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An Automated Network Intrusion Detection and Identification System by Synergizing AI Techniques

An Automated Network Intrusion Detection and Identification ...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international Conference on

作者： Md Rishadul Bayesh Md Sakhawat Hossain Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Currently, cybercrimes are a serious threat to computer networks. Researchers have come to view Network Intrusion Detection Systems as a critical defensive component and have worked to develop new methods to detect hostile network activity. However, unknown attacks are difficult to detect on a zero-day basis. Previous studies addressed detection or identification techniques using binary classifications of attacks. In this paper, we introduce a system for the detection and identification of network intrusions that combines the strengths of the misuse and anomaly detection techniques. Our approach integrates a Gaussian Mixture Model, identify attacks resembling normal distributions enhances early detection capability, for anomaly detection at zero day and supervised models for identification of known attacks. After that, we evaluated our system using the CICIDS2017 and CICIDS2018 datasets, focusing on unsupervised learning for initial attack detection and supervised learning for multiclass classification. Our empirical findings highlight superior performance in terms of accuracy and F1 score, underscoring the effectiveness of our proposed system.

关键词： computational modeling Supervised learning Network intrusion detection Denial-of-service attack Data models Robustness Safety Anomaly detection Unsupervised learning Tuning

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Co-Design Optimisation of Morphing Topology and Control of Winged Drones

Co-Design Optimisation of Morphing Topology and Control of W...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Fabio Bergonti Gabriele Nava Valentin Wüest Antonello Paolino Giuseppe L’Erario Daniele Pucci Dario Floreano Artificial and Mechanical Intelligence Laboratory Istituto Italiano di Tecnologia (IIT) School of Computer Science University of Manchester Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne (EPFL) Department of Industrial Engineering University of Naples Federico II

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone’s conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimisation to a multi-body winged drone with trajectory optimisation to solve the motion intelligence problem under diverse flight mission requirements, such as energy consumption and mission completion time. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and mission time, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.

关键词： Energy consumption Costs computational modeling Process control Energy efficiency Topology Trajectory

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Inertial sensor-based movement classification with dimension reduction based on feature aggregation

Inertial sensor-based movement classification with dimension...

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international symposium on computational intelligence and Informatics

作者： Peter Sarcevic József Sárosi Dominik Csík Ákos Odry Department of Mechatronics and Automation University of Szeged Szeged Hungary

ISBN: (纸本)9798350398830

In this paper, a novel feature aggregation method is proposed for dimension reduction in inertial sensor-based pattern recognition applications. The method is utilized in a movement classification algorithm, which applies measurements of wearable sensor units attached to the wrists of the subjects. The feature aggregation is realized separately for each feature type by using a linear combination of the feature values extracted for each sensor axis. The weights are computed using genetic algorithm-based optimization, which uses the classification efficiency provided by the k-nearest neighbor (kNN) method to compute the fitness value. After the weights are determined for all feature types and the aggregated inputs are computed for both the training and validation data, a multi-layer perceptron (MLP) classifier is trained, which can be used during real-time classification. The algorithm was tested with multiple datasets based on features extracted using different processing window sizes and used sensor combinations. The achieved results show that the proposed weighted aggregation provides significantly, mostly 5-6%, higher recognition rates on validation data than when equal coefficients are utilized during aggregation. The method in the case of some datasets with the same configuration gives even higher classification efficiencies than when using the features extracted for the sensor axes separately.

关键词： Dimensionality reduction Training Wrist Feature extraction Robot sensing systems Pattern recognition Classification algorithms

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Toward Robust and Efficient Online Adaptation for Deep Stereo Depth Estimation

Toward Robust and Efficient Online Adaptation for Deep Stere...

