Data Mining and the Future of Cybersecurity
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 735
Special Issue Editors
Interests: social network and social inter-networking analysis; privacy, security, trust, and reputation; intelligent agents; IoT
Special Issues, Collections and Topics in MDPI journals
Interests: trust and reputation systems; Internet of Things; distributed artificial intelligence; artificial neural network; multiagent systems
Special Issues, Collections and Topics in MDPI journals
Interests: agent-based computing; artificial neural networks; distributed computing; electronic commerce; intelligent transportation systems; Internet of Things (IoT); recommender systems; trust and reputation systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computer and communication systems are subject to repeated security attacks. Given the variety of new vulnerabilities discovered every day, the introduction of new attack schemes, and the ever-expanding use of the Internet, it is not surprising that the field of computer and network security has grown and evolved significantly in recent years. Attacks are so pervasive nowadays that many firms, especially large financial institutions, spend over 10% of their total information and communication technology budget directly on computer and network security. Changes in the type of attacks and the identification of new vulnerabilities have resulted in a highly dynamic threat landscape that is unamenable to traditional security approaches.
Data mining techniques that explore data in order to discover hidden patterns and develop predictive models have proven to be effective in tackling the aforementioned information security challenges. In recent years, classification, anomaly detection, and temporal analysis, among other techniques, have all been used to discover and generalize attack patterns in order to develop powerful solutions for coping with the latest threats.
The articles presented in this Special Issue are quite representative of the field of data mining applied to cybersecurity—both in terms of the tasks and domains that they consider and in terms of the solutions that they propose. Specifically, the tasks represented in this issue include user authentication through biometrics, SCADA systems vulnerability assessment, user action identification in IoT encrypted traffic, and network anomaly and intrusion detection in large computer networks as well as in small ones such as car controller networks. In order to address all the issues surveyed in this volume, a plethora of approaches are presented, including ensemble methods, one-class classification methods, text mining, transfer learning, data stream mining, and temporal analyses via neural networks. The principal problems tackled by these techniques are problems of reliability, the need to function in different environments, or adaptability to dynamic conditions either due to natural changes to the systems or to adversarial settings.
Dr. Lidia Fotia
Prof. Dr. Domenico Rosaci
Prof. Dr. Giuseppe Maria Luigi Sarnè
Prof. Dr. Fabrizio Messina
Guest Editors
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