Non-Parametric Early Warning Signals from Volumetric DDoS Attacks.

Distributed Denial of Service (DDoS) is a classic type of Cybercrime and can still strongly damage company reputation and increase costs. Attackers have continuously improved their strategies, and the amount of unleashed communication requests has doubled in volume, size and frequency. This has occurred through different isolated hosts, leading them to resource exhaustion. Previous studies have concentrated efforts in detecting or mitigating ongoing DDoS attacks. However, addressing DDoS when it is already in place may be too late. In this article, we attract the attention for the crucial role and importance of the early prediction of attack trends in order to support network resilience. We suggest the use of statistical and non-parametric leading indicators for identifying trends of volumetric DDoS attacks and we report promising results over real dataset from CAIDA.

[1]  Ebrahim A. Gharavol,et al.  A Novel DoS and DDoS Attacks Detection Algorithm Using ARIMA Time Series Model and Chaotic System in Computer Networks , 2016, IEEE Communications Letters.

[2]  Reza Ebrahimi Atani,et al.  A survey of IT early warning systems: architectures, challenges, and solutions , 2016, Secur. Commun. Networks.

[3]  Kotagiri Ramamohanarao,et al.  Survey of network-based defense mechanisms countering the DoS and DDoS problems , 2007, CSUR.

[4]  Riping Wang.  First-principles prediction of ferroelastic phase transition in AlPO4 , 2013 .

[5]  Xiaoping Li,et al.  TrustR: An Integrated Router Security Framework for Protecting Computer Networks , 2016, IEEE Communications Letters.

[6]  Vyas Sekar,et al.  Bohatei: Flexible and Elastic DDoS Defense , 2015, USENIX Security Symposium.

[7]  Saman Taghavi Zargar,et al.  A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks , 2013, IEEE Communications Surveys & Tutorials.

[8]  Kihong Park,et al.  The Internet as a Large-Scale Complex System , 2005, Santa Fe Institute Studies in the Sciences of Complexity.

[9]  Jun Ho Huh,et al.  Hive oversight for network intrusion early warning using DIAMoND: a bee-inspired method for fully distributed cyber defense , 2016, IEEE Communications Magazine.

[10]  A. Bovier.  Metastability: A Potential-Theoretic Approach , 2016 .

[11]  Maurizio Aiello,et al.  Are mobile botnets a possible threat? The case of SlowBot Net , 2016, Comput. Secur..

[12]  Wei Chen,et al.  A novel approach to detecting DDoS Attacks at an Early Stage , 2006, The Journal of Supercomputing.

[13]  José M. F. Moura,et al.  A Stochastic Adaptive Model to Explore Mobile Botnet Dynamics , 2017, IEEE Communications Letters.

[14]  Steve Mansfield-Devine,et al.  The growth and evolution of DDoS , 2015, Netw. Secur..

[15]  Allen Y. Chang,et al.  Early Warning System for DDoS Attacking Based on Multilayer Deployment of Time Delay Neural Network , 2010, 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.