Statistical Models for the Characterization, Identification, and Mitigation of Distributed Attacks in Data Networks (2018)
Abstract / truncated to 115 words
The thesis focuses on statistical approaches to model, mitigate, and prevent distributed network attacks. When dealing with distributed network attacks (and, more in general, with cyber-security problems), three fundamental phases/issues emerge distinctly. The first issue concerns the threat propagation across the network, which entails an "avalanche" effect, with the number of infected nodes increasing exponentially as time elapses. The second issue regards the design of proper mitigation strategies (e.g., threat detection, attacker's identification) aimed at containing the propagation phenomenon. Finally (and this is the third issue), it is also desirable to act on the system infrastructure to grant a conservative design by adding some controlled degree of redundancy, in order to face those cases where ...
cybersecurity – statistical models of network attacks – ddos – availability of network infrastructures
Information
- Author
- Di Mauro, Mario
- Institution
- University of Salerno
- Supervisor
- Publication Year
- 2018
- Upload Date
- Oct. 3, 2018
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