Description
Book SynopsisFuzzing and IoT security.- Fast Firmware Fuzz with Input/Output Reposition.- ChipFuzzer: Towards Fuzzing Matter based IoT Devices for Vulnerability Detection.- Reusablity Evaluation of Reports in Security Operation Centers for IoT with Sentence ALBERT and Jaccard Similarity.- Multi Server Publicly Verifiable Computation of polynomials.- Siamese Neural Network for Robust IoT Device Type Identification: A Few Shot Learning Approach.- FISFuzzer: A Grey Box Protocol Fuzzer Based on Field Inference and Scheduling.- Malware and Attack Analysis.- Mal POBM: A Genetic Algorithm for Malware Adversarial Sample Generation.- Graphite: Real Time Graph Based Detection of Windows Fileless Malware Attacks.- Robust Network Intrusion Detection via Semi-Supervised Deep Reinforcement Learning.- BinSimDB: Benchmark Dataset Construction for Fine Grained Binary Code Similarity Analysis.- OnionPeeler: A Novel Input Enriched Website Fingerprinting Attack on Tor Onion Services.- Web Security.- Beyond the Public Mempool: Catching DeFi Attacks Before They Happen with Real Time Smart Contract Analysis.- Ensuring Integrity in Online Content Usage and Download Counting with Smart Contracts.-ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection.- EasyCSPeasy: A Server side and Language agnostic XSS Mitigation by Devising and Ensuring Compliance with CSP.- Encoder based Multimodal Ensemble Learning for High Compatibility and Accuracy in Phishing Website Detection.- Bag of Characters: A Multiple Instance Learning Framework for URL Embedding in Web Security.- AutoS2ploit: From Automotive Safety Critical Functionalities to Security Exploit.- Pitfalls of data masking techniques: Re-identification attacks.- Faster Three Party Constant round Comparison with Application in Neural Network Inference.- WtLDP:Generating Synthetic Decentralized Weighted Graphs with Local Differential Privacy.- Tarnhelm: Using Adversarial Samples to Protect User Privacy Against Traffic Identification.
