Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. Such systems depend on the availability of both (benign and malicious) network data classes during the training phase. However, attack data samples are often challenging to collect in most organisations due to security controls preventing the penetration of known malicious traffic to their networks. Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples. The novel one-class classification architecture consists of a histogram-based deep feed-forward classifier to extract useful network data features and use efficient outlier detection. The DOC classifier has been extensively evaluated using two benchmark NIDS datasets. The results demonstrate its superiority over current state-of-the-art one-class classifiers in terms of detection and false positive rates.

在本文中，我们提出了XG-Bot，这是一种可解释的深层图神经网络模型，用于僵尸网络淋巴结检测。所提出的模型主要由僵尸网络检测器和自动取证的解释器组成。XG机器人检测器可以有效检测大型网络下的恶意僵尸网络节点。具体而言，它利用与图同构网络的分组可逆残差连接从僵尸网络通信图中学习表达性节点表示。XG机器人中的解释器可以通过突出可疑网络流和相关的僵尸网络节点来执行自动网络取证。我们评估了现实世界中的大规模僵尸网络网络图。总体而言，就评估指标而言，XG机器人能够超越最先进的方法。此外，我们表明XG机器人解释器可以基于自动网络取证的Gnnexplainer生成有用的解释。

本文提出了一种基于图形神经网络（GNN）的新的Android恶意软件检测方法，并具有跳跃知识（JK）。Android函数呼叫图（FCGS）由一组程序功能及其术间调用组成。因此，本文提出了一种基于GNN的方法，用于通过捕获有意义的心理内呼叫路径模式来检测Android恶意软件的检测方法。此外，采用跳跃知识技术来最大程度地减少过度平滑问题的效果，这在GNN中很常见。该方法已使用两个基准数据集对所提出的方法进行了广泛的评估。结果表明，与关键分类指标相比，与最先进的方法相比，我们的方法的优越性，这证明了GNN在Android恶意软件检测和分类中的潜力。

在单个组织中设计和评估时，机器学习（ML）在检测网络攻击中的用途是有效的。然而，通过利用源自若干来源的异构网络数据样本来设计基于ML的检测系统非常具有挑战性。这主要是由于隐私问题和缺乏数据集的普遍格式。在本文中，我们提出了协同联合学习计划来解决这些问题。拟议的框架允许多个组织在设计，培训和评估中加入强大的ML的网络入侵检测系统的武力。威胁情报方案利用其应用的两个关键方面;以通用格式提供网络数据流量的可用性，以允许在数据源上提取有意义的模式。其次，采用联合学习机制来避免在组织之间共享敏感用户信息的必要性。因此，每个组织都与其他组织网络威胁智能受益，同时在内部保持其数据的隐私。该模型在本地培训，只有更新的权重与剩余的参与者共享联合平均过程。通过使用称为NF-UNSW-NB15-V2和NF-BOT-IOT-V2的NETFOL格式的两个密钥数据集，在本文中设计和评估了该框架。在评估过程中考虑了另外两种常见情景;一种集中式培训方法，其中与其他组织共享本地数据样本和本地化培训方法，没有共享威胁情报。结果证明了通过设计通用ML模型的建议框架的效率和有效性，这些框架模型有效地分类源自多个组织的良性和侵入性流量，而无需当地数据交换。

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network interruptions and loss of sensitive data have occurred, which led to an active research area for improving NIDS technologies. In an analysis of related works, it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction (FR) and Machine Learning (ML) techniques on NIDS datasets. However, these datasets are different in feature sets, attack types, and network design. Therefore, this paper aims to discover whether these techniques can be generalised across various datasets. Six ML models are utilised: a Deep Feed Forward (DFF), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Decision Tree (DT), Logistic Regression (LR), and Naive Bayes (NB). The accuracy of three Feature Extraction (FE) algorithms; Principal Component Analysis (PCA), Auto-encoder (AE), and Linear Discriminant Analysis (LDA), are evaluated using three benchmark datasets: UNSW-NB15, ToN-IoT and CSE-CIC-IDS2018. Although PCA and AE algorithms have been widely used, the determination of their optimal number of extracted dimensions has been overlooked. The results indicate that no clear FE method or ML model can achieve the best scores for all datasets. The optimal number of extracted dimensions has been identified for each dataset, and LDA degrades the performance of the ML models on two datasets. The variance is used to analyse the extracted dimensions of LDA and PCA. Finally, this paper concludes that the choice of datasets significantly alters the performance of the applied techniques. We believe that a universal (benchmark) feature set is needed to facilitate further advancement and progress of research in this field.

