A Survey and Analysis of Crowd Anomaly Detection Techniques

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

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One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process

Logic Journal of IGPL ◽

10.1093/jigpal/jzaa065 ◽

2020 ◽

Author(s):

Alberto Leira ◽

Esteban Jove ◽

Jose M Gonzalez-Cava ◽

José-Luis Casteleiro-Roca ◽

Héctor Quintián ◽

...

Keyword(s):

Side Effects ◽

Anomaly Detection ◽

General Anesthesia ◽

Closed Loop ◽

Real Data ◽

Successful Performance ◽

Final Model ◽

Detection Techniques ◽

Adverse Side Effects ◽

Operative Recovery

Abstract Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.

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Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

Security and Communication Networks ◽

10.1155/2020/5874935 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Randa Aljably ◽

Yuan Tian ◽

Mznah Al-Rodhaan

Keyword(s):

Machine Learning ◽

Social Networks ◽

Access Control ◽

Anomaly Detection ◽

Privacy Preservation ◽

Support Vector ◽

Detection Techniques ◽

Access Control Models ◽

Control Models ◽

Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

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