Framework for experimental analysis of adversarial example attacks on policy learning in Deep RL. Attack methodologies are based on our paper “Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks” (Behzadan & Munir, 2017 – https://arxiv.org/abs/1701.04143 ).
A framework for detection and classification of cyber threat indicators in the Twitter stream. Contrary to the bulk of similar proposals that rely on manually-designed heuristics and keywordbased filtering of tweets, our framework provides a data-driven approach for modeling and classification of tweets that are related to cybersecurity events. We present a cascaded Convolutional Neural Network (CNN) architecture, comprised of a binary classifier for detection of cyber-related tweets, and a multi-class model for the classification of cyber-related tweets into multiple types of cyber threats. Furthermore, we present an open-source dataset of 21000 annotated cyber-related tweets to facilitate the validation and further research in this area.
TrolleyMod is an open-source platform based on the CARLA simulator for the collection of ethical decision-making data for autonomous vehicles. This platform is designed to facilitate experiments aiming to observe and record human decisions and actions in high-fidelity simulations of ethical dilemmas that occur in the context of driving. Targeting experiments in the class of trolley problems, TrolleyMod provides a seamless approach to creating new experimental settings and environments with the realistic physics-engine and the high-quality graphical capabilities of CARLA and the Unreal Engine. Also, TrolleyMod provides a straightforward interface between the CARLA environment and Python to enable the implementation of custom controllers, such as deep reinforcement learning agents. The results of such experiments can be used for sociological analyses, as well as the training and tuning of value-aligned autonomous vehicles based on social values that are inferred from observations.
More information is provided in our paper: https://arxiv.org/abs/1811.05594
I am a (minor) contributor to Nettacker. OWASP Nettacker project aims to automate information gathering, vulnerability scanning, and report generation for networked systems, covering services, bugs, vulnerabilities, misconfigurations, and other security-related information. This software utilizes TCP SYN, ACK, ICMP and many other protocols in order to detect and bypass Firewall/IDS/IPS devices. Leveraging a unique method, Nettacker is able to discover protected services and devices such as SCADA. It provides make a competitive edge in automated pentesting.