引用

IEEE international Conference on robotics and automation (ICRA)

作者： Knowles, Milo Peretroukhin, Valentin Greene, W. Nicholas Roy, Nicholas MIT Comp Sci & Artificial Intelligence Lab CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728190778

Although deep neural networks have achieved state-of-the-art performance for stereo depth estimation, they can suffer from a significant drop in accuracy when tested on images from novel domains. Recent work has shown that self-supervised online adaptation is a promising approach for closing this performance gap. In this work, we address three unsolved challenges for online adaptation. First, we propose a method for detecting novel environments, allowing us to trigger adaptation and notify downstream systems that depth predictions are unreliable. We find that the feature similarity scores from our deep stereo network can be leveraged for out-of-distribution (OOD) detection, providing the necessary starting criterion for adaptation. Next, we use online validation to terminate adaptation when it stops improving performance, allowing us to free up computational resources. Finally, we demonstrate that existing methods for continuous adaptation cause catastrophic forgetting of the training domain. By augmenting adaptation with experience replay, we retain high accuracy in the training domain while rapidly improving performance in novel environments. In sum, these three contributions form the basis of a more robust and efficient deep stereo system that can recognize and adapt to new environments without forgetting.

关键词： Training Deep learning automation Conferences Estimation Feature extraction

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Bidirectional Attention Network for Monocular Depth Estimation

Bidirectional Attention Network for Monocular Depth Estimati...

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IEEE international Conference on robotics and automation (ICRA)

作者： Aich, Shubhra Vianney, Jean Marie Uwabeza Islam, Md Amirul Kaur, Mannat Liu, Bingbing Huawei Technol Noahs Ark Lab Markham ON L3R 5Y1 Canada

ISBN: (纸本)9781728190778

In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks. The structure of this mechanism derives from a strong conceptual foundation of neural machine translation, and presents a light-weight mechanism for adaptive control of computation similar to the dynamic nature of recurrent neural networks. We introduce bidirectional attention modules that utilize the feed-forward feature maps and incorporate the global context to filter out ambiguity. Extensive experiments reveal the high degree of capability of this bidirectional attention model over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets - KITTI and DIODE. We show that our proposed approach either outperforms or performs at least on a par with the state-of-the-art monocular depth estimation methods with less memory and computational complexity.

关键词： Recurrent neural networks Conferences computational modeling Memory management Estimation Machine translation Convolutional neural networks

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A Comparative Study of Model-Based Control Schemes for Contouring Accuracy Improvement of 3PRR Planar Parallel-Kinematics Manipulator

A Comparative Study of Model-Based Control Schemes for Conto...

引用

robotics, automation and Artificial intelligence (RAAI), IEEE international Conference on

作者： Abdur Rosyid Bashar El-Khasawneh Mechanical and Nuclear Engineering Department Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

The highly nonlinear coupled dynamics in a parallel-kinematics manipulator typically cannot be simply treated as disturbance. Model-based control schemes are usually used to address this issue. This paper presents the improvement of contouring accuracy of a planar 3PRR parallel-kinematics manipulator by applying two model-based control schemes namely decentralized PID control with centralized computed torque feedforward and centralized inverse dynamics control with feedforward. The simulation shows that both control schemes provide better contouring accuracy than decentralized PID control scheme. Furthermore, it is shown that the decentralized PID control with centralized computed torque feedforward outperforms the centralized inverse dynamics control with feedforward given an accurate dynamic model of the manipulator.

关键词： Accuracy PI control Torque automation computational modeling Hardware PD control

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B-splines for Purely Vision-based Localization and Mapping on Non-holonomic Ground Vehicles

B-splines for Purely Vision-based Localization and Mapping o...

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IEEE international Conference on robotics and automation (ICRA)

作者： Huang, Kun Wang, Yifu Kneip, Laurent ShanghaiTech Univ Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781728190778

Purely vision-based localization and mapping is a cost-effective and thus attractive solution to localization and mapping on smart ground vehicles. However, the accuracy and especially robustness of vision-only solutions remain rivalled by more expensive, lidar-based multi-sensor alternatives. We show that a significant increase in robustness can be achieved if taking non-holonomic kinematic constraints on the vehicle motion into account. Rather than using approximate planar motion models or simple, pair-wise regularization terms, we demonstrate the use of B-splines for an exact imposition of smooth, non-holonomic trajectories inside the 6 DoF bundle adjustment. We introduce both hard and soft formulations and compare their computational efficiency and accuracy against traditional solutions. Through results on both simulated and real data, we demonstrate a significant improvement in robustness and accuracy in degrading visual conditions.

