Biblio

Based on the campus wireless IPv6 network system, using WiFi contactless sensing and positioning technology and action recognition technology, this paper designs a new campus security early warning system. The characteristic is that there is no need to add new monitoring equipment. As long as it is the location covered by the wireless IPv6 network, personnel quantity statistics and personnel body action status display can be realized. It plays an effective monitoring supplement to the places that cannot be covered by video surveillance in the past, and can effectively prevent campus violence or other emergencies.

In order to improve the accuracy and efficiency of transmission line safety early warning, a transmission line safety early warning system based on big data variable analysis is proposed. Firstly, the overall architecture of the system is designed under the B / S architecture. Secondly, in the hardware part of the system, the security data real-time monitoring module, data transmission module and security warning module are designed to meet the functional requirements of the system. Finally, in the system software design part, the big data variable analysis method is used to calculate the hidden danger of transmission line safety, so as to improve the effectiveness of transmission safety early warning. The experimental results show that, compared with the traditional security early warning system, the early warning accuracy and efficiency of the designed system are significantly improved, which can ensure the safe operation of the transmission line.

The recent technological advances and changes in the daily human activities increased the production and sharing of data. In the ecosystem of interconnected systems, data can be circulated among systems for various reasons. This could lead to exchange of private or sensitive information between entities. Data Sanitisation involves processes and practices that remove sensitive and private information from documents before sharing them with entities that should not have access to this information. This paper presents the design and development of a data sanitisation and redaction solution for a Cyber Threat Intelligence sharing platform. The Data Sanitisation and Redaction Plugin has been designed with the purpose of operating as a plugin for the ECHO Project’s Early Warning System platform and enhancing its operative capabilities during information sharing. This plugin aims to provide automated security and privacy-based controls to the concept of CTI sharing over a ticketing system. The plugin has been successfully tested and the results are presented in this paper.

In order to better ensure the operation safety of the network, the real-time early warning of network security threats is studied based on the improved ant colony algorithm. Firstly, the network security threat perception algorithm is optimized based on the principle of neural network, and the network security threat detection process is standardized according to the optimized algorithm. Finally, the real-time early warning of network security threats is realized. Finally, the experiment proves that the network security threat real-time warning based on the improved ant colony algorithm has better security and stability than the traditional warning methods, and fully meets the research requirements.

Signature-based Intrusion Detection Systems (IDS) are a key component in the cybersecurity defense strategy for any network being monitored. In order to improve the efficiency of the intrusion detection system and the corresponding mitigation action, it is important to address the problem of false alarms. In this paper, we present a comparative analysis of two approaches that consider the false alarm minimization and alarm correlation techniques. The output of this analysis provides us the elements to propose a parallelizable strategy designed to achieve better results in terms of precision, recall and alarm load reduction in the prioritization of alarms. We use Prelude SIEM as the event normalizer in order to process security events from heterogeneous sensors and to correlate them. The alarms are verified using the dynamic network context information collected from the vulnerability analysis, and they are prioritized using the HP Arsight priority formula. The results show an important reduction in the volume of alerts, together with a high precision in the identification of false alarms.

False alarm and miss are two general kinds of alarm errors and they can decrease operator's trust in the alarm system. Specifically, there are two different forms of trust in such systems, represented by two kinds of responses to alarms in this research. One is compliance and the other is reliance. Besides false alarm and miss, the two responses are differentially affected by properties of the alarm system, situational factors or operator factors. However, most of the existing studies have qualitatively analyzed the relationship between a single variable and the two responses. In this research, all available experimental studies are identified through database searches using keyword "compliance and reliance" without restriction on year of publication to December 2017. Six relevant studies and fifty-two sets of key data are obtained as the data base of this research. Furthermore, neural network is adopted as a tool to establish the quantitative relationship between multiple factors and the two forms of trust, respectively. The result will be of great significance to further study the influence of human decision making on the overall fault detection rate and the false alarm rate of the human machine system.

Recently, threat of previously unknown cyber-attacks are increasing because existing security systems are not able to detect them. Past cyber-attacks had simple purposes of leaking personal information by attacking the PC or destroying the system. However, the goal of recent hacking attacks has changed from leaking information and destruction of services to attacking large-scale systems such as critical infrastructures and state agencies. In the other words, existing defence technologies to counter these attacks are based on pattern matching methods which are very limited. Because of this fact, in the event of new and previously unknown attacks, detection rate becomes very low and false negative increases. To defend against these unknown attacks, which cannot be detected with existing technology, we propose a new model based on big data analysis techniques that can extract information from a variety of sources to detect future attacks. We expect our model to be the basis of the future Advanced Persistent Threat(APT) detection and prevention system implementations.