Munindar Singh

Tanisha Khurana, Kaushik Pillalamarri, Vikram Pande et al.

This paper explores humor detection through a linguistic lens, prioritizing syntactic, semantic, and contextual features over computational methods in Natural Language Processing. We categorize features into syntactic, semantic, and contextual dimensions, including lexicons, structural statistics, Word2Vec, WordNet, and phonetic style. Our proposed model, Colbert, utilizes BERT embeddings and parallel hidden layers to capture sentence congruity. By combining syntactic, semantic, and contextual features, we train Colbert for humor detection. Feature engineering examines essential syntactic and semantic features alongside BERT embeddings. SHAP interpretations and decision trees identify influential features, revealing that a holistic approach improves humor detection accuracy on unseen data. Integrating linguistic cues from different dimensions enhances the model's ability to understand humor complexity beyond traditional computational methods.

Iffat Anjum, Mu Zhu, Isaac Polinsky et al.

Historically, enterprise network reconnaissance is an active process, often involving port scanning. However, as routers and switches become more complex, they also become more susceptible to compromise. From this vantage point, an attacker can passively identify high-value hosts such as the workstations of IT administrators, C-suite executives, and finance personnel. The goal of this paper is to develop a technique to deceive and dissuade such adversaries. We propose HoneyRoles, which uses honey connections to build metaphorical haystacks around the network traffic of client hosts belonging to high-value organizational roles. The honey connections also act as network canaries to signal network compromise, thereby dissuading the adversary from acting on information observed in network flows. We design a prototype implementation of HoneyRoles using an OpenFlow SDN controller and evaluate its security using the PRISM probabilistic model checker. Our performance evaluation shows that HoneyRoles has a small effect on network request completion time and our security analysis demonstrates that once an alert is raised, HoneyRoles can quickly identify the compromised switch with high probability. In doing so, we show that a role-based network deception is a promising approach for defending against adversaries that have compromised network devices.