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New advances in securing cyberspace and curbing crowdturfing
Author(s) -
Li Gang,
Niu Wenjia,
Batten Lynn,
Liu Jiqiang
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4162
Subject(s) - gossip , popularity , competitor analysis , cyberspace , internet privacy , exploit , the internet , social media , curiosity , task (project management) , newspaper , product (mathematics) , computer science , advertising , business , world wide web , computer security , marketing , engineering , political science , psychology , social psychology , geometry , systems engineering , mathematics , law
Cyberspace is reshaping the way businesses manage their sales and marketing assets. Unlike traditional media, such as TV, radio or newspapers, social media is characterized by freely available user-generated content. In addition to gaining phenomenal popularity as the web becomes accessible via all sorts of devices, they also have a strong influence on brands, making social media a force that many businesses can no longer ignore. However, among these network applications and services, it has been reported that some public relations companies hired people to post product comments on different online communities and social networks, without even consuming the services or products. While online paid posters can be used as an efficient e-marketing strategy, they can also act maliciously by spreading gossip or negative information about competitors. More specifically, a group of paid posters could operate with well-coordinated attacks and generate a desired result of positive or negative opinions, to attract attention or trigger curiosity. This kind of suspicious online behavior is known as ‘crowdturfing’ or ‘astroturfing’, which may mislead the public perception and bring a negative effect on both the Internet users and society. Technically, crowdturfing is closely related with other known suspicious online behaviors: spam email, fake review, social spam and link farming. Therefore, how to exploit the characteristics and behavior patterns of crowdturfing from the massive user information and user-generated content has become a challenging task. The research field is very extensive, and as a result, recent years have witnessed increasing research attentions on curbing crowdturfing in social networks like contentbased method, behavior-based methods, social relation-based methods and applications and open case study. This trend raised the need for launching this special issue, and we solicit the recent theoretical and application output in curbing crowdturfing. Based on the invited extension of the best work in the International Conference on Applications and Techniques in Information Security and an open call, four submissions have been accepted to best illustrate the recent directions and perspectives. The papers in this issue report a variety of methods in securing cyberspace and curbing crowdturfing and mainly involve the following aspects: the security algorithm optimization, the encryption scheme, abnormal behavior discovering and ciphertext update. These work proposed more security and efficiency schemes in various kinds of crowdturfing detection, traffic classification and monitoring systems, meanwhile against selective identity adaptively chosen-message attacks. Here, we provide an integrative perspective of this special issue, by summarizing each contribution contained therein. In [1], Tang et al. proposed an optimization algorithm to improve the efficiency in traffic classification system, network monitoring system and network intrusion detection system (NIDS). More specifically, they introduced a character escaping and replacing scheme to decrease the size of DFA’s character set and to reduce DFA’s space requirement through optimization. Then based on transition rewriting, a space-efficient and time-efficient DFA presentation, Reduced Input Character Set DFA (RICS-DFA), is proposed together with an efficient constructing algorithm. For real rule sets, ICS-DFA reduces the memory consumption by 68–92%, compared with the original DFA, and this paper designed a scalable RICS-DFA matching engine on FPGA platform on which the reduced state transition matrix is mapped to on-chip memories. The throughput of executing deep packet inspection for real world rule sets can achieve 7–50.5 Gbps. In [2], Yan et al. proposed a pre-image sample algorithm with outputs obeying irregular Gaussian distribution and then designed the construction of hierarchical identity-based signature. Two measures were adopted to prevent the leakage of the geometrical property of trapdoor caused by irregular Gaussian outputs. Theoretically, the authors showed that this identity-based signature is more efficient and unforgeable against selective identity adaptively chosen-message attacks.