Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media

Social media is often viewed as a sensor into various societal events such as disease outbreaks, protests, and elections. We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our approach detects a broad range of cyber-attacks (e.g., distributed denial of service (DDOS) attacks, data breaches, and account hijacking) in an unsupervised manner using just a limited fixed set of seed event triggers. A new query expansion strategy based on convolutional kernels and dependency parses helps model reporting structure and aids in identifying key event characteristics. Through a large-scale analysis over Twitter, we demonstrate that our approach consistently identifies and encodes events, outperforming existing methods.Comment: 13 single column pages, 5 figures, submitted to KDD 201

Robust Spammer Detection Using Collaborative Neural Network in Internet of Thing Applications

Spamming is emerging as a key threat to Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches; and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this paper, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based Spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multi-source information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world datasets under different scenario and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines

Contextual Semantics for Radicalisation Detection on Twitter

Much research aims to detect online radical content mainly using radicalisation glossaries, i.e., by looking for terms and expressions associated with religion, war, offensive language, etc. However, such crude methods are highly inaccurate towards content that uses radicalisation terminology to simply report on current events, to share harmless religious rhetoric, or even to counter extremism. Language is complex and the context in which particular terms are used should not be disregarded. In this paper, we propose an approach for building a representation of the semantic context of the terms that are linked to radicalised rhetoric. We use this approach to analyse over 114K tweets that contain radicalisation-terms (around 17K posted by pro-ISIS users, and 97k posted by “general” Twitter users). We report on how the contextual information differs for the same radicalisation terms in the two datasets, which indicate that contextual semantics can help to better discriminate radical content from content that only uses radical terminology.The classifiers we built to test this hypothesis outperform those that disregard contextual informatio