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Part I Overview of Cyber Situational Awareness |
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Cyber SA: Situational Awareness for Cyber Defense |
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3 | (12) |
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Scope of the Cyber SA Problem |
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3 | (2) |
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5 | (1) |
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6 | (1) |
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7 | (6) |
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Principles and Rationales |
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7 | (1) |
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A Collection of Viewpoints on the Research Agenda |
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7 | (6) |
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13 | (1) |
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14 | (1) |
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Overview of Cyber Situation Awareness |
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15 | (24) |
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What is Situation Awareness (SA)? |
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15 | (3) |
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Situation Awareness Reference and Process Models |
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18 | (8) |
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Situation Awareness Reference Model |
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18 | (6) |
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Situation Awareness Process Model |
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24 | (2) |
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26 | (1) |
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Application to the Cyber Domain |
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27 | (1) |
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Measures of Performance and Effectiveness |
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28 | (6) |
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29 | (2) |
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31 | (1) |
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32 | (1) |
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33 | (1) |
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Measures of Effectiveness |
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33 | (1) |
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34 | (1) |
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34 | (5) |
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Part II The Reasoning and Decision Making Aspects |
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RPD-based Hypothesis Reasoning for Cyber Situation Awareness |
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39 | (12) |
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39 | (2) |
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Naturalistic Decision Making as a Holistic Model for Cyber SA |
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41 | (1) |
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41 | (1) |
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The Recognition-Primed Decision (RPD) Model |
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41 | (1) |
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RPD-based Hypothesis Generation and Reasoning for Cyber SA |
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42 | (3) |
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Recognition-based Hypothesis Generation |
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43 | (1) |
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Hypothesis-driven story Building |
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43 | (1) |
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Collaborative RPD-based Hypothesis Generation and Reasoning |
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44 | (1) |
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Hypergraph-based Hypothesis Reasoning |
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45 | (2) |
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Modeling Events as Network Entities |
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46 | (1) |
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Hypergraph-based Network Analysis Techniques |
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46 | (1) |
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Market-based Evidence Gathering |
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47 | (1) |
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48 | (1) |
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49 | (2) |
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Uncertainty and Risk Management in Cyber Situational Awareness |
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51 | (20) |
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Reasoning about Uncertainty is a Necessity |
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51 | (1) |
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Two Approaches to Handling Dynamic Uncertainty |
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52 | (1) |
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52 | (1) |
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53 | (1) |
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From Attack Graphs to Bayesian Networks |
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53 | (4) |
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54 | (1) |
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Desired properties of Bayesian Networks in Intrusion Analysis |
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55 | (1) |
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Building BN's from attack graphs |
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56 | (1) |
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An Empirical Approach to Developing a Logic for Uncertainty in Situation Awareness |
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57 | (6) |
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57 | (1) |
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Encoding the case study in logic |
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58 | (4) |
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Comparison with previous approaches |
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62 | (1) |
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Static Uncertainty and Risk Management |
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63 | (2) |
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63 | (2) |
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Combining CVSS and Attack Graphs |
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65 | (1) |
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65 | (1) |
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65 | (6) |
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Part III Macroscopic Cyber Situational Awareness |
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Employing Honeynets For Network Situational Awareness |
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71 | (32) |
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72 | (1) |
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73 | (1) |
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Classifying Honeynet Activity |
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74 | (2) |
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Experiences With Activity Classification |
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76 | (1) |
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Situational Awareness In-depth |
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77 | (7) |
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79 | (1) |
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Destination/Source Net Coverage |
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80 | (1) |
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81 | (3) |
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Towards Automated Classification |
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84 | (1) |
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Assessing Botnet Scanning Patterns |
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85 | (2) |
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85 | (1) |
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Checking for Liveness-Aware Scanning |
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86 | (1) |
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87 | (1) |
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87 | (1) |
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Extrapolating Global Properties |
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87 | (5) |
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Assumptions and Requirements |
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88 | (1) |
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Estimating Global Population |
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89 | (1) |
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Exploiting IPID/Port Continuity |
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90 | (2) |
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Extrapolating from Interarrival Times |
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92 | (1) |
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Evaluation of Automated Classification |
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92 | (9) |
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Basic Characteristics of Botnet Events |
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94 | (1) |
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95 | (1) |
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Property-Checking Results |
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95 | (3) |
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Extrapolation Evaluation & Validation |
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98 | (3) |
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101 | (1) |
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101 | (2) |
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Assessing Cybercrime Through the Eyes of the WOMBAT |
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103 | (36) |
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103 | (1) |
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104 | (1) |
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104 | (8) |
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104 | (1) |
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105 | (2) |
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107 | (1) |
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Some illustrative examples |
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108 | (4) |
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112 | (5) |
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Increasing the level of interaction |
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112 | (1) |
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112 | (1) |
