I.J. Intelligent Systems and Applications, 2021, 1, 58-68 Published Online February 2021 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2021.01.05 Copyright © 2021 MECS I.J. Intelligent Systems and Applications, 2021, 1, 58-68 Genetic-based Summarization for Local Outlier Detection in Data Stream Mohamed Sakr, Walid Atwa and Arabi Keshk Computer Science Dept. Faculty of Computers and Information, Menoufia University, Egypt E-mail: mssakr@ymail.com, walid.atwa@ci.menofia.edu.eg, arabikeshk@yahoo.com Received: 03 February 2020; Accepted: 16 March 2020; Published: 08 February 2021 Abstract: Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data. Index Terms: Outlier detection, data streams, local outlier factor, genetics. 1. Introduction Outlier detection is also known as anomaly detection has gained a lot of importance and attention in the field of data mining. It has been used in many applications such as credit card fraud detection and intrusion detection in web apps. A lot of algorithms have been developed to detect outliers in static data in which the number of points are determined and doesn’t change over time. However, detecting outliers on streamed data is difficult because the size of the data set is infinite, and the data is changing over time thus can’t be stored in memory for processing [1]. One of the techniques that are used in outlier detection is density-based techniques. Density-based techniques have a great ability to detect outliers in different densities and dealing with nonhomogeneous densities datasets. One of the well-known algorithms for outlier detection that is density based is Local Outlier Factor LOF. LOF has been used in data sets with heterogeneous densities [2, 3, 4]. However, LOF deals with static data that don’t change over time as its calculations are done over the whole data one time. Because LOF did its calculations one time on the whole data it needs a huge amount of memory to store the data to process. Specifically, LOF has O( n 2 ) space complexity to detect outliers as it stores all the points of the data and its distances between the all points. Also in any change in data by adding or deleting any points the LOF needs to be recalculated on the whole data set. Such these limitations of LOF, it can’t be used wit h data streams as data streams size are infinite and data are changing over time as new points arrive [5]. A data stream is a continuous data records ordered by timestamps and the data points are available partially at any given point in time. Thus, when working on applications with streaming data, their temporal contexts need to be considered. In addition, the processing needs additional requirement on computational and memory resources. There are many applications that detect outlier detection over streaming data, such as network detection, fraud detections, and environmental monitoring. Thus, we need to find abnormal data over data streams in real-time. Researchers have proposed different solutions to this problem. One of those solutions works by using sliding- window in the application and performing learning only on those windowed data. This solution performs well in some applications and also makes real-time results. However, the correctness of its results depends largely on the size of window that is not considered. There are other existing solutions but most of them fail to address those properties of streaming data, and thus produce results exhibiting poor accuracy [6]. In this paper, we aim to propose new algorithm called GSILOF (Genetic Summarizing Incremental LOF). that overcome aforementioned challenges in streaming data. The GSILOF algorithm consists of two phases 1) detection