Abstract
Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.
This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.
Intrusion detection system (IDS) plays a very critical part in identifying threats and monitoring malicious activities in networking system. The system administrators can use IDS to detect unauthorized access by intruders in different organizations. It has become an inevitable element to the security administration of every organization. IDSs can be generally categorized into two categories. The first group focuses on patterns/signatures of network packets/traffic and they identify network intrusions using rule-based matching. The second group uses machine learning (ML) based approaches such as supervised and/or semi-supervised learning and train IDS models on a collection of labeled and/or unlabeled network data. This method has obtained better detection compared to the previous method. This project paper’s scope involves implementing an intrusion detection system using deep learning technology for efficient detection of intrusion and intrusive activities that can cause disruption in the networking system. We use a Feed-forward Neural Network, a deep learning based technique, on KDD99 CUP – a commonly used dataset for network intrusion. In this paper the performance of the proposed system is compared with the existing previous work.
This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.
In the advance of the technology and implantation of Internet of Things (IoT), the realization of smart city seems to be very needed. One of the key parts of a cyber-physical system of urban life is transportation. Such mission-critical application has led to inquisitiveness in researchers to develop autonomous robots from academicians and industry. In the domain of autonomous robot, intelligent video analytics is very crucial. By the advent of deep learning many neural ¬¬¬networks-based learning approaches are considered. One of the advanced Single Shot Multibox Detector (SSD) method is exploited for real-time video/image analysis using an IOT device and vehicles/any barrier avoidance on road is done using image processing. The proposed work makes use of SSD algorithm which is responsible for object detection and image processing to control the car, based on its current input. Thus, this work aims to develop real-time barrier detection and barrier avoidance for autonomous robots using a camera and barrier avoidance sensor in an unstructured environment.
3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human–robot interaction. Autonomous robots equipped with 3D recognition capability can better perform their social roles through supportive task assistance in professional jobs and effective domestic services. For active assistance, social robots must recognize their surroundings, including objects and places to perform the task more efficiently. This article first highlights the value-centric role of social robots in society by presenting recently developed robots and describes their main features. Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models. In this direction, we delineate the research gaps that need to be addressed, summarize 3D recognition datasets, and present performance comparisons. Finally, a discussion of future research directions concludes the article. This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves as a guide to those who are interested in vision-based robotics applications.
Using vision-based object detection for link quality prediction in 5.6-GHz channel
