Anomaly detection in online social networks using data mining techniques and fuzzy logic

ANOMALY DETECTION IN ONLINE SOCIAL NETWORKS: USING DATAMINING TECHNIQUES AND FUZZY LOGIC Reza Hassanzadeh BEng., MSc Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy School of Electrical Engineering and Computer Science Faculty of Science and Engineering Queensland University of Technology November 2014 Keywords Anomaly Detection Clustering-Based Model Distribution-Based Model Expectation Maximization Fuzzy Clustering Fuzzy Inference Engine Fuzzy Logic Gaussian Mixture Model Graph-Based Anomaly Detection Membership Function Online Social Networks Orthogonal Projection Power-Law Regression Model Predictive Model Anomaly Detection In Online Social Networks: Using data-mining Techniques and Fuzzy Logic i ii Anomaly Detection In Online Social Networks: Using data-mining Techniques and Fuzzy Logic Abstract The Online Social Networks (OSNs), which captures the structure and dynamics of person-to-person and person-to-technology interaction, is being used for various purposes such as business, education, telemarketing, medical, entertainment This technology also opens the door for unlawful activities Detecting anomalies, in this new perspective of social life that articulates and reflects the off-line relationships, is an important factor as they could be a sign of a significant problem or carrying useful information for the analyser Two types of data can be inferred from OSNs: (1) the behavioural data that considers the dynamic usage behaviour of users; and (2) the structural data that considers the structure of the networks These two types of data can be modelled by graph theory in order to extract meaningful features which can be analysed by appropriate techniques Existing anomaly detection techniques using graph modelling are limited due to issues such as time and computational complexity, low accuracy, missing value, privacy, and lack of labelled datasets To overcome the existing limitations, we present various hybrid methods that utilise different types of structural input features and techniques We present these approaches within a multi-layered framework which provides the full requirements needed for finding anomalies in online social networks data graph, including modelling, algorithms, labelling, and evaluation Anomaly Detection In Online Social Networks: Using data-mining Techniques and Fuzzy Logic iii In the first layer of the proposed framework, we model an online social network with graph theory and compute the various graph features for the nodes in the graph The second layer of the framework includes our methods which tackle the problem of anomaly detection in online social networks from different angles: distance-based, distribution-based, and clustering-based We use fuzzy logic to define the boundaries of the anomalies as they can be treated as a multiple-valued logic problem in which we have a degree of truth rather than as only two possible values (normal or abnormal) The third layer of our framework is for evaluating the proposed methods using three different and popular OSNs The experiment results show in general that (1) a combination of orthogonal projection and a clustering algorithm can improve the accuracy of the distance-based method, and (2) in terms of increasing accuracy, using fuzzy based clustering shows better results compared to using hard portioning ones The reason behind the outperformance of the proposed fuzzy-based clustering method is that instances can be members of more than one cluster, with different levels of certainty This contrasts with hard partitioning algorithms such as k-means in which any instances can belong to only a single cluster This means that the fuzzy nature of friendship relations is lost during clustering, which affects the quality of detecting anomalies within the OSNs data Moreover, experiments show the distribution-based method outperforms the accuracy among all other methods, because of the ability to find the natural relationship between instances with the expectation-maximization algorithm and describe the fuzziness of the instances with fuzzy logic The evaluation results are consistent among the three different real-life datasets iv Anomaly Detection In Online Social Networks: Using data-mining Techniques and Fuzzy Logic Table of Contents CHAPTER 1: 1.1 1.2 1.3 1.4 1.5 INTRODUCTION MOTIVATION PROBLEM STATEMENT RESEARCH QUESTIONS AND OBJECTIVE 11 CONTRIBUTION TO THE BODY OF