Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 1/4 ( 121 ) 2023 56 Copyright © 2023, Authors. This is an open access article under the Creative Commons CC BY license ANOMALY DETECTION IN INTERNET OF MEDICAL THINGS WITH ARTIFICIAL INTILLEGENCE Shalau Farhad Hussein College of Information Technology and Computer Science Presidency of University of Kirkuk* Zena Ez. Dallalbashi Department of Electronic Technical Institute Northern Technical University Mosul, Iraq, 41002 Ahmed Burhan Mohammed Corresponding author College of Dentistry Presidency of University of Kirkuk* E-mail: ahmedlogic79@uokirkuk.edu.iq *University of Kirkuk Al-Sayada Area Kirkuk, Kirkuk, Iraq, 36001 Internet of things (IoT) becomes the most popular term in the recent advances in Healthcare devices. The health- care data in the IoT process and structure is very sensi- tive and critical in terms of healthy and technical consid- erations. Outlier detection approaches are considered as principal tool or stage of any IoT system and are main- ly categorized in statistical and probabilistic, clustering and classification-based outlier detection. Recently, fuzzy logic (FL) system is used in ensemble and cascade systems with other ML-based tools to enhance outlier detection performance but its limitation involves the false detection of outliers. In this paper, we propose a fuzzy logic system that uses the anomaly score of each point using local out- lier factor (LOF), connectivity-based outlier factor (COF) and generalized LOF to eliminate the confusion in classi- fying points as outliers or inliers. Regarding human acti- vity recognition (HAR) dataset, the FL achieved a value of 98.2 %. Compared to the performance of LOF, COF, and GLOF individually, the accuracy increased slightly, but the increase in precision and recall indicates an increase in correctly classified data and that neither true nor abnormal data is classified wrongly. The results show the increase in precision and recall which indicates an increase in correct- ly classified data. Thus, it can be confirmed that fuzzy logic with input of scores achieved the desired goal in terms of mitigating cases of false detection of anomalous data. By comparing the proposed ensemble of fuzzy logic and diffe- rent types of local density scores in this study, the outcomes of fuzzy logic presents a new way of elaborating or fusing the different tools of the same purpose to enhance detection performance Keywords: anomaly detection, outlier score, anomaly score, fuzzy logic, hybrid system UDC 621 DOI: 10.15587/1729-4061.2023.274575 How to Cite: Hussein, S. F., Dallalbashi, Z. Ez., Mohammed, A. B. (2023). Anomaly detection in internet of medical things with artificial intillegence. Eastern-European Journal of Enterprise Technologies, 1 (4 (121)), 56–62. doi: https://doi.org/ 10.15587/1729-4061.2023.274575 Received date 07.12.2022 Accepted date 15.02.2023 Published date 28.02.2023 1. Introduction Internet of things (IoT) becomes the most popular term in the recent advances in Healthcare devices. Whereas the most common example of IoT in health care is remote mon- itoring of patients, like IoT devices that collect patient data such as heart rate and biomarkers [1]. IoT devices offer new features for healthcare providers including accurate and in- time patient monitoring and treating. In addition, various wearable monitors and sensors provide plenty of benefits and capabilities for healthcare providers and their patients. Basi- cally, the applications of IoT in the medical field are remote patient monitoring like biosignals, depression and mood monitoring and human activity recognition (HAR). Reliable IoT technology in the medical field requires proper data transmission and addressing critical security challenges [2]. Healthcare data is very sensitive and critical and any distortion or malfunction in the transmitter and receiver may change or contaminate the diagnosis of the patients. In cases of cyber-attacks or the noise of the equipment and the surrounding environment, there will be anomalies or outliers in this data. Those anomalies must be detected accurately in the real-time or appropriate moment to prevent any serious consequences, the worst of which is the life of the monitored patient. The concept of anomaly detection includes a wide range of applications, such as outlier or novelty detection and intrusion detection [3]. Outlier detection approaches are considered as principal tool or stage of any IoT system [2] and are mainly catego- rized in statistical and probabilistic, clustering and classifi- cation-based outlier detection. Statistical methods rely on previously measured data to approximate the correct beha- vior model of the data such as the autoregressive and moving average (ARMA) prediction model, adaptive kernel density estimator (AKDE) approach and classification and regression Trees (CART) model where new data is compared to the pre- viously generated model data. If the results show the statisti- cal significance of the data is different, the new data is marked as an anomaly. The probabilistic method relies on distinguish- ing outliers from normal or inlier data. If the probability is below a predetermined threshold, it is classified as abnormal. Outlier detection and classification during monitoring and detection of human activity using environmental sensors, wearable sensors, or both is still under development due to the prevalence and recent achievements in medical IoT and 6G era communications. Clustering-based outlier detection