Machine learning (ML) models are commonly used to detect hate speech, which is considered one of the main challenges of online social networks. However, ML models have been shown to be vulnerable to well-crafted input samples referred to as adversarial examples. In this paper, we present an adversarial attack against hate speech detection models and explore the attack’s ability to: (1) prevent the detection of a hateful user, which should result in termination of the user’s account, and (2) classify normal users as hateful, which may lead to the termination of a legitimate user’s account. The attack is targeted at ML models that are trained on tabular, heterogeneous datasets (such as the datasets used for hate speech detection) and attempts to determine the minimal number of the most influential mutable features that should be altered in order to create a successful adversarial example. To demonstrate and evaluate the attack, we used the open and publicly available “Hateful Users on Twitter” dataset. We show that under a black-box assumption (i.e., the attacker does not have any knowledge on the attacked model), the attack has a 75% success rate, whereas under a white-box assumption (i.e., the attacker has full knowledge on the attacked model), the attack has an 88% success rate.

Attack graphs are one of the main techniques used to automate the cybersecurity risk assessment process. In order to derive a relevant attack graph, up-to-date information on known cyber attack techniques should be represented as interaction rules. However, designing and creating new interaction rules is a time consuming task performed manually by security experts. We present a novel, end-to-end, automated framework for modeling new attack techniques from the textual description of security vulnerabilities. Given a description of a security vulnerability, the proposed framework first extracts the relevant attack entities required to model the attack, completes missing information on the vulnerability, and derives a new interaction rule that models the attack; this new rule is then integrated within the MulVal attack graph tool. The proposed framework implements a novel data science pipeline that includes a dedicated cybersecurity linguistic model trained on the NVD repository, a recurrent neural network model used for attack entity extraction, a logistic regression model used for completing the missing information, and a transition probability matrix for automatically generating new interaction rule. We evaluated the performance of each of the individual algorithms, as well as the complete framework, and demonstrated its effectiveness.