The purpose of this article is to summarise some simple yet powerful statistical techniques that can be readily used for initial screening of outliers. Credit card fraud detection is the most cited one but in numerous other cases anomaly detection is an essential part of doing business such as detecting network intrusion, identifying instrument failure, detecting tumor cells etc.Ī range of tools and techniques are used to detect outliers and anomalies, from simple statistical techniques to complex machine learning algorithms, depending on the complexity of data and sophistication needed. There is also a huge industrial application of anomaly detection. Sometimes we filter completely legitimate outlier data points and remove them to ensure greater model performance. As a data scientist when we make data preparation we take great care in understanding if there is any data point unexplained, which may have entered erroneously. There are numerous reasons why understanding and detecting outliers are important. In this article, however, I am using these terms interchangeably. That is to say, all anomalies are outliers but not necessarily all outliers are anomalies. However, when this outlier is completely unexpected and unexplained, it becomes an anomaly. An “outliers’ generally refers to a data point that somehow stands out from the rest of the crowd. In data science, “Outlier”, “Anomaly” and “Fraud” are often synonymously used, but there are subtle differences. To counter these kinds of financial losses a huge amount of resources are employed to identify frauds and anomalies in every single industry. In the UK fraudulent credit card transaction losses were estimated at more than USD 1 billion in 2018. According to a Nilson Report, the amount of global credit card fraud alone was USD 7.6 billion in 2010. Furthermore, the proposed method prevents the occurrence of excessive overload on the controller and OpenFlow.Anomaly and fraud detection is a multi-billion-dollar industry. The AC, DR, CE, and FAR of the proposed model were measured as 99.92%, 99.83%, 0.08%, and 0.03%, respectively. The results obtained from the experiments revealed that the proposed method performs better than other methods in terms of enhancing accuracy (AC) and detection rate (DR) and reducing classification error (CE) and false alarm rate (FAR). In this paper, a novel combined approach is proposed this method uses NetFlow protocol for gathering information and generating dataset, information gain ratio (IGR), in order to select the effective and relevant features and ensemble learning scheme (Stacking) for developing a structure with desirable performance and efficiency for detecting anomaly in SDN environment. The challenges are related to designing these systems including gathering data, extracting effective features, and selecting the best model for anomaly detection. Indeed, anomaly detection systems have been considered to deal with these attacks. As a case in point, one of the shortcomings of SDNs is related to its high vulnerability to distributed denial of service (DDoS) attacks and other similar ones. However, it should be noted that SDN architecture suffers from the same security issues, which are the case with common networks. Nowadays, software-defined networking (SDN) is regarded as the best solution for the centralized handling and monitoring of large networks.
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