-------- Forwarded Message -------- Subject: [AISWorld] CFP: Special Issue of Security and Communication Networks on Characterization and Detection of Fakes, Spammers, and Bots in Social and Communication Networks Date: Wed, 31 Oct 2018 11:22:49 +0100 From: Stefano Cresci stefano.cresci@iit.cnr.it To: AISWorld@lists.aisnet.org

[apologies if you receive multiple copies]

Security and Communication Networks Special Issue on Characterization and Detection of Fakes, Spammers, and Bots in Social and Communication Networks

Deadline for submission: January 18, 2019 Publication date: June, 2019

This special issue focuses on authentication techniques, access control mechanisms, network traffic analysis, and novel detection techniques for malicious users/nodes in social/communication networks.

Online social networks, and in general, communication networks, are a crucial component in the public sphere, enhancing communications, fostering discussions, and influencing the public perception for a myriad of issues. However, social and communication networks have become the ideal stage for the proliferation of fictitious and malicious accounts, including bots and botnets. Efficient detection of such malicious nodes in social and communication networks has thus become one of the most pressing contemporary challenges. Particular emphasis should be given to the design of flexible and scalable techniques capable of dealing with different types of malicious nodes in large-scale analyses. We also encourage studies assessing the consequences of malicious actions. Finally, novel approaches to the collection and annotation of large ground-truth datasets are also welcomed.

Topics of interest include (but are not limited to) the following:

- Authentication techniques to discriminate between legitimate and malicious nodes - Access control mechanisms that prevent data disclosure hosted in social and communication networks to possible bots - Machine learning-based detection techniques for malicious nodes - Network analysis techniques for characterization and detection of malicious nodes - Online behavioral modeling for the detection of anomalous behaviors - Techniques for detecting groups of synchronized and coordinated malicious nodes - Techniques for detecting evolving/evading malicious nodes

Guest Editors:

- Maurizio Tesconi (m.tesconi@iit.cnr.it), IIT-CNR, Pisa, Italy - Stefano Cresci (s.cresci@iit.cnr.it), IIT-CNR, Pisa, Italy - Roberto Di Pietro (rdipietro@hbku.edu.qa), Hamad Bin Khalifa University, Doha, Qatar - Mueen Abdullah (mueen@unm.edu), University of New Mexico, Albuquerque, USA

More information:

https://www.hindawi.com/journals/scn/si/985904/cfp/

-- *Stefano Cresci*

Institute of Informatics and Telematics (IIT) National Research Council (CNR) Via Giuseppe Moruzzi, 1 56124 Pisa (Italy)

Phone: *+39 050 315 8272* Mobile: *+39 328 1330773* Skype: *mystic_ste* Web:* http://www.iit.cnr.it/stefano.cresci http://www.iit.cnr.it/stefano.cresci* Twitter: *@s_cresci https://twitter.com/s_cresci* _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org