Aim: The Domain Name System (DNS) plays an integral role in the functionality of the Internet. Clients receive Internet service by mapping domain names into internet protocol addresses, which are routable. DNS provides a scalable and flexible name resolution service to clients easily and quickly. However, DNS was initially developed without security, and the information is not secured. Although DNS security extensions was released in 1999 to protect against vulnerabilities, it is not widely deployed, and DNS continues to suffer from a variety of attacks. The purpose of this study is to provide a comprehensive survey of DNS security.

Methods: We describe an overview of DNS vulnerabilities, DNS attacks, and even mitigation systems. In detail, attacks are classified by purpose and methods for defending against these attacks are introduced and assessed. Finally, we conclude with a summary of the current state of DNS security.

Results: The main findings of this study is to introduce fundamental vulnerabilities of DNS and classify representative DNS attacks into four categories to efficiently analyze them. Moreover, we describe and assess mitigation systems to defense these attacks.

Conclusion: We conclude that DNS is an integral part of Internet operations but is still exposed to various attacks due to its vulnerabilities, low deployment of available mitigation techniques, and limitations of such techniques.

Aim: Current crime behavior observation has the problem of not being real time, thus criminal behavior cannot be promptly controlled. To improve the control of criminal behavior, this study was based on cloud computing image processing, and adopted data mining for criminal behavior.

Methods: This study obtained many criminal behavior characteristics through data collection and combined the rapid response capability of cloud computing to adopt data processing. In addition, to improve the accuracy of criminal behavior recognition, the identification method for criminal behaviors in selected populations was studied, and the image processing technology was combined to identify individual crimes and subject segmentation.

Results: Our work used statistical methods to collect the characteristics of criminal behavior, and we designed experiments to verify the effectiveness of the algorithm. The experimental research shows that the algorithm has high accuracy in identifying abnormal behavior.

Conclusion: The research shows that the accuracy of the algorithm for identifying abnormal behavior is relatively high, and it has high practical value, which can meet the accuracy and real-time requirements of security systems.

Aim: The aim of this paper is to explore whether and how far organisations learn from failures.

Methods: The paper reviews the current literature about organisational learning and theories of learning from failures, where learning here implies change of practice, and use of modelling techniques to inform recommendations to prevent repetition of similar incidents. Further, it analyses two case studies related to aspects of security and safety: the Virginia Tech Shooting in 2007 and the Lion Air 610 crash in 2018. Both case studies address the concept of learning from failures. In doing so, an analysis of the root causes and vulnerabilities through the methods of Fault Tree Analysis and Reliability Block Diagram is conducted to identify lessons to be learned.

Results: Findings are intended to stimulate organisational learning and improve organisational processes to mitigate disasters from happening again.

Conclusion: The value of this study is that aspects of learning and unlearning from failures have been identified for the cases used. Expectation for future studies is to extend the proposed methodology to other cases in the fields of surveillance, security and safety.