Nowadays, edge computing has become part of the Internet of Things (IoT) that plays a vital role in developing smart applications. As the usage of IoT devices significantly increases, at the same time, network edge infrastructure faces several security challenges. Distributed Denial-of-Service (DDoS) attack is one of the most severe threats to edge-cloud services. Therefore, designing a robust mitigating system is unavoidable for the network edge, and it must be able to recognize emerging attacks. This work proposes an anomaly-based DDoS detection approach that combines information-theoretic metrics and multivariate correlation analysis. The information-theoretic metric captures the randomness and complex nature of traffic behaviour. Similarly, multivariate correlation analysis identifies the relationship among traffic features. Combining information metrics and correlation analysis, we generate normal and attack traffic profiles for the training base to estimate density. The generated profiles build on the metrics including Triangle Area Mapping (TAM) with correlation analysis, Renyi’s divergence, covariance, mean, and standard deviation, which enhances the detection performance of the proposed approach. The effectiveness of the proposed approach is evaluated using testbed and benchmark datasets. The results show that the proposed approach achieves 0.17% and 2.32%, and 0.50% higher accuracy compared to the baseline approaches on the testbed, UNSW and CIC-DDoS datasets, respectively.

We propose a 6D pose estimation method for an object from a single RGB image for a robotic grasping task. Many approaches estimate pose parameters from images taken from other viewpoints and use deep learning to achieve high accuracy. However, most of these methods are not robust to changes in object texture, and there is a possibility that the correct pose cannot be estimated by only one-time inference. Our aims are to reduce the number of failure cases and improve the accuracy by a novel architecture using the iterative backpropagation of a pose decoder network and pose estimation on intermediate representation. The error between random and target pose parameters are backpropagated to a neural network and the gradient for approaching the target pose is obtained. The pose parameter is updated using the obtained gradient, the error is calculated again, and backpropagation is re-performed. Repeating this process, we estimate a more accurate pose. Experiments using our own dataset show that estimation accuracy is improved and the number of failure cases is reduced. Furthermore, estimation by coarse-to-fine iterative processing is more accurate and faster. We also experiment with grasping using a UR5 robot and show that the robot can grasp objects without depth information when using the pose estimated by the proposed method.

This paper presents the multi-task Deconvolutional Single Shot Detector (MT-DSSD), which runs three tasks-object detection, semantic object segmentation, and grasping detection for a suction cup-in a single network based on the DSSD. Simultaneous execution of object detection and segmentation by multi-task learning improves the accuracy of these two tasks. Additionally, the model detects grasping points and performs the three recognition tasks necessary for robot manipulation. The proposed model can perform fast inference, which reduces the time required for grasping operation. Evaluations using the Amazon Robotics Challenge (ARC) dataset showed that our model has better object detection and segmentation performance than comparable methods, and robotic experiments for grasping show that our model can detect the appropriate grasping point.

When robots engage in bin picking of multiple objects, a failure in grasping partially occluded objects may occur because other objects may overlap the desired ones. Therefore, the layered structure of objects needs to be detected, and the picking order needs to be established. In this paper, we propose a new dataset that evaluates not only the area of objects but also the layered structures of objects. In this dataset, three tasks are targeted: object detection, semantic segmentation, and segmentation of occluded areas for bin picking of multiple objects. The dataset, called “the Amazon Robotics Challenge (ARC) Multi-task Dataset” contains 1,500 RGB images and depth images, including all scenes containing bounding box labels, semantic segmentation labels, and occluded area labels. This enables representing the layered structure of overlapped objects with a tree structure. A benchmark of the ARC multi-task dataset demonstrated that occluded areas could be segmented using a Mask regional convolutional neural network (R-CNN) and that layered structures of objects could be predicted. Our dataset is available at the following URL:http://mprg.jp/research/arc_dataset_2017_e.