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SGNET: a ScriptGen-based honeypot deployment |
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113 | (4) |
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Analysis of Attack Events |
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117 | (6) |
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Identification of Attack Events |
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117 | (2) |
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119 | (3) |
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Impact of Observation View Point |
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122 | (1) |
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Multi-Dimensional Analysis of Attack Events |
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123 | (6) |
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123 | (1) |
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124 | (3) |
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Combining Cliques of Attackers |
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127 | (2) |
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Beyond Events Correlation: Exploring the epsilon-gamma-pi-mu space |
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129 | (4) |
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130 | (1) |
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131 | (2) |
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133 | (1) |
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134 | (5) |
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Part IV Enterprise Cyber Situational Awareness |
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Topological Vulnerability Analysis |
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139 | (16) |
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139 | (1) |
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140 | (2) |
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142 | (3) |
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145 | (2) |
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Analysis and Visualization |
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147 | (2) |
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149 | (3) |
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152 | (1) |
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152 | (1) |
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153 | (2) |
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Cross-Layer Damage Assessment for Cyber Situational Awareness |
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155 | (24) |
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155 | (6) |
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A Multi-Level Damage Assessment Framework |
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156 | (3) |
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Existing Damage Assessment Techniques |
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159 | (1) |
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Focus of This Work: Damage Assessment Cross Instruction Level and OS Level |
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160 | (1) |
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PEDA: An Architecture For Fine-Grained Damage Assessment in a Production Environment |
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161 | (2) |
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VM-Based Cross-Layer Damage Assessment: An Overview |
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163 | (2) |
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163 | (1) |
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164 | (1) |
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165 | (1) |
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Design and Implementation |
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165 | (6) |
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Cross-Layer Damage Assessment when the Guest Kernel is Not Compromised |
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166 | (2) |
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Cross-Layer Damage Assessment when the Guest Kernel is Compromised |
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168 | (1) |
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``What-if'' Damage Assessment |
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169 | (2) |
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171 | (2) |
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Compromised Process Damage Assessment Experiment |
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171 | (1) |
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Malicious Kernel Module Experiment |
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172 | (1) |
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173 | (1) |
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174 | (1) |
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174 | (1) |
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174 | (5) |
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Part V Microscopic Cyber Situational Awareness |
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A Declarative Framework for Intrusion Analysis |
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179 | (22) |
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179 | (1) |
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180 | (7) |
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Forensic Analysis of Intrusions |
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180 | (2) |
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Recovery From and Remediation of Intrusions |
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182 | (1) |
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182 | (1) |
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183 | (1) |
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Event Collection and Processing Infrastructure |
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184 | (2) |
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Common Characteristics of Existing Techniques |
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186 | (1) |
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187 | (2) |
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188 | (1) |
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188 | (1) |
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Intrusion Analysis Framework |
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189 | (3) |
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Information Extraction and Normalization |
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190 | (1) |
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Event Correlation and Dependence Analysis |
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191 | (1) |
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Simplification and Refinement |
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192 | (1) |
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The Slog Declarative Programming Language |
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192 | (3) |
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Language Constructs and Syntax |
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192 | (2) |
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194 | (1) |
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195 | (1) |
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195 | (1) |
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195 | (1) |
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196 | (1) |
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197 | (4) |
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Automated Software Vulnerability Analysis |
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201 | (26) |
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201 | (2) |
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203 | (1) |
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MemSherlock: An Automated Debugger for Unknown Memory Corruption Vulnerabilities |
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203 | (8) |
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204 | (3) |
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Debugging Vulnerabilities |
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207 | (2) |
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Automated Debugging Using MemSherlock |
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209 | (2) |
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CBones: Security Debugging Using Program Structural Constraints |
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211 | (7) |
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Program Structural Constraints |
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212 | (2) |
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Security Debugging through Constraints Verification |
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214 | (2) |
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216 | (1) |
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216 | (1) |
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Security Debugging Using CBones |
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217 | (1) |
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218 | (1) |
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219 | (1) |
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219 | (8) |
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Part VI The Machine Learning Aspect |
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Machine Learning Methods for High Level Cyber Situation Awareness |
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227 | (22) |
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227 | (1) |
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228 | (4) |
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Tracking the User's Current Project |
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228 | (1) |
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229 | (3) |
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232 | (1) |
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Machine Learning for Project Associations |
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232 | (8) |
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232 | (2) |
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234 | (4) |
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238 | (2) |
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Discovering User Workflows |
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240 | (5) |
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Building the Information Flow Graph |
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242 | (1) |
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Mining the Information Flow Graph |
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242 | (1) |
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243 | (1) |
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243 | (2) |
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245 | (1) |
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246 | (1) |
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246 | (3) |
Author Index |
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249 | |