Biblio

{Surrogate model-based optimization algorithm remains as an important solution to expensive black-box function optimization. The introduction of ensemble model enables the algorithm to automatically choose a proper model integration mode and adapt to various parameter spaces when dealing with different problems. However, this also significantly increases the computational burden of the algorithm. On the other hand, utilizing parallel computing resources and improving efficiency of black-box function optimization also require combination with surrogate optimization algorithm in order to design and realize an efficient parallel parameter space sampling mechanism. This paper makes use of parallel computing technology to speed up the weight updating related computation for the ensemble model based on Dempster-Shafer theory, and combines it with stochastic response surface method to develop a novel parallel sampling mechanism for asynchronous parameter optimization. Furthermore, it designs and implements corresponding parallel computing framework and applies the developed algorithm to quantitative trading strategy tuning in financial market. It is verified that the algorithm is both feasible and effective in actual application. The experiment demonstrates that with guarantee of optimizing performance, the parallel optimization algorithm can achieve excellent accelerating effect.

With the rapid development of the smart grid, a large number of intelligent sensors and meters have been introduced in distribution network, which will inevitably increase the integration of physical networks and cyber networks, and bring potential security threats to the operating system. In this paper, the functions of the information system on distribution network are described when cyber attacks appear at the intelligent electronic devices (lED) or at the distribution main station. The effect analysis of the distribution network under normal operating condition or in the fault recovery process is carried out, and the reliability assessment model of the distribution network considering cyber attacks is constructed. Finally, the IEEE-33-bus distribution system is taken as a test system to presented the evaluation process based on the proposed model.

Distributed data aggregation via summation (counting) helped us to learn the insights behind the raw data. However, such computing suffered from a high privacy risk of malicious collusion attacks. That is, the colluding adversaries infer a victim's privacy from the gaps between the aggregation outputs and their source data. Among the solutions against such collusion attacks, Distributed Differential Privacy (DDP) shows a significant effect of privacy preservation. Specifically, a DDP scheme guarantees the global differential privacy (the presence or absence of any data curator barely impacts the aggregation outputs) by ensuring local differential privacy at the end of each data curator. To guarantee an overall privacy performance of a distributed data aggregation system against malicious collusion attacks, part of the existing work on such DDP scheme aim to provide an estimated lower bound of privacy budget for the global differential privacy. However, there are two main problems: low data utility from using a large global function sensitivity; unknown privacy guarantee when the aggregation sensitivity of the whole system is less than the sum of the data curator's aggregation sensitivity. To address these problems while ensuring distributed differential privacy, we provide a new lower bound of privacy budget, which works with an unconditional aggregation sensitivity of the whole distributed system. Moreover, we study the performance of our privacy bound in different scenarios of data updates. Both theoretical and experimental evaluations show that our privacy bound offers better global privacy performance than the existing work.

Vulnerabilities of controller that is caused by separation of control and forwarding lead to a threat which attacker can take remote access detection in SDN. The current work proposes a controller architecture called secure control plane (SCP) that enhances security and increase the difficulty of the attack through a rotation of heterogeneous and multiple controllers. Specifically, a dynamic-scheduling method based on Bayesian Stackelberg Games is put forward to maximize security reward of defender during each migration. Secondly, introducing a self-cleaning mechanism combined with game strategy aims at improving the secure level and form a closed-loop defense mechanism; Finally, the experiments described quantitatively defender will get more secure gain based on the game strategy compared with traditional strategy (pure and random strategies), and the self-cleaning mechanism can make the control plane to be in a higher level of security.

Traditional Anti-virus technology is primarily based on static analysis and dynamic monitoring. However, both technologies are heavily depended on application files, which increase the risk of being attacked, wasting of time and network bandwidth. In this study, we propose a new graph-based method, through which we can preliminary detect malicious URL without application file. First, the relationship between URLs can be found through the relationship between people and URLs. Then the association rules can be mined with confidence of each frequent URLs. Secondly, the networks of URLs was built through the association rules. When the networks of URLs were finished, we clustered the date with modularity to detect communities and every community represents different types of URLs. We suppose that a URL has association with one community, then the URL is malicious probably. In our experiments, we successfully captured 82 % of malicious samples, getting a higher capture than using traditional methods.