CAMLIS

56 talks · 2018–2022 · indexed by Breach Arc Archive. Links open each talk on YouTube.

• Building a Multi-Tenant Machine Learning Workflow Orchestration Platform (CAMLIS 2022) 2022
• Detecting Homoglyph Domains with Character Image LSTMs (CAMLIS 2022) 2022
• Efficient Malware Analysis Using Metric Embeddings (CAMLIS 2022) 2022
• Enhancing 2FA with IP-based geolocation without blocking all your users (CAMLIS 2022) 2022
• Firenze: Model Evaluation Using Weak Signals (CAMLIS 2022) 2022
• Half-Day Vulnerabilities: A Study of the First Days of CVE Entries (CAMLIS 2022) 2022
• Heterogenous Graph Embedding for Malicious Azure Sign-in Detection (CAMLIS 2022) 2022
• Inroads in Autonomous Network Defence using Explained Reinforcement Learning (CAMLIS 2022) 2022
• Keynote: Lessons Learned in Red Teaming AI Systems in High-Stakes Environments (CAMLIS 2022) 2022
• Keynote: PE Binary Classification Pitfalls (CAMLIS 2022) 2022
• Keys to the Digital Castle: Detecting Malicious MDA Device Enrollment at Scale (CAMLIS 2022) 2022
• Learning to Embed Byte Sequences with Convolutional Autoencoders (CAMLIS 2022) 2022
• Minimizing Compute Costs: When When Should We Run More Expensive Malware Analysis? (CAMLIS 2022) 2022
• Network Security Modelling with Distributional Data (CAMLIS 2022) 2022
• Playing Cat and Mouse with the Attacker: Frequent Item Set Mining in the Registry (CAMLIS 2022) 2022
• Temporal Attack Detection in Multimodal Cyber-Physical Systems with Sticky HDP-HMM (CAMLIS 2022) 2022
• Webshell Detection Case Study (CAMLIS 2022) 2022
• Bonware to the Rescue: the Future Autonomous Cyber Defense Agents | Dr Alexander Kott | CAMLIS 2018 2018
• A feature presentation: semi-supervised learning of file representations
• Activation Analysis of a Byte-based Deep Neural Network for Malware Classification
• An Effective Framework for Malware Detection and Classification using Feature Prioritization
• An Information Security Approach to Feature Engineering
• Anticipatory Cyber Defense via Predictive Analytics, Machines Learning and Simulation
• Applications of Graph Integration to Function Comparison and Malware Classification
• Applying Deep Graph Representation Learning to the Malware Graph
• APTinder: An optimized approach for finding that perfect APT match
• Automated in-memory malware/rootkit detection via binary analysis and machine learning
• Datasets for the Everyman
• Describing Malware via Tagging
• Do You Know What Your ML Is Doing?
• EMBER Improvements
• Estimating uncertainty for binary classifiers
• Exploring Backdoor Poisoning Attacks Against Malware Classifiers
• Improved Multi-Stage Classification for Information Security Applications
• Inferring Model Families from Deployed Black Boxes
• Interpretation of Threat Prediction Model for SOC Analysts
• Labeling Red: Harvesting Labeled Data from Adversary Simulations
• Learning to Rank Relevant Malware Strings Using Weak Supervision
• Measure Twice, Quarantine Once: A Tale of Malware Labeling over Time
• Mitigating Adversarial Attacks against Machine Learning for Static Analysis
• Next Generation Process Emulation with Binee
• On Evaluating Adversarial Robustness
• Point process modeling of temporal patterns in user authentication behavior
• ProblemChild: Discovering Anomalous Patterns based on Parent-Child Process Relationships
• Protecting Users: When Security and Privacy Collide
• Scalable Infrastructure for Malware Labeling and Analysis
• Serverless Data Processing Architecture for Binary Analysis
• Some Mistakes are More Mistaken Than Others: Using Cost-Matrix Clustering to Address Misclassificati
• Towards a Trustworthy and Resilient Machine Learning Classifier - a Case Study of Ransomware Behavio
• TreeHuggr: Discovering where tree-based classifiers are vulnerable to adversarial attack
• Trying to Make Meterpreter into an Adversarial Example
• TweetSeeker: Extracting Adversary Methods from the Twitterverse
• Using Anomaly Detection on User Demographic Distributions to Identify Fake Account Bursts
• Using Lexical Features for Malicious URL Detection- A Machine Learning Approach
• What is the Shape of an Executable?
• Worm2Vec: Embedding Malicious Code for Efficient Clustering & Classification