Submissions Due: 20 January, 2023
Publication: September 2023
Digital protection is a set of rules and innovations designed to protect our frameworks, organization, and information from unapproved access, assaults, and undesirable interferes. In recent years, several market leaders in the IT industry have begun to use data mining techniques for malware detection. They intend to keep up with the secrecy, respectability, and accessibility of data and the executive's frameworks through different digital safeguard frameworks. Because of the accessibility of a large amount of information in digital framework and expanding number of digital hoodlums, there is a need for abilities to address network safety.
Internet technology has made the industry more hidden in various ways; in any case, this has disconnected separated us by implications that have traditionally never been sincerely adaptive, thus testing. As rapidly as stability was advanced, there was the cyberattack's image. A variety of data affirmation obligations are required about errands, information, and resources from ongoing arising assaults.
The use of security risk can prevent a lot of digitally enhanced attacks, privacy invasion, and documentation misunderstandings while still helping the panel's threat. Whenever the organization grows an interpretation of coalition vigilance and a feasible contingency plan, it is easier to avoid and authenticate those very threats. A digital security detective promotes the security of the firm's paradigms and entities by planning and implementing our security measures. They provide troublesome responses to prevent relevant data from becoming taken, affected, or damaged. Moreover, tracking data exploitation investigates at least three linear models or repercussions. Communication data mining methods should be effective in combating these modifications. The use of evidence, mining algorithms have resulted in the establishment of a horrific quantity of knowledge that is far too large to purchase. Information is now becoming faster than our ability to process and sell actual data. The statistics must be limited as quickly as necessary to reach suitable data storage dimensions. Once knowledge or large amounts of data are recognized as the new daily investment of the era, the relevance of knowledge discovery and going to agree on techniques keeps increasing. Its fierce progression of effective methods us both to collect data hugely evaluated and sophisticated volumes of information. This special issue investigates the operability of data mining for cyber-physical systems and complex, time-evolving networks. Researchers are requested to describe emerging developments and advances in adopting time-evolving networks. Furthermore, there is a need to explore performance, metrics and challenges in data mining for cyber-physical systems and complex, time-evolving networks.
Topics of interest include (but are not limited to):
For author information and guidelines on submission criteria, please visit the TBD's Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Email the guest editor at clindut@ieee.org
Guest Editors:
