Anomaly Detection In Blockchain

Anomaly detection has been a well-studied area for a long time. Its applications in the financial sector have aided in identifying suspicious activities of hackers. However, with the advancements in the financial domain such as blockchain and artificial intelligence, it is more challenging to deceive financial systems. Despite these technological advancements many fraudulent cases have still emerged. Many artificial intelligence techniques have been proposed to deal with the anomaly detection problem; some results appear to be considerably assuring, but there is no explicit superior solution. This thesis leaps to bridge the gap between artificial intelligence and blockchain by pursuing various anomaly detection techniques on transactional network data of a public financial blockchain named 'Bitcoin'. This thesis also presents an overview of the blockchain technology and its application in the financial sector in light of anomaly detection. Furthermore, it extracts the transactional data of bitcoin blockchain and analyses for malicious transactions using unsupervised machine learning techniques. A range of algorithms such as isolation forest, histogram based outlier detection (HBOS), cluster based local outlier factor (CBLOF), principal component analysis (PCA), K-means, deep autoencoder networks and ensemble method are evaluated and compared

Predicting deadline transgressions using event logs

Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution

Implementation and assessment of two density-based outlier detection methods over large spatial point clouds

Several technologies provide datasets consisting of a large number of spatial points, commonly referred to as point-clouds. These point datasets provide spatial information regarding the phenomenon that is to be investigated, adding value through knowledge of forms and spatial relationships. Accurate methods for automatic outlier detection is a key step. In this note we use a completely open-source workflow to assess two outlier detection methods, statistical outlier removal (SOR) filter and local outlier factor (LOF) filter. The latter was implemented ex-novo for this work using the Point Cloud Library (PCL) environment. Source code is available in a GitHub repository for inclusion in PCL builds. Two very different spatial point datasets are used for accuracy assessment. One is obtained from dense image matching of a photogrammetric survey (SfM) and the other from floating car data (FCD) coming from a smart-city mobility framework providing a position every second of two public transportation bus tracks. Outliers were simulated in the SfM dataset, and manually detected and selected in the FCD dataset. Simulation in SfM was carried out in order to create a controlled set with two classes of outliers: clustered points (up to 30 points per cluster) and isolated points, in both cases at random distances from the other points. Optimal number of nearest neighbours (KNN) and optimal thresholds of SOR and LOF values were defined using area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Absolute differences from median values of LOF and SOR (defined as LOF2 and SOR2) were also tested as metrics for detecting outliers, and optimal thresholds defined through AUC of ROC curves. Results show a strong dependency on the point distribution in the dataset and in the local density fluctuations. In SfM dataset the LOF2 and SOR2 methods performed best, with an optimal KNN value of 60; LOF2 approach gave a slightly better result if considering clustered outliers (true positive rate: LOF2\u2009=\u200959.7% SOR2\u2009=\u200953%). For FCD, SOR with low KNN values performed better for one of the two bus tracks, and LOF with high KNN values for the other; these differences are due to very different local point density. We conclude that choice of outlier detection algorithm very much depends on characteristic of the dataset\u2019s point distribution, no one-solution-fits-all. Conclusions provide some information of what characteristics of the datasets can help to choose the optimal method and KNN values

Implementing Peer Group Analysis within a Track and Trace System to Detect Potential Fraud(s)

Tracking and tracing of goods movement is a key requirement for supply chain management and analysis. Data collection can be broad and large in volumes. Goods can moves in complex supply chain distributions, where disputes, frauds and thefts can happens. This paper aimed to develop a practical method to analyze the incoming data and employ unsupervised potential fraud detection in near real-time. The method is designed and discussed around peer group analysis (PGA) approach which is commonly used in financial market. The paper shall focus on two steps. First, monitor and groups good movements and categorize vendors or suppliers with similar trend / behaviours into dedicatedpeers. Second build a tool / services that detect anomalies in event transactions. The monitoring serviceshalldetect the outlier orindividual objects that distinct from peers whichpotentially fraud /alerts

Twitter data analysis by means of Strong Flipping Generalized Itemsets

Twitter data has recently been considered to perform a large variety of advanced analysis. Analysis ofTwitter data imposes new challenges because the data distribution is intrinsically sparse, due to a large number of messages post every day by using a wide vocabulary. Aimed at addressing this issue, generalized itemsets - sets of items at different abstraction levels - can be effectively mined and used todiscover interesting multiple-level correlations among data supplied with taxonomies. Each generalizeditemset is characterized by a correlation type (positive, negative, or null) according to the strength of thecorrelation among its items.This paper presents a novel data mining approach to supporting different and interesting targetedanalysis - topic trend analysis, context-aware service profiling - by analyzing Twitter posts. We aim atdiscovering contrasting situations by means of generalized itemsets. Specifically, we focus on comparingitemsets discovered at different abstraction levels and we select large subsets of specific (descendant)itemsets that show correlation type changes with respect to their common ancestor. To this aim, a novelkind of pattern, namely the Strong Flipping Generalized Itemset (SFGI), is extracted from Twitter mes-sages and contextual information supplied with taxonomy hierarchies. Each SFGI consists of a frequentgeneralized itemset X and the set of its descendants showing a correlation type change with respect to X. Experiments performed on both real and synthetic datasets demonstrate the effectiveness of the pro-posed approach in discovering interesting and hidden knowledge from Twitter dat