KNOWLEDGE 12 OUTLINE OF THE THESIS 16 CHAPTER 2: LITERATURE REVIEW 19 2.1 ANOMALY DETECTION 19 2.1.1 Anomaly Detection Techniques 20 2.1.1.1 2.1.1.2 2.1.1.3 Supervised Anomaly Detection 22 Semi-supervised Anomaly Detection Techniques 26 Unsupervised Anomaly Detection Techniques 26 2.1.2 Reporting Anomaly Detection 28 2.1.3 Summary 28 2.2 ANOMALY DETECTION AND ONLINE SOCIAL NETWORKS 29 2.2.1 Online behaviours 30 2.2.2 Type Of Anomalies 34 2.2.3 OSNs Anomaly Detection Challenges 36 2.2.3.1 2.2.4 Labelled Dataset 37 Anomaly Detection In Graph-Based Data 38 2.2.4.1 2.2.4.2 Anomaly In Static Large Data Graph 40 Graph Mining Algorithms 42 2.2.5 Summary 48 2.3 CLUSTERING ALGORITHMS 49 2.3.1 Semi-Unsupervised Clustering 51 2.3.2 Unsupervised Clustering 52 2.3.3 Fuzzy Clustering 52 2.3.4 Summary 53 2.4 FUZZY LOGIC 54 2.5 RESEARCH GAP 56 2.6 SUMMARY 58 CHAPTER 3: MULTI-LAYER FRAMEWORK 61 3.1 FRAMEWORK OVERVIEW 62 3.2 LAYER-ONE: PRE-PROCESSING, MODELLING, IDENTIFYING EGONETS, AND SUPER-EGONETS 64 3.2.1 Modelling OSNs Using Graph Theory 65 3.2.2 Features Extraction 67 3.1.1.1 3.1.1.2 3.1.1.3 3.1.1.4 Online Social Network Characteristics 69 Centrality Metrics 72 Community Detection 77 Cliqueness and Starness 79 3.3 LAYER-TWO OVERVIEW: ANOMALY DETECTION ALGORITHMS 79 3.3.1 Layer-Two (a): Distance-Based Anomaly Detection Using Graph Metrics 81 3.3.2 Layer-Two (b): Distribution-Based (Statistical-Based) Anomaly Detection Using Graph Metrics 82 3.3.3 Layer-Two (c): Clustering-Based Approach Using Graph Metrics 83 Anomaly Detection In Online Social Networks: Using data-mining Techniques and Fuzzy Logic v 3.4 LAYER-THREE: EVALUATION 84 3.4.1 Datasets 84 3.4.2 Labelled Dataset 87 3.4.3 Evaluation Measures .90 3.1.1.5 Coefficient Of Determination 91 3.4.4 Benchmark Based On Structural Behaviours In OSNs 92 3.4.5 Find Threshold 93 3.5 SUMMARY .94 CHAPTER 4: ANOMALY DETECTION METHODS 97 4.1 INTRODUCTION 97 4.2 DISTANCE-BASED APPROACH USING GRAPH METRICS AND ORTHOGONAL PROJECTION 100 4.2.1 Method Overview 101 4.2.2 Input-Computing Graph Metrics 102 4.2.3 Compute Regression Model 104 4.2.4 Computing The Distance From Regression Model 106 4.2.5 Compute Orthogonal Projection 107 4.2.6 Fuzzy C-Means (FCM) Clustering 110 4.3 DISTRIBUTION-BASED (STATISTICAL-BASED) APPROACH USING GRAPH METRICS .112 4.3.1 Method Overview 113 4.3.2 Input–Local Graph Properties 115 4.3.3 Clustering Preliminary Anomaly Score With Unsupervised Learning 115 4.3.4 Classification Using Fuzzy Inference Engine 119 4.4 CLUSTERING-BASED ANOMALY DETECTION IN ONLINE-SOCIAL-NETWORK GRAPHS 124 4.4.1 Method Overview 126 4.4.2 Input To Algorithm 128 4.4.3 Finding Cluster Number Using GMM-EM 128 4.4.4 Clustering Using Fuzzy C-means (FCM) 129 4.4.5 Representing Clusters With Fuzzy Inference Engine 131 4.5 SUMMARY 133 CHAPTER 5: EXPERIMENTS AND DISCUSSIONS 137 5.1 FRAMEWORK .138 5.1.1 Distance-Based Approach 140 5.1.1.1 5.1.1.2 5.1.1.3 5.1.2 Distribution-Based Approach 145 5.1.2.1 5.1.2.2 5.1.3 Experiment Design 140 Power-Law Regression Method Results 143 Orthogonal Projection Method Results 144 Experiment Design 145 Distribution Method Results 148 Clustering-Based Approach 151 5.1.3.1 5.1.3.2 Experiment Design 152 Clustering Method Results 153 5.2 EXPERIMENT RESULTS DISCUSSION 155 5.2.1 Power-Law Degree And Normal Instances Distribution .156 5.2.2 Strengths and Shortcomings of EACH Method 161 5.2.2.1 5.2.2.2 5.2.2.3 5.2.2.4 5.2.3 Performance Comparisons .163 5.2.3.1 5.2.3.2 5.2.3.3 5.2.3.4 5.2.3.5 vi Power-Law Regression Method 161 Orthogonal Projection Method 161 Distribution Method 162 Clustering Method 163 Dealing With Anomaly Detection Challenges 164 Distance-Based Method vs Clustring-Based Method 165 Distribution-Base Method vs Clustring-Based Method 167 Proposed Methods vs Benchmarking 168 Effectiveness Of Clustering And Orthogonal Projection 168 Anomaly Detection In Online Social Networks: Using data-mining Techniques and Fuzzy Logic 5.2.3.6 5.3 CHAPTER 6: 6.1 6.2 6.3 6.4 Top Performance Comparisons 169 SUMMARY 172 CONCLUSIONS 175 RESEARCH CONTRIBUTIONS 176 MAIN FINDINGS 179 ANSWERS TO RESEARCH QUESTIONS 183 FUTURE WORK 186 BIBLIOGRAPHY 189 Anomaly Detection In Online Social Networks: Using data-mining Techniques and Fuzzy Logic vii Bibliography Abe, N., Zadrozny, B., & Langford, J (2006) Outlier Detection by Active Learning In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp 504-509) ACM Aggarwal, C C (2013) Outlier Analysis Springer 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