Evaluation of machine learning algorithms for anomaly detection

Malicious attack detection is one of the critical cyber-security challenges in the peer-to-peer smart grid platforms due to the fact that attackers' behaviours change continuously over time. In this paper, we evaluate twelve Machine Learning (ML) algorithms in terms of their ability to detect anomalous behaviours over the networking practice. The evaluation is performed on three publicly available datasets: CICIDS-2017, UNSW-NB15 and the Industrial Control System (ICS) cyber-attack datasets. The experimental work is performed through the ALICE high-performance computing facility at the University of Leicester. Based on these experiments, a comprehensive analysis of the ML algorithms is presented. The evaluation results verify that the Random Forest (RF) algorithm achieves the best performance in terms of accuracy, precision, Recall, F1-Score and Receiver Operating Characteristic (ROC) curves on all these datasets. It is worth pointing out that other algorithms perform closely to RF and that the decision regarding which ML algorithm to select depends on the data produced by the application system

On the Construction of Biobank in General Hospitals

Objective: Discussion on a series of biospecimen related issues are conducted, such as collection and preservation, quality control, as well as management and application, during the construction of human tissue biobank in a general hospital. Methods: To develop a set of standardized operational procedures and to collect tissue samples, such as whole blood, serum, plasma, fresh frozen tissues, cerebrospinal fluid, and paraffin-embedded tissues, which were classified and made aliquots according to different requirements, and stored at -80℃ temperature refrigerator or in liquid nitrogen. At the same time, a set of information management software was used to realize management of the biobank. Results: Currently, there are more than 20,000 specimens of various benign and malignant cases, which cover 380 diseases, being collected in the biological database in our hospital. These specimens include paraffin-embedded tissue, fresh frozen tissue, femoral head, whole blood, plasma, serum and cerebrospinal fluid, etc. A large number of these specimensare beneficial is used in clinical research at present. Conclusion: The establishment of biological sample bank can maximize the value of non-reborn human tissue specimens, and provide normal control standards as well as benign and malignant disease standards for clinical diagnosis and treatment, which is of great significance to the research of disease pathogenesis and the development of detection technology

Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack

Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end, we propose a new method to attack action recognizers that rely on 3D skeletal motion. Our method involves an innovative perceptual loss that ensures the imperceptibility of the attack. Empirical studies demonstrate that our method is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Our method shows that adversarial attack on 3D skeletal motions, one type of time-series data, is significantly different from traditional adversarial attack problems. Its success raises serious concern on the robustness of action recognizers and provides insights on potential improvements.Comment: Accepted in CVPR 2021. arXiv admin note: substantial text overlap with arXiv:1911.0710