Tag Archives: Machine Learning

Neural Networks for Securing Vehicles

Probing the limits of anomaly detectors for automobiles with a cyber attack framework Adrian Taylor (Defence R&D Canada), Sylvain Leblanc (Royal Military College of Canada), and Nathalie Japkowicz (American University, USA) Modern vehicles are increasingly governed and controlled by a network of computers.  Automobile security requires that these networks are secure.  Detecting malicious traffic on …

Pierre-Luc Vaudry – Feeding the Machine: Data Collection and Other Challenges of Machine Learning for Spam Detection

Presented at the SERENE-RISC Workshop, 2017 October Spam detection software can use both handcrafted rules and machine learning techniques. At ZEROSPAM we are aiming at reducing the need to create or edit rules manually to adapt to constantly evolving email-borne threats. At the same time, the performance of our machine learning tools could be improved …

Adrian Taylor – Detecting anomalies on the automotive control bus with machine learning

Presented at the SERENE-RISC Workshop, 2017 October.  Detecting anomalies on the automotive control bus with machine learning. Cars are vulnerable to hacking. While automotive cyber attacks are not yet a widespread threat, learning how to detect them will be an important part of future countermeasures. Attacks must be crafted for specific models, so attack detectors must …

Benjamin Fung – Kam1n0 Assembly Clone Search for Reverse Engineering

Presented at the Spring 2016 SERENE-RISC Workshop. Assembly code analysis is one of the critical processes for mitigating the exponentially increasing threats from malicious software. It is also a common practice for detecting and justifying software plagiarism and software patent infringements when the source code is unavailable. However, it is a manually intensive and time-consuming …