Intro
Preface
Organization
Contents
Part IV
Anomaly Detection and Malware
Anomaly Detection: How to Artificially Increase Your F1-Score with a Biased Evaluation Protocol
1 Introduction
2 Related Work
3 Issues When Using F1-Score and AVPR Metrics
3.1 Formalism and Problem Statement
3.2 Definition of the Metrics
3.3 Evaluation Protocols: Theory vs Practice
3.4 Metrics Sensitivity to the Contamination Rate of the Test Set
3.5 How to Artificially Increase Your F1-Score and AVPR
3.6 F1-Score Cannot Compare Datasets Difficulty
4 Call for Action

4.1 Use AUC
4.2 Do Not Waste Anomalous Samples
5 Conclusion
References
Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional Data
1 Introduction
2 Related Work
3 Definitions and Notation
4 System Architecture
4.1 Subspace Searching Module
4.2 Discord Mining Module
4.3 Discussion
5 Evaluation
5.1 Alternative Approaches
5.2 Synthetic Data
5.3 Real-World Transactional Data
6 Conclusion
References
AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learning
1 Introduction
2 Related Work

2.1 Reinforcement Learning
2.2 Further Approaches
3 AIMED-RL
3.1 Framework and Notation
3.2 Experimental Setting
3.3 Environment
4 Experimental Results
4.1 Diversity of Perturbations
4.2 Evasion Rate
5 Availability
6 Conclusion
References
Learning Explainable Representations of Malware Behavior
1 Introduction
2 Related Work
3 Problem Setting and Operating Environment
3.1 Network Events
3.2 Identification of Threats
3.3 Data Collection and Quantitative Analysis
4 Models
4.1 Architectures
4.2 Unsupervised Pre-training
5 Experiments

5.1 Hyperparameter Optimization
5.2 Malware-Classification Performance
5.3 Indicators of Compromise
6 Conclusion
References
Strategic Mitigation Against Wireless Attacks on Autonomous Platoons
1 Introduction
1.1 Related Work
2 Message Falsification Attacks Against Platoons
2.1 Vehicular Platoon Control Policy
2.2 Attack Model
2.3 Attack Detection Algorithm
3 Security Game-Based Mitigation Framework
3.1 Numerical Example
4 Simulation Setup
5 Simulation Results and Discussion
5.1 Realistic Driving Scenario
6 Conclusion
References

DeFraudNet: An End-to-End Weak Supervision Framework to Detect Fraud in Online Food Delivery
1 Introduction
2 Related Work
3 The Framework: DeFraudNet
3.1 Problem Definition
3.2 Fraud Detection Pipeline
4 Data and Feature Processing
4.1 Dataset
4.2 Feature Engineering
5 Label Generation
5.1 Generating Noisy Labels Using LFs
5.2 Snorkel Generative Model
5.3 Class-Specific Autoencoders for Denoising
6 Discriminator Models
6.1 Multi Layer Perceptron
6.2 LSTM Sequence Model
7 Deployment and Serving Infrastructure
8 Ablation Experiments