Earticle

The controller in the traditional chord-based botnets needs to maintain the periodic communication among all the adjacent peerbots so that every peerbot could efficiently master the new routing information. Once some peerbot is captured by the anti-virus software and is treated as a honeypot, the security personnel can capture more peerbots by tracking the deployed honeypot when it updates the routing table so that the robustness of botnet would be affected. To solve the above-mentioned problem, the trusted chord-based quantum botnet is proposed that utilizes the quantum secure channel to achieve the updating of routing table, and introduces the anti-tracking network for capturing the deployed honeypots. The anlysis result showed that the robustness and stability of trusted chord-based quantum botnet could efficiently be improved.

With the development of social economy, the public cultural service is the main service platform for the whole society, which not considers profit as the purpose. However, as the characteristics of the China’s government function and the financial investment, the government tends to pursue the maximization of the scale, then how to scientifically evaluate the performance of public cultural services has become a hot issue. In this paper, the authors construct the performance evaluation index of public cultural service based on web survey, and make empirical analysis by using DEA method. The result shows that the overall efficiency of national museum is moderate; the efficiency level is 0.87. At the same time, the overall efficiency of the national public library is low; the efficiency level is 0.746. On this basis, we put forward relevant policy recommendations

Automatic variable key is a technique to generate time variant key. In this paper, we have proposed a new key generation technique. The proposed technique provides higher level of security compared to that provided by other key generating schemes. The newly generated keys in the technique are more random.

Performance management is core part of enterprise human resources management, for any organization or enterprise a good, the secret to its success lies in the effective performance management. Through the enterprise performance management, it can motivate employees, improve employee satisfaction, will help the staff's personal goals combined with enterprise strategy, realize the goal of the enterprise and the balance of personal development, promote enterprise realize sustainable development. In this paper, we conduct research on enterprise performance management information system development and the robustness optimization based on data regression analysis and mathematical optimization theory. We firstly review the modern regression analysis approaches for large-scale data with the introduction of our proposed revised regression model for enterprises. Later, we summarize the state-of-the-art mathematical optimization models to select the most feasible one to serve as the tool for our system. Then, we analyze the performance of the current existing performance management information system and adopt the prior knowledge to optimize the system. The experimental result proves the feasibility and effectiveness of our system. The performance evaluation accuracy is enhanced from 86.7% to 93.5% and the system robustness is enhanced as well.

Recent studies have shown that ‘’hacktivists’’ can mount serious attacks on automobiles. The automotive On-Board Diagnostic (OBD) interface enables an efficient way to access information of the in-vehicle electronic system and leaves way for unauthorized access by an intruder. We discover that remote exploitation is feasible via a broad range of attack points such as mechanic tools, CD players, Bluetooth and Tire Pressure Monitoring System. Wireless communications channels allow long distance vehicle control. Finally, the paper discusses the drawbacks of Seed-Key Mechanism to authenticate and provide an extra layer of authentication to help build a safer automotive ecosystem.

To detect various network attacks in real time, this paper developed a network intrusion detection system based on artificial neural network. This paper first introduced the recent development of neural network, BP algorithm and structure of a simple perceptron. Then, this paper developed an improved BP neural network algorithm to detect anomaly network traffic with adjusted correlation rules. Finally, the network intrusion system in this paper was tested in a real network situation; the improved BP algorithm neural network with adjusted correlation rules shows a reduction in total error and increment in alarm rate compared to the traditional basic BP algorithm model.