CAMLIS 2019 Oct. 24, 2019 to Oct. 24, 2019, Washington, USA

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Title Speakers Summary Topic Types
Keynote: Protecting Users: When Security and Privacy Collide ( Aleatha Parker-wood Machine learning for security is data hungry, and the scope of the data used is ...
Keynote: On Evaluating Adversarial Robustness Nicholas Carlini Several hundred papers have been written over the last few years proposing defenses to adversarial ...
Trying to Make Meterpreter into an Adversarial Example Andy Applebaum While machine learning has put previously hard-to-solve problems within reach, recent research has shown that ...
Scalable Infrastructure for Malware Labeling and Analysis Konstantin Berlin One of the best-known secrets of machine learning (ML) is that the most reliable way ...
TweetSeeker: Extracting Adversary Methods from the Twitterverse Matthew Berninger Like it or not, Twitter is a useful cybersecurity resource. Every day, cybersecurity practitioners share ...
Applying Deep Graph Representation Learning to the Malware Graph C. Bayan Bruss Malware is widespread, both increasing in its ubiquity but also growing in diversity. This poses ...
CNN-Based Malware Visualization and Explainability Lara Dedic , Matthew Teschke Manually determining the malware-like characteristics of an executable using signature and behavioral based identifiers has ...
Describing Malware via Tagging Felipe Ducau Although powerful for conviction of malicious artifacts, machine learning based detection do not generally produce ...
Mitigating Adversarial Attacks against Machine Learning for Static Analysis David Elkind Computer security increasingly leverages machine learning to detect malware. This is not without risks. Machine ...
ProblemChild: Discovering Anomalous Patterns based on Parent-Child Process Relationships Bobby Filar , David French It is becoming more common that malware attacks are not just a standalone executable or ...
What is the Shape of an Executable? Erick Galinkin The empirical success of neural networks in fields such as natural language processing and computer ...
Using Lexical Features for Malicious URL Detection- A Machine Learning Approach Apoorva Joshi Background: Malicious websites are responsible for a majority of the cyber-attacks and scams today. Malicious ...
An Information Security Approach to Feature Engineering Brian Murphy Feature engineering in data science is central to obtaining satisfactory results from deep learning models. ...
Next Generation Process Emulation with Binee Jared Nishikawa The capability to emulate x86 and other architectures has been around for some time. Malware ...
EMBER Improvements Phil Roth Endgame released an update to the EMBER dataset that includes updated features and an new ...
Exploring Backdoor Poisoning Attacks Against Malware Classifiers Scott E. Coull , Giorgio Serveri , Jim Meyer Antivirus vendors often rely on crowdsourced threat feeds, such as VirusTotal and ReversingLabs, to provide ...
Applications of Graph Integration to Function Comparison and Malware Classification Michael Slawinski We classify .NET files as either benign or malicious by examining directed graphs derived from ...
Learning to Rank Relevant Malware Strings Using Weak Supervision Michael Sikorski , Philip Tully , Matthew Haigh , Jay Gibble In static analysis, one of the most useful initial steps is to inspect a binary's ...
Towards a Trustworthy and Resilient Machine Learning Classifier - a Case Study of Ransomware Behavior Detector Evan C Yang The crypto-ransomware is a type of malware which hijacks user’s resources and demands for a ...
Privacy-preserving Surveillance Methods using Homomorphic Encryption Bill Buchanan , William Bowditch , Will Abramson , Nikolaos Pitropakis , Adam Hall Data analysis and machine learning methods often involve the processing of cleartext data, and where ...
Supervised/unsupervised cross-over method for autonomous anomaly classification Neil Caithness Classical threat detection relies on rule-based systems that are often too rigid for rapid changes ...
Detecting Unexpected Network Flows with Streaming Graph Clustering Andrew Fast When assessing questionable network traffic, network security practitioners focus on answering the classic investigative questions ...
On the OTHER Application of Graph Analytics for Insider Threat Detection Nahid Farhady Ghalaty , Ana Cruz Insider threat detection is a growing challenge for organizations. Insider threat is defined as “the ...
Cyber-Adversary Behavior Extraction and Comparisons Using IDS Alert Logs Stephen Moskal Computer networks are under constant threat from cyber attackers as adversaries from anywhere in the ...
Canopy: A Learning-Based Approach for Automatic Low-Volume DDoS Mitigation Brad Moore , Tony Wong , Banjo Obayomi , Chris Todd , Lucas Cadalzo In a low-volume distributed denial-of-service (LVDDoS) attack, an adversary attempts to overwhelm the server by ...
Predicting Exploitability: Forecasts for Vulnerability Management Michael Roytman Security is all about reacting. It’s time to make some predictions. We explain how Kenna ...
The Secret Life of Pwns: Characterizing and Predicting Exploit Weaponization Tudor Dumitras , Octavian Suciu , Erin Avllazagaj In recent years it has become challenging to weaponize the exploits of software vulnerabilities, so ...
Serverless Machine Learning for Phishing Scott Rodgers Phishing emails are one of the largest issues Cybersecurity professionals face today. An errant user ...
Adversarial Attacks against Malware N-gram Machine Learning Classifiers Emily Rogers Despite astonishing success in complex pattern recognition, machine learning classifiers have yet to be fully ...
Towards A Public Dataset/Benchmark for ML-Sec Richard Harang , Ethan Rudd While machine learning for information security (ML-Sec) has taken off in recent years, as a ...
Phish Language Processing (PhishLP) Santhosh Kumar Ramachandran As attacks get more sophisticated, detecting these threats pose innumerable challenges. Spear Phishing is one ...
Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model Approach (pdf) Sagar Samtani The Dark Web has emerged as a valuable source to proactively develop cyber threat intelligence ...
Evaluating the Potential Threat of Generative Adversarial Models to Intrusion Detection Systems Conrad Tucker Signature-based Intrusion Detection Systems (IDS) use pre-defined signatures of malware activity to identify malware, and ...