Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches.

Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet. The large volume and high complexity of such intrusion attacks have impeded the effectiveness of most traditional defence techniques. While at the same time, the remarkable performance of the machine learning methods, especially deep learning, in computer vision, had garnered research interests from the cyber security community to further enhance and automate intrusion detections. However, the expensive data labeling and limitation of anomalous data make it challenging to train an intrusion detector in a fully supervised manner. Therefore, intrusion detection based on unsupervised anomaly detection is an important feature too. In this paper, we propose a three-stage deep learning anomaly detection based network intrusion attack detection framework. The framework comprises an integration of unsupervised (K-means clustering), semi-supervised (GANomaly) and supervised learning (CNN) algorithms. We then evaluated and showed the performance of our implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and TON_IoT.

由于最近在ML和IoT中的突破，部署机器学习（ML）在MilliWatt-Scale-Scale-Scale-Scale Edge设备（Tinyml）上正在越来越受欢迎。但是，Tinyml的功能受到严格的功率和计算约束的限制。 Tinyml中的大多数当代研究都集中在模型压缩技术上，例如模型修剪和量化，以适合低端设备上的ML模型。然而，由于积极的压缩迅速缩小了模型能力和准确性，因此通过现有技术获得的能源消耗和推理时间的改善是有限的。在保留其模型容量的同时，改善推理时间和/或降低功率的另一种方法是通过早期筛选网络。这些网络将中间分类器沿基线神经网络放置，如果中间分类器对其预测表现出足够的信心，则可以促进神经网络计算的早期退出。早期效果网络的先前工作集中在大型网络上，超出了通常用于Tinyml应用程序的功能。在本文中，我们讨论了将早期外观添加到最先进的小型CNN中的挑战，并设计了一种早期筛选架构T-RECX，以解决这些挑战。此外，我们开发了一种方法来减轻在最终退出中通过利用早期外观学到的高级代表性来减轻网络过度思考的影响。我们从MLPERF微小的基准套件中评估了三个CNN的T-RECX，用于图像分类，关键字发现和视觉唤醒单词检测任务。我们的结果表明，T-RECX提高了基线网络的准确性，并显着减少了微小CNN的平均推理时间。 T-RECX达到了32.58％的平均拖鞋降低，以换取所有评估模型的1％精度。此外，我们的技术提高了我们评估的三个模型中的两个基线网络的准确性