Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data

Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect problems in their networks. Unfortunately, there is lack of efficient tools to automatically track and detect anomalous events on operational data, causing ISP operators to rely on manual inspection of this data. While anomaly detection has been widely studied in the context of network data, operational data presents several new challenges, including the volatility and sparseness of data, and the need to perform fast detection (complicating application of schemes that require offline processing or large/stable data sets to converge). To address these challenges, we propose Tiresias, an automated approach to locating anomalous events on hierarchical operational data. Tiresias leverages the hierarchical structure of operational data to identify high-impact aggregates (e.g., locations in the network, failure modes) likely to be associated with anomalous events. To accommodate different kinds of operational network data, Tiresias consists of an online detection algorithm with low time and space complexity, while preserving high detection accuracy. We present results from two case studies using operational data collected at a large commercial IP network operated by a Tier-1 ISP: customer care call logs and set-top box crash logs. By comparing with a reference set verified by the ISP's operational group, we validate that Tiresias can achieve >94% accuracy in locating anomalies. Tiresias also discovered several previously unknown anomalies in the ISP's customer care cases, demonstrating its effectiveness

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Machine Learning-Based Approaches for Credit Card Fraud Detection: A Comprehensive Reviewz

The objective of data analytics is to discover hidden patterns and use them to guide wise judgements in a range of circumstances. Theft of credit cards has significantly grown as a result of modern technologies and has become a popular target for scam artists. Publicly available databases on credit card fraud are very unbalanced. As more people conduct business online, Fraud involving credit cards has grown to be a serious problem for both consumers and financial establishments. standard rule-based fraud detection strategies have shown to be insufficient to combat fraudsters' ever-evolving tactics. Machine learning algorithms have thus developed into a powerful tool for real-time unsupervised learning, and anomaly detection., is then explored in detail in order to accurately identify fraudulent transactions. Furthermore, we explore the various data features utilized by machine learning algorithms, including transaction history, transaction amounts, merchant information, and geographical locations. “For people, companies, and financial institutions, A significant financial danger is credit card fraud. In order to detect theft, robust methods for machine learning must be developed. researchers can help minimize financial losses associated with fraudulent activities”. In this research we will be using weighted product method. Taken as Alternative parameters is “Fraud detection using Game theory for M1, Hybrid Approach For Fraud Detection Using Svm And Decision Tree for M2, Fraud Detection Using Som & Psofor M3, Dempster Shafer Theory Along With Bayesian Learning For Detecting Fraudfor M4, Cardwatchfor M5”. Taken as Evaluation parameters is “Sum of Squared Error, Mean Squared error, Root Mean Square error, Mean Absolute error, Root Mean Square Prediction Error, and Accuracy”. Model 1 outperformed the other 4 models when a machine learning algorithm was used to identify credit card frauds. With Weighted Product Method we are able to find the best way of detection of credit card frauds by machine learning algorithm which has been evaluated with various parameters and methodology

A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses issues related to multi-operating-condition TCM models, namely the varying discriminability of sensory features with different operating conditions; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed that all the studied anomaly detection approaches achieved a higher assessment accuracy with our feature selection scheme as compared to the Principal Component Analysis (PCA), Laplacian Score (LS), and extended LS in which we added a final step to the original LS method in order to eliminate redundant features

Data analytics for smart parking applications

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset