We discuss distributed denial of service attacks in the Internet. We were motivated by the widely known February 2000 distributed attacks on Yahoo!, Amazon.com, CNN.com, and other major Web sites. A denial of service is characterized by an explicit attempt by an attacker to prevent legitimate users from using resources. An attacker may attempt to: "flood" a network and thus reduce a legitimate user's bandwidth, prevent access to a service, or disrupt service to a specific system or a user. We describe methods and techniques used in denial of service attacks, and we list possible defenses. In our study, we simulate a distributed denial of service attack using ns-2 network simulator. We examine how various queuing algorithms implemented in a network router perform during an attack, and whether legitimate users can obtain desired bandwidth. We find that under persistent denial of service attacks, class based queuing algorithms can guarantee bandwidth for certain classes of input flows.

Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce a new dataset, Natural Adversarial Objects (NAO), to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence. The mean average precision (mAP) of EfficientDet-D7 drops 74.5% when evaluated on NAO compared to the standard MSCOCO validation set.Moreover, by comparing a variety of object detection architectures, we find that better performance on MSCOCO validation set does not necessarily translate to better performance on NAO, suggesting that robustness cannot be simply achieved by training a more accurate model.We further investigate why examples in NAO are difficult to detect and classify. Experiments of shuffling image patches reveal that models are overly sensitive to local texture. Additionally, using integrated gradients and background replacement, we find that the detection model is reliant on pixel information within the bounding box, and insensitive to the background context when predicting class labels. NAO can be downloaded here.

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be described as follows:It has been found that a bottleneck RED gateway can become oscillatory when regulating a number of TCP flows. We develop a method to qualitatively describe the characteristic frequency and intermittency of the oscillation in the RED gateway. The results have been verified with the ns simulator.