In the past few years, cybersecurity is becoming very important due to the rise in internet users. The internet attacks such as Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks severely harm a website or server and make them unavailable to other users. Network Monitoring and control systems have found it challenging to identify the many classes of DoS and DDoS attacks since each operates uniquely. Hence a powerful technique is required for attack detection. Traditional machine learning techniques are inefficient in handling extensive network data and cannot extract high-level features for attack detection. Therefore, an effective deep learning-based intrusion detection system is developed in this paper for DoS and DDoS attack classification. This model includes various phases and starts with the Deep Convolutional Generative Adversarial Networks (DCGAN) based technique to address the class imbalance issue in the dataset. Then a deep learning algorithm based on ResNet-50 extracts the critical features for each class in the dataset. After that, an optimized AlexNet-based classifier is implemented for detecting the attacks separately, and the essential parameters of the classifier are optimized using the Atom search optimization algorithm. The proposed approach was evaluated on benchmark datasets, CCIDS2019 and UNSW-NB15, using key classification metrics and achieved 99.37% accuracy for the UNSW-NB15 dataset and 99.33% for the CICIDS2019 dataset. The investigational results demonstrate that the suggested approach performs superior to other competitive techniques in identifying DoS and DDoS attacks.
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Many researchers and organizations, such as WHO and UNICEF, have raised awareness of the dangers of advertisements targeted at children. While most existing laws only regulate ads on television that may reach children, lawmakers have been working on extending regulations to online advertising and, for example, forbid (e.g., the DSA) or restrict (e.g., the COPPA) advertising based on profiling to children. At first sight, ad platforms such as Google seem to protect children by not allowing advertisers to target their ads to users who are less than 18 years old. However, this paper shows that other targeting features can be exploited to reach children. For example, on YouTube, advertisers can target their ads to users watching a particular video through placement-based targeting, a form of contextual targeting. Hence, advertisers can target children by placing their ads in children-focused videos. Through a series of ad experiments, we show that placement-based targeting is possible on children-focused videos and enables marketing to children. In addition, our ad experiments show that advertisers can use targeting based on profiling (e.g., interest, location, behavior) in combination with placement-based advertising on children-focused videos. We discuss the lawfulness of these two practices concerning DSA and COPPA. Finally, we investigate to which extent real-world advertisers are employing placement-based targeting to reach children with ads on YouTube. We propose a measurement methodology consisting of building a Chrome extension to capture ads and instrument six browser profiles to watch children-focused videos. Our results show that 7% of ads that appear in the children-focused videos we test use placement-based targeting. Hence, targeting children with ads on YouTube is not only hypothetically possible but also occurs in practice...
3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersive and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job scheduling algorithm that leverages Mobile Edge Computing (MEC) servers in order to minimize the total latency in 3D holographic communication. One of the motivations for this work is to prevent the uncanny valley effect, which can occur when the latency hinders the seamless and real-time rendering of holographic content, leading to a less convincing and less engaging user experience. Our proposed algorithm dynamically allocates computation tasks to MEC servers, considering the network conditions, computational capabilities of the servers, and the requirements of the 3D holographic communication application. We conduct extensive experiments to evaluate the performance of our algorithm in terms of latency reduction, and the results demonstrate that our approach significantly outperforms other baseline methods. Furthermore, we present a practical scenario involving Augmented Reality (AR), which not only illustrates the applicability of our algorithm but also highlights the importance of minimizing latency in achieving high-quality holographic views. By efficiently distributing the computation workload among MEC servers and reducing the overall latency, our proposed algorithm enhances the user experience in 3D holographic communications and paves the way for the widespread adoption of this technology in various applications, such as telemedicine, remote collaboration, and entertainment.
Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption of low-diameter networks, we design, implement, deploy, and evaluate the first real-world SF installation. We focus on deployment, management, and operational aspects of our test cluster with 200 servers and carefully analyze performance. We demonstrate techniques for simple cabling and cabling validation as well as a novel high-performance routing architecture for InfiniBand-based low-diameter topologies. Our real-world benchmarks show SF's strong performance for many modern workloads such as deep neural network training, graph analytics, or linear algebra kernels. SF outperforms non-blocking Fat Trees in scalability while offering comparable or better performance and lower cost for large network sizes. Our work can facilitate deploying SF while the associated (open-source) routing architecture is fully portable and applicable to accelerate any low-diameter interconnect.
Open RAN brings multi-vendor diversity and interoperability to mobile/cellular networks. It is becoming part of governmental strategies for diversifying telecoms supply chains. This paper describes the approach and key achievements of the BEACON-5G project, jointly funded by the UK government and industry. The BEACON-5G project aims at developing a competitive edge for 5G Open RAN and contributing toward its maturity. It addresses some of the key challenges in this respect and provides various innovations for system integration, network slicing, marketplace integration, cyber security, and white-box RAN. It also conducts real-world technology trials for urban use-cases. The paper also captures some of the key lessons learned during delivery, the main outcomes, and highlights potential impact on the wider UK 5G diversification strategy.
Model Predictive Control lacks the ability to escape local minima in nonconvex problems. Furthermore, in fast-changing, uncertain environments, the conventional warmstart, using the optimal trajectory from the last timestep, often falls short of providing an adequately close initial guess for the current optimal trajectory. This can potentially result in convergence failures and safety issues. Therefore, this paper proposes a framework for learning-aided warmstarts of Model Predictive Control algorithms. Our method leverages a neural network based multimodal predictor to generate multiple trajectory proposals for the autonomous vehicle, which are further refined by a sampling-based technique. This combined approach enables us to identify multiple distinct local minima and provide an improved initial guess. We validate our approach with Monte Carlo simulations of traffic scenarios.
System correctness is one of the most crucial and challenging objectives in software and hardware systems. With the increasing evolution of connected and distributed systems, ensuring their correctness requires the use of formal verification for multi-agent systems. In this paper, we present a summary of certain results on model checking for multi-agent systems that derive from the selection of strategies and information for agents. Additionally, we discuss some open directions for future research.
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to interpret. In that regard, many efforts have been made to explain the prediction mechanisms of these models from perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works present systematic frameworks to interpret GNNs, a holistic review for explainable GNNs is unavailable. In this survey, we present a comprehensive review of explainability techniques developed for GNNs. We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We further provide the common performance metrics for GNNs explanations and point out several future research directions.
Neural networks have shown tremendous growth in recent years to solve numerous problems. Various types of neural networks have been introduced to deal with different types of problems. However, the main goal of any neural network is to transform the non-linearly separable input data into more linearly separable abstract features using a hierarchy of layers. These layers are combinations of linear and nonlinear functions. The most popular and common non-linearity layers are activation functions (AFs), such as Logistic Sigmoid, Tanh, ReLU, ELU, Swish and Mish. In this paper, a comprehensive overview and survey is presented for AFs in neural networks for deep learning. Different classes of AFs such as Logistic Sigmoid and Tanh based, ReLU based, ELU based, and Learning based are covered. Several characteristics of AFs such as output range, monotonicity, and smoothness are also pointed out. A performance comparison is also performed among 18 state-of-the-art AFs with different networks on different types of data. The insights of AFs are presented to benefit the researchers for doing further research and practitioners to select among different choices. The code used for experimental comparison is released at: \url{//github.com/shivram1987/ActivationFunctions}.
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.
Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions. Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal. In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations. Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction. Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space. A domain critic module (DCM) is set up for discriminating the feature space of both domains. We optimize the DAM and DCM via an adversarial loss without using any target domain label. Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.
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