关键词： Location awareness Visualization Conferences computational modeling Kinematics Robustness Land vehicles

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Semi-Infinite Programming with Complementarity Constraints for Pose Optimization with Pervasive Contact

Semi-Infinite Programming with Complementarity Constraints f...

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IEEE international Conference on robotics and automation (ICRA)

作者： Zhang, Mengchao Hauser, Kris Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Dept Comp Sci Urbana IL USA

ISBN: (纸本)9781728190778

This paper presents a novel computational model to address the problem that contact is an infinite phenomena involving continuous regions of interaction. The problem is cast as a semi-infinite program with complementarity constraints (SIPCC). Rather than pre-discretize contacting surfaces into a finite number of contact points, we use semi-infinite programming (SIP) techniques that operate on the underlying continuous geometry, but dynamically determine a finite number of constraints that are most relevant to solving the problem. Then we solve the series of problems whose solutions converge toward one that contains a true optimum of the original SIPCC. We apply the model to a grasping pose optimization problem for a gripper and a humanoid robot, and our model enables the robots to find a feasible pose to hold (non-)convex objects while ensuring force and torque balance.

关键词： Geometry Torque Conferences computational modeling Force Humanoid robots Grasping

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Efficient Real-Time Inference in Temporal Convolution Networks

Efficient Real-Time Inference in Temporal Convolution Networ...

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IEEE international Conference on robotics and automation (ICRA)

作者： Khandelwal, Piyush MacGlashan, James Wurman, Peter Stone, Peter Sony Corp Amer Sony AI 25 Madison Ave New York NY 10010 USA Univ Texas Austin Dept Comp Sci 2317 Speedway Austin TX 78712 USA

ISBN: (纸本)9781728190778

It has been recently demonstrated that Temporal Convolution Networks (TCNs) provide state-of-the-art results in many problem domains where the input data is a time-series. TCNs typically incorporate information from a long history of inputs (the receptive field) into a single output using many convolution layers. Real-time inference using a trained TCN can be challenging on devices with limited compute and memory, especially if the receptive field is large. This paper introduces the RT-TCN algorithm that reuses the output of prior convolution operations to minimize the computational requirements and persistent memory footprint of a TCN during real-time inference. We also show that when a TCN is trained using time slices of the input time-series, it can be executed in real-time continually using RT-TCN. In addition, we provide TCN architecture guidelines that ensure that real-time inference can be performed within memory and computational constraints.

关键词： automation Convolution Conferences Memory management Network architecture Real-time systems Inference algorithms

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A Parametric and Generative Based Panels Design Optimization for Mass Timber Panel Manufacturing

A Parametric and Generative Based Panels Design Optimization...

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international Conference on robotics and Artificial intelligence (ICRAI)

作者： Afia Rasool Qipei Mei Rafiq Ahmad Department of Mechanical Engineering Smart & Sustainable Manufacturing Systems Laboratory (SMART Lab) University of Alberta Edmonton Canada Department of Civil and Environmental Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

With the development of Construction 4.0 and digital technologies, off-site construction has evolved and become a practical choice for rapid construction. In today's fast-paced manufacturing framework, there is a need to automate every construction step, especially the design and analysis phases of mass timber panel manufacturing, as these two steps are highly expert's dependent. This study summarizes a case to design multiple variants of mass timber panels using parametric and generative modeling. Integrating these technologies helps create speedy and tailored design solutions in the automation setting. A building information modeling (BIM) based on a 3D parametric design of a timber panel is generated, followed by optimizations via generative design. Focusing on the Canadian construction industry, different types of mass timber are examined to analyze and optimize design features such as stud width, stud bays, and weight-bearing capacity. This study aims to support customization and adaptability at the yield of less time and effort for ongoing industrial automation.

关键词： Solid modeling automation Three-dimensional displays Service robots computational modeling Buildings Reliability theory Reliability engineering Manufacturing Planning

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