本文介绍了基于图形神经网络（GNN）的新的网络入侵检测系统（NID）。 GNN是深度神经网络的一个相对较新的子领域，可以利用基于图形数据的固有结构。 NIDS的培训和评估数据通常表示为流记录，其可以自然地以图形格式表示。这建立了探索网络入侵检测GNN的潜在和动力，这是本文的重点。基于机器的基于机器的NIDS的目前的研究只考虑网络流动，而不是考虑其互连的模式。这是检测复杂的物联网网络攻击的关键限制，例如IOT设备推出的DDOS和分布式端口扫描攻击。在本文中，我们提出了一种克服了这种限制的GNN方法，并允许捕获图形的边缘特征以及IOT网络中网络异常检测的拓扑信息。据我们所知，我们的方法是第一次成功，实用，广泛地评估应用图形神经网络对使用流基于流的数据的网络入侵检测问题的方法。我们在最近的四个NIDS基准数据集上进行了广泛的实验评估，表明我们的方法在关键分类指标方面占据了最先进的，这证明了网络入侵检测中GNN的潜力，并提供了进一步研究的动机。

Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency. In many cases, the implementation of DyNNs can be sub-optimal due to its underlying backbone architecture being developed at the design stage independent of both: (i) the dynamic computing features, e.g. early exiting, and (ii) the resource efficiency features of the underlying hardware, e.g., dynamic voltage and frequency scaling (DVFS). Addressing this, we present HADAS, a novel Hardware-Aware Dynamic Neural Architecture Search framework that realizes DyNN architectures whose backbone, early exiting features, and DVFS settings have been jointly optimized to maximize performance and resource efficiency. Our experiments using the CIFAR-100 dataset and a diverse set of edge computing platforms have seen HADAS dynamic models achieve up to 57% energy efficiency gains compared to the conventional dynamic ones while maintaining the desired level of accuracy scores. Our code is available at https://github.com/HalimaBouzidi/HADAS

由于自动驾驶应用程序的高性能和安全要求，现代自动驾驶系统（AD）的复杂性一直在增长，刺激了对更复杂的硬件的需求，这可能会增加广告平台的能量足迹。在解决此问题时，Edge Computing有望包含自动驾驶应用程序，从而使计算密集型的自治任务能够在计算能力的边缘服务器下进行处理。但是，除了严格的鲁棒性需求外，ADS平台的复杂硬件体系结构还阐明了自动驾驶独有的任务卸载并发症。因此，我们提出了$ romanus $，这是一种具有多传感器处理管道的模块化广告平台的可靠和高效任务的方法。我们的方法论需要两个阶段：（i）沿相关深度学习模型的执行路径引入有效的卸载点，以及（ii）基于深度强化学习的运行时解决方案的实现，以根据在操作模式下根据变化的变化来调整操作模式。感知到的道路场景复杂性，网络连接和服务器负载。对象检测用例的实验表明，我们的方法比纯局部执行高14.99％，同时从强大的不稳定卸载基线中降低了危险行为的77.06％。

掌握进行手术所需的技术技能是一项极具挑战性的任务。基于视频的评估使外科医生可以收到有关其技术技能的反馈，以促进学习和发展。目前，此反馈主要来自手动视频评论，该视频审查是耗时的，限制了在许多情况下跟踪外科医生进展的可行性。在这项工作中，我们引入了一种基于运动的方法，以自动评估手术病例视频饲料的手术技能。拟议的管道首先可靠地轨道轨迹，以创建运动轨迹，然后使用这些轨迹来预测外科医生的技术技能水平。跟踪算法采用了一个简单而有效的重新识别模块，与其他最新方法相比，它可以改善ID-开关。这对于创建可靠的工具轨迹至关重要，当仪器定期在屏幕上和屏幕外移动或定期遮盖。基于运动的分类模型采用最先进的自我发明变压器网络来捕获对技能评估至关重要的短期和长期运动模式。在体内（Cholec80）数据集上评估了所提出的方法，其中专家评级的目标技能评估对Calot三角解剖的评估被用作定量技能度量。我们将基于变压器的技能评估与传统的机器学习方法进行比较，并使用拟议的和最新的跟踪方法进行比较。我们的结果表明，使用可靠跟踪方法的运动轨迹对仅根据视频流进行评估的外科医生技能是有益的。

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of <instrument, verb, target> combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.