Anomaly detection is the first step with a challenging task of securing a communication network, as the anomalies may indicate suspicious behaviors, attacks, network malfunctions, or failures. In this paper, we address the problem of not only detecting different anomalies, such as volume based (e.g., DDoS or Flash crowd) and spatial based (e.g., network scan), that arise simultaneously in the wild but also of attributing the anomalous point to a single-anomaly event causing it. Besides, we also tackle the problem of low-detection accuracy caused by the phenomenon of traffic drift. To this end, a novel adaptive profile-based anomaly detection scheme is proposed. More specifically, a more comprehensive metrics set is defined from the dimensions of temporal, spatial, category, and intensity to compose IP traffic behavior characteristic spectrum for fine-grained traffic characterization. Then, the digital signature matrix obtained by using the ant colony optimization (ACO) algorithm is applied to construct the baseline profile of the normal traffic behavior. Anomalous points are identified and analyzed by using confidence bands and a generic clustering technique, respectively. Finally, a lightweight updating strategy is applied to reduce the number of false positives. Real-world data of China Education Research Network backbone and synthetic data are collected to verify our proposal. The experimental results demonstrate that our approach provides a fine-grained behavior description ability and has significantly increased the detection accuracy compared with other similar alternatives.
Cell tracking is currently a powerful tool in a variety of biomedical research topics. Most cell tracking algorithms follow the tracking by detection paradigm. Detection is critical for subsequent tracking. Unfortunately, very accurate detection is not easy due to many factors like densely populated, low contrast, and possible impurities included. Keeping tracking multiple cells across frames suffers many difficulties, as cells may have similar appearance, they may change their shapes, and nearby cells may interact each other. In this paper, we propose a unified tracking-by-detection framework, where a powerful detector AttentionUnet++, a multimodal extension of the ECO algorithm, and an effective data association algorithm are included. Experiments show that the proposed algorithm can outperform many existing cell tracking algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.