White List: An Administrative Tool to Contrast Crime

White Lists are lists of firms set up at each Italian prefecture. It is a relatively young instrument born as an experimental project designed to solve problems for particular conditions of crime and corruption in the Italian territory. This work aims to understand if the “White List” tool can represent a reliable legality tool for the contracting station and representation for firms registered in these lists, an incentive to obtain awards for faster times of public procurement. Through a descriptive survey, we will try to photograph the “White List” phenomenon by comparing firms belonging to these lists with companies not belonging to them but which are similar in terms of size and economic sector. The comparison takes off some differences in the financial structure of the two groups of companies. in particular, the White List’s firms show a better profitability and a lower recourse to third-party capital.

Study of Chinese spam filtering Based on Improved Naive Bayesian Classification Algorithm

Spam is a growing threat to mobile communications. This paper puts forward some mitigation technologies, including white list and blacklist, challenge response and content-based filtering. However, none are perfect and it makes sense to use an algorithm with higher accuracy for classification. Bayesian classification method shows high accuracy in spam processing, so it has attracted extensive attention. In this paper, a Bayesian classification method based on annealing evolution algorithm is introduced into Chinese spam filtering to improve the accuracy of classification. Our simulation results show that the algorithm has better performance in spam filtering.

A Hybrid Association Rule-Based Method to Detect and Classify Botnets

Nowadays, botnet has become a threat in the area of cybersecurity, and, worse still, it is difficult to be detected in complex network environments. Thus, traffic analysis is adopted to detect the botnet since this kind of method is practical and effective; however, the false rate is very high. The reason is that normal traffic and botnet traffic are quite close to the border, making it so difficult to be recognized. In this paper, we propose an algorithm based on a hybrid association rule to detect and classify the botnets, which can calculate botnets’ boundary traffic features and receive effects in the identification between normal and botnet traffic ideally. First, after collecting the data of different botnets in a laboratory, we analyze botnets traffic features by processing a data mining on it. The suspicious botnet traffic is filtered through DNS protocol, black and white list, and real-time feature filtering methods. Second, we analyze the correlation between domain names and IP addresses. Combining with the advantages of the existing time-based detection methods, we do a global correlation analysis on the characteristics of botnets, to judge whether the detection objects can be botnets according to these indicators. Then, we calculate these parameters, including the support, trust, and membership functions for association rules, to determine which type of botnet it belongs to. Finally, we process the test by using the public dataset and it turns out that the accuracy of our algorithm is higher.

Composition and Genealogical Relation Network of the Medical Family in the Late Joseon Dynasty

Uiyeokju Palsebo (醫譯籌八世譜) is a genealogy record that contains the eighth generation of patrilineal ancestors, maternal grandfathers, and fathers-in-law of technical officials who worked in three fields: medicine, linguistics, and mathematics. This book covers members of influential families who monopolized the positions of technical officers. In that respect, it seems to have had an effect like a kind of 'white list'. This paper identifies the range of families based on common ancestors above eight generations according to the editing method of this book, attempting various statistical analysis. The results of the analysis shows that it is possible to determine the size of the medical families, which varied according to the number of medical bureaucrats and the distance of kinship between them. Most of the families had workers in the three fields of medicine, linguistics, and mathematics, but there were also ‘families more specialized in medicine’ that produced a large number of medical figures. The ancestors of medical figures were mainly engaged in the three fields of medicine, linguistics, and mathematics, but there were also a small number of officials in charge of “unhak(雲學: including astronomy, geography, and fortune-telling),” law, art, and transcription. For distant ancestors from common ancestors to the fifth generation, the proportion of technical officers was small, but for relatively close ancestors, the proportion of technical officers, especially medical officers, increased. It can be seen that the status as a medical officer tends to be hereditary further down the generations. The fields of activity of the maternal grandfathers and fathers-in-law of medical figures were more concentrated in the medical field. This can be the result of confirming the influence of the marital relationship network that was formed in the close period with the medical persons being investigated. In this paper, only medical figures were considered as primary research subjects, but their macroscopic networks were relatively evenly spread out in the three fields of medicine, linguistics, and mathematics. In this network, Uiyeokju Palsebo contained homogeneous hierarchies that could continuously dominate a specific field of government office.

