# A Mixed Clustering Approach for Real-Time Anomaly Detection
## 2 Related Works  
Anomaly detection is the finding of the data object, which deviates from the previously observed one. In [1], the authors discussed anomalies and different clustering-based techniques and algorithm to identify them. Intrusion detection systems (IDS) are protection steps that can assure safety and security from unauthorized access. There are several well-known IDS available and signature detections system is one of them. Recently, the anomaly based IDS [2] have been gaining popularity because of their wide-spread use and ability to detect insider attacks or previously unknown attacks. Most of the anomaly based IDS follows either clustering or classification-based approaches. Data mining is a well-known area of research, which includes techniques to discover patterns from large datasets. The most popular data mining tasks include pattern mining, association rule mining, clustering, classification, anomaly detections, etc. [3]. Usually, there are three fundamental practices of data mining, namely unsupervised, supervised and semi-supervised learning [4]. Clustering [5] is a popular technique used to find patterns and data distributions in datasets and it has been frequently used in many fields of human knowledge. There are several clustering algorithms developed to date and \(k\)-means, _ROCK_, _CACTUS_, _DBSCAN_, and hierarchical agglomerative algorithms [5; 6; 7] are some of them. Clustering can be either static or dynamic depending on the nature of datasets. Recently, dynamic clustering is receiving a lot of attention from researchers mainly because of its widespread utility in Wireless Sensor Networks, IoT, Cloud, Finance and stock markets, etc., which demand a dynamic clustering approach. A hierarchical algorithm for both static as well as dynamic datasets was proposed in [8]. In [9], the authors proposed several incremental clustering algorithms that can process new records or data instance as they are added to the dataset. Accordingly, clustering has widely been employed in many areas of information security. In [12; 14], the authors proposed two algorithms for the detection of anomalies from mixed attribute-dataset. [PERSON] et al. [15] introduced an anomaly detection technique of multi-dimensional as well as categorical data. [PERSON] et al. [16] proposed a clustering-based anomaly detection approach of big data. [PERSON] et al. [17] proposed a density-based clustering approach using R. A hybrid approach of anomaly detection of high-dimensional data using semi-supervised technique was proposed in [18]. [PERSON] et al. [19] proposed a real-time anomaly detection method based on time series analysis. Fuzzy was brought into clustering and IDS by [PERSON] et al. [13] and [PERSON] et al. [20]. In [37], the authors conducted a detailed study on intrusion detection systems, fuzzy anomaly detection approach along with their advantages and limitations. In [35], the authors proposed an algorithm to detect anomalies from temporal data. In [36], the authors proposed a real-time eGFC, to the log-based anomaly detection problem with time-varying data from the Tier-1 Bologna computer center. In [34], the authors discussed a model using the association between fuzzy logic and ANN to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In [21], the authors conducted comparative study of five different time series anomaly detection models. [PERSON] et al. [22] proposed neighborhood rough set-based classification model for the anomaly detection of mixed data. In [23], a comprehensive review was conducted on anomaly detection using data mining techniques. In [24], a detailed review was conducted on anomaly detection of high-dimensional big data. In [26; 27], the authors conducted a detailed analysis of different real-time anomaly techniques available in the literature. A method of anomaly detection of multi-dimensional data using time series data cube as new data structure was proposed in [28]. [PERSON] et al.