transaction records between more than 100,000 account addresses, and divided them into three datasets of different sizes. Through data analysis, we first summarized the periodic pattern of Ethereum phishing scam activities. Then we constructed 14 kinds of account node features and 3 kinds of transaction edge features. Experimental evaluations based on the above three datasets demonstrate that PDTGA with 94.78% AUC score and 88.76% recall score outperforms the state-of-the-art methods.     

Credit card incidents and control systems

Credit and debit cards have spread and skyrocketed all around the world to become the most popular means of payments in many countries. Despite their enormous popularity, cards are not free of risk. Technology development and e-commerce have exponentially increased internal credit card incidents. This paper identifies and quantifies the different types of credit card fraud and puts into question the effectiveness of the role assigned to cardholders in its detection.