Silicon Valley Cybersecurity Conference: Third Conference, SVCC 2022, Virtual Event, August 17–19, 2022, Revised Selected Papers (Communications in Computer and Information Science, 1683)

This open access book constitutes refereed proceedings of the Third Conference on Silicon Valley Cybersecurity Conference, SVCC 2022, held as virtual event, in August 17–19, 2022. The 8 full papers included in this book were carefully reviewed and selected from 10 submissions. The contributions are divided into the following thematic blocks: Malware Analysis; Blockchain and Smart Contracts; Remote Device Assessment. This is an open access book. Preface Organization Contents Malware Analysis Robustness of Image-Based Malware Analysis 1 Introduction 2 Related Work 2.1 Color Images for Malware Analysis 2.2 Obfuscation 3 Background 3.1 Classifiers 3.2 Computing Environment 3.3 Dataset 3.4 Images from Malware 3.5 Obfuscating Malware Images 3.6 Gist Descriptors 4 Experiments and Results 4.1 Comparing Image Modes with CNN 4.2 Random Salting Experiments 4.3 Contiguous Salting 4.4 CNN Experiments Without Obfuscation 5 Conclusion and Future Work References Word Embeddings for Fake Malware Generation 1 Introduction 2 Background 2.1 Selective Survey of Related Work 2.2 Machine Learning Techniques 3 Methodology 3.1 Dataset 3.2 Training Procedure 3.3 Evaluation Procedure 4 Implementation 4.1 Feature Extraction 4.2 WGAN with Gradient Penalty 4.3 Evaluation Implementation 5 Results 5.1 Evaluation Score 5.2 Further Analysis 6 Conclusions and Future Work References Twitter Bots' Detection with Benford's Law and Machine Learning 1 Introduction 2 Background 2.1 Benford's Law 3 Related Work 4 Implementation 4.1 Dataset 4.2 Approach 4.3 Data Preprocessing 4.4 Training and Testing Classifiers 5 Results 5.1 Naïve Bayes 5.2 Logistic Regression 5.3 SVM 5.4 Random Forest 5.5 AdaBoost 5.6 Multi-layer Perceptron 5.7 Latency Analysis of ML Algorithms 5.8 Statistical Tests Majority Vote 6 Conclusions and Future Works 6.1 Future Works References Blockchain and Smart Contracts A Blockchain-Based Retribution Mechanism for Collaborative Intrusion Detection 1 Introduction 2 Related Work 2.1 Retribution Mechanism 2.2 Detection Signature Verification 3 Models 3.1 System Network Model 3.2 Threat Model 4 Design 4.1 System Architecture Overview 4.2 Efficiency Calculation Method 4.3 Distributed Verification Consensus 4.4 Retribution Mechanism 5 Implementation 6 Experiments 6.1 Computation Performance 6.2 Reward per Majority Verifier 6.3 Detection File Download Time Cost 6.4 Distributed Networking Overhead 7 Conclusion References Smart Contracts in the Cloud 1 Introduction 2 Background 2.1 Bitcoin Blockchain 2.2 Decentralized Applications 2.3 Permissioned Ledgers 2.4 Cloud Computing 3 Smart-Contracts in the Cloud 3.1 Storage Tiering 4 Conclusion and Future Work References A Blockchain-Based Tamper-Resistant Logging Framework 1 Introduction 2 Background and Related Work 3 Logger Design and Implementation 3.1 SpartanGold Overview 3.2 Logging Framework Codebase 3.3 Extensions 4 Experimental Results 4.1 Untampered Blockchain Dataset 4.2 Simple Attack 4.3 Subtle Attack 5 Discussion and Future Work References Remote Device Assessment Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks 1 Introduction 2 Exploratory Analysis of 5G Dataset 3 Performance Prediction 3.1 Binary Classification Performance 3.2 Multi-class Prediction Performance 4 Location Spoofing Attacks 5 Related Work 6 Conclusion References Deep IoT Monitoring: Filtering IoT Traffic Using Deep Learning 1 Introduction 2 Our Approach 2.1 Dataset 2.2 Approach Overview 2.3 Federated Learning Algorithms 3 Experiment Setup 4 Experiment Results 4.1 Federated Supervised Learning 4.2 Federated Unsupervised Learning 4.3 Centralized Supervised Learning 4.4 Centralized Unsupervised Learning 4.5 Performance Comparison 5 Conclusion References Author Index