Biblio

Nowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7%; the precision is 96.6%; the recall is 96.8%; the F1-score is 96.4%, which is better than the existing detection model based on Markov chain and graph embedding detection model.

With the wide application of Internet technology in the industrial control field, industrial control networks are getting larger and larger, and the industrial data generated by industrial control systems are increasing dramatically, and the performance requirements of the acquisition and storage systems are getting higher and higher. The collection and analysis of industrial equipment work logs and industrial timing data can realize comprehensive management and continuous monitoring of industrial control system work status, as well as intrusion detection and energy efficiency analysis in terms of traffic and data. In the face of increasingly large realtime industrial data, existing log collection systems and timing data gateways, such as packet loss and other phenomena [1], can not be more complete preservation of industrial control network thermal data. The emergence of software-defined networking provides a new solution to realize massive thermal data collection in industrial control networks. This paper proposes a 10-gigabit industrial thermal data acquisition and storage scheme based on software-defined networking, which uses software-defined networking technology to solve the problem of insufficient performance of existing gateways.

In real-time video streaming, data packets are transported over the network from a transmitter to a receiver. The quality of the received video fluctuates as the network conditions change, and it can degrade substantially when there is considerable packet loss. Forward error correction (FEC) techniques can be used to recover lost packets by incorporating redundant data. Conventional FEC schemes do not work well when scalable video coding (SVC) is adopted. In this paper, we propose a novel FEC scheme that overcomes the drawbacks of these schemes by considering the reference picture structure of SVC and weighting the reference pictures more when FEC redundancy is applied. The experimental results show that the proposed FEC scheme outperforms conventional FEC schemes.

In the current network security situation, the types of network threats are complex and changeable. With the development of the Internet and the application of information technology, the general trend is opener. Important data and important business applications will face more serious security threats. However, with the development of cloud computing technology, the trend of large-scale deployment of important business applications in cloud centers has greatly increased. The development and use of software-defined networks in cloud data centers have greatly reduced the effect of traditional network security boundary protection. How to find an effective way to protect important applications in open multi-step large-scale cloud data centers is a problem we need to solve. Threat intelligence has become an important means to solve complex network attacks, realize real-time threat early warning and attack tracking because of its ability to analyze the threat intelligence data of various network attacks. Based on the research of threat intelligence, machine learning, cloud central network, SDN and other technologies, this paper proposes an active defense method of network security based on threat intelligence for super-large cloud data centers.

With the rapid development of the Internet and communication technologies, efficient management of cyberspace, safe monitoring and protection of various network assets can effectively improve the overall level of network security protection. Accurate, effective and comprehensive network asset detection is the prerequisite for effective network asset management, and it is also the basis for security monitoring and analysis. This paper proposed an artificial intelligence algorithm based scheme which accurately identify the website icon and help to determine the ownership of network assets. Through experiments based on data set collected from real network, the result demonstrate that the proposed scheme has higher accuracy and lower false alarm rate, and can effectively reduce the training cost.

The construction of power Internet of things is an important development direction of power grid enterprises in the future. Big data not only brings economic and social benefits to the power system industry, but also brings many information security problems. Therefore, in the case of accelerating the construction of ubiquitous electric Internet of things, it is urgent to standardize the data security protection in the ubiquitous electric Internet of things environment. By analyzing the characteristics of big data in power system, this paper discusses the security risks faced by big data in power system. Finally, we propose some methods of data security protection based on the defects of big data security in current power system. By building a data security intelligent management and control platform, it can automatically discover and identify the types and levels of data assets, and build a classification and grading information base of dynamic data assets. And through the detection and identification of data labels and data content characteristics, tracking the use of data flow process. So as to realize the monitoring of data security state. By protecting sensitive data against leakage based on the whole life cycle of data, the big data security of power grid informatization can be effectively guaranteed and the safety immunity of power information system can be improved.