Communal Fraud Detection Algorithm for Establishing Identity Thefts in Online Shopping

In recent times, e-commerce sector is gaining popularity and expressing progressive growth. Due to increasing the demand of automation process and the reach of internet towards the end-users have poised this trust. In spite of the technology advancements, the privacy and security of e-commerce merchant as well as consumer data are constantly under threat. Identity theft, which is considered as more important security problems for end-users, is addressed by one time password generated instantly. This paper focuses on communal fraud detection algorithm for protecting identity theft in online shopping by creating a white list. Experimental results have proved white lists outperform one-time passwords in identity theft in a more effective manner.

Control by infrastructure: Political ambitions meet technical implementations in RuNet

Discourse about sovereignty and Internet in Russia is predominantly focused on control of harmful content and information and its negative influence on the political regime and society. However, content control is not the only way to exercise sovereignty over digital media and the Web. Recently, the Russian government started to realize that without controlling Internet infrastructure, most strategies to filter and block Web sites and services are wasted. In the past five to seven years, Russia invested a lot of efforts in the development and adoption of new laws and regulations that deal with RuNet infrastructure, where the aim of centralized Internet traffic control was a real novelty, albeit a very ambitious political goal. This article tries to address the pitfalls of the control-by-infrastructure endeavor of the Russian government through four emblematic cases: the implementation of the “Revizor” system to control ISPs’ compliance to filter Internet resources from the blacklist; the battle to block Telegram messenger in Russia; the implementation of law FZ-90 (popularly referred to as the law “on Sovereign RuNet”); and finally, the ongoing experiment with free access to ‘socially significant Web sites’, which may have serious consequences in the future if used as a ‘white list’ of permitted Web resources. These four cases were chosen because they are deeply interconnected and show how the government has been gradually implementing infrastructure control in connection to content control.

Research and Analysis of Electromagnetic Trojan Detection Based on Deep Learning

The electromagnetic Trojan attack can break through the physical isolation to attack, and the leaked channel does not use the system network resources, which makes the traditional firewall and other intrusion detection devices unable to effectively prevent. Based on the existing research results, this paper proposes an electromagnetic Trojan detection method based on deep learning, which makes the work of electromagnetic Trojan analysis more intelligent. First, the electromagnetic wave signal is captured using software-defined radio technology, and then the signal is initially filtered in combination with a white list, a demodulated signal, and a rate of change in intensity. Secondly, the signal in the frequency domain is divided into blocks in a time-window mode, and the electromagnetic signals are represented by features such as time, information amount, and energy. Finally, the serialized signal feature vector is further extracted using the LSTM algorithm to identify the electromagnetic Trojan. This experiment uses the electromagnetic Trojan data published by Gurion University to test. And it can effectively defend electromagnetic Trojans, improve the participation of computers in electromagnetic Trojan detection, and reduce the cost of manual testing.

9. Limited Partnerships

This chapter concerns the rapidly development of the law relating to limited partnerships under the Limited Partnerships Act 1907 (as amended).. It follows the recent rapid rise in the number of such firms on both sides of the border and the recent reform of the law, including the creation of the private fund limited partnerships(PFLPs). For non PFLPs it details the rules relating to formation, the liability of a limited partner and the modifications of partnership law, including non-interference in management and financial constraints. The relationship between the general and limited partners as to provision of information and derivative actions are set out. With regard to PFLPs, the differences from non PFLPs as to registration, interference in management (the white list), capital contributions, duties of limited partners and winding up are covered. Finally, the chapter considers the proposed (2019) further reforms to counter abuse of the limited partnership form in financial frauds.