Special Issue "Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure"
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 24521
Interests: artificial intelligence; cloud security, cyber security; information security; internet-of-things; intrusion detection; mobile and web security; network performance evaluation
Special Issues, Collections and Topics in MDPI journals
Interests: bio-medical applications of wireless sensor networks; secured communication in WSN, IoT framework; vehicular systems
Special Issues, Collections and Topics in MDPI journals
There are some systems and networks that make up the infrastructure of society. Some of these infrastructures are of utmost importance and are related to each other. If one of these is critically damaged, then they can cause huge disturbances and losses for a nation. These are known as critical infrastructures. Particularly, the security and privacy of critical infrastructures (a nation’s strategic national assets, i.e., banking and finance, communications, emergency services, energy, food chain, health, water, mass gatherings, transport, etc.), which is an essential part of our daily life, in accessing different systems, services, and applications are serious issues. However, it is challenging to achieve, as technology is changing at rapid speed and our systems are ever more complex. The explosion of multimedia data has created unprecedented opportunities and fundamental security challenges as they are not just large in volume, but also unstructured and multi-modal. Deep learning can be used to provide a robust defense mechanism for critical infrastructures. There is always the possibility of cyber-attacks against these infrastructures, which can be predicted and detected with the help of deep learning. Deep learning can be used to identify the direct and indirect connections between these infrastructures so that in case of attack, appropriate security measures can be enforced. Deep learning can also be used to identify the weaknesses present in the current security mechanisms so that the vulnerabilities can be patched before they can be exploited. Deep learning can also be used for device layers of security mechanisms that can efficiently withstand such attacks. These defense mechanisms could be of autonomous nature and thus will require almost no human intervention.
This Special Issue mainly focuses on deep learning for the safety and security of multimedia data in critical infrastructure, addressing both original algorithmic development and new applications. We are soliciting original contributions, of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in this domain. Please note that all the submitted papers must be within the general scope of the Symmetry journal.
The topics relevant to this Special Issue include but are not limited to the following:
- Security and privacy of multimedia data in telecommunication systems
- Security and privacy of multimedia data in communication systems
- Security and privacy of multimedia data in eCommerce
- Security and privacy of multimedia data in emergency services, energy, food chain
- Security, privacy and forensics of multimedia data in critical infrastructure
- Security and privacy of multimedia data in mobile cloud computing
- Security and privacy management of data in cloud computing
- Security and privacy of Industrial control systems
- Mobile cloud computing intrusion detection systems
- Cryptography, authentication, authorization, and usage control for data in cloud
- Security and privacy of multimedia data in smartphone devices
- Security of mobile, peer-to-peer and pervasive services in clouds
- Security of data in mobile commerce and mobile internet of things
- Security and privacy of multimedia data in sensor networks
- Big data-enabling social networks on clouds
- Resource management for multimedia data on clouds
- Cryptography, authentication, and authorization for data in mobile devices
- Security and privacy of multimedia data in web service
- Evolutionary algorithms for mining social networks for decision support
Artificial neural network and neural system applied to social media and mitigating the privacy risks in critical infrastructure
Dr. Brij Gupta
Prof. Dr. Dharma P. Agrawal
Dr. Deepak Gupta
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.