Bio-Inspired Models of Network, Information, and Computing by Gianni A. Di Caro, Guy Theraulaz (eds.)

By Gianni A. Di Caro, Guy Theraulaz (eds.)

This booklet constitutes the completely refereed post-conference court cases of the seventh foreign convention on Bio-Inspired versions of community, info and Computing structures (Bionetics 2012), held in Lugano, Switzerland, in December 2012. The 23 revised complete papers provided have been conscientiously reviewed and chosen from forty submissions. They conceal issues corresponding to networking, robotics and neural networks, molecular scale and bioinformatics, optimization and bio-inspired modeling in quite a few fields.

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Additional info for Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers

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When the variance is large, it is enough to slightly decrease the sending rate to push down the delay to the desired level. On the contrary, aggressive rate control is required in a network with small fluctuation, which implies that a network is stable. With such a control mechanism based on the attractor perturbation model, efficient and effective rate adaptation can be accomplished without detailed information about a network system or tailored facilities. The remainder of this paper is organized as follows.

Figure 7 (right) shows how the DDoS attack can be detected by analyzing the averaged value of the Anderson–Darling statistic. There is a high peak on the plot after the attack starts. It can be seen that also the end of the attack is visible as a second peak on the plot. The DDoS attack can be easily detected when using the Reactive ASR-FA algorithm, as it implies a change in the network conditions. 7 47 50 40 30 20 10 0 0 500 1000 1500 2000 Simulation Time [s] Fig. 7. Performance comparison of different versions of the reactive ASR-FA algorithm in case of a DDoS network attack (left), the averaged Anderson-Darling statistic from node 34 to node 20 (right).

We verify whether the modified ant routing algorithm is able to quickly detect such load level changes. We compare the results obtained for Actions 1-3, described in Sect. 4, with the original ASR-FA algorithm for different values of the load level jump (Fig. 2). The value of beta alternates between 4 and 2 depending on A (Ψm = 2). Let us call the initial process of finding the suitable routing policy - the learning process, and the process of modifying the policy after the load jump - the adaptation process.

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