Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data

By Joshua Glasser, Brian Lindauer. 2013 IEEE Security and Privacy Workshops, 2013

307 citations recorded by the source corpus.

The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hinderance. We outline the use of synthetic data to enable progress in one research program, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, as well as future research directions.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/696938f50404661092c3cad68ef8efa25d76f647

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Citations in the corpus

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions (2021, background): “[64], EnronSpam [13], SpamAssassin [19], LingSpam [17], DGA [1] [2] [12] [181], Malware Genome project [184], Virus Share [20], VirusTotal [21], Comodo [7], Contagio”
Anomaly detection in online social networks (2014, methodology): “In contrast, agent-based models (alternatively, individual-based models) such as that proposed by Menges et al. (2008) (see also Glasser and Lindauer (2013); Hummel et al. (2012)) describe the speciﬁc actions taken by individuals in the underlying system that lead to the formation of a social…”
Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions (2022, background): “[146], CERT [84] [48], EnronSpam [12], SpamAssassin [17], LingSpam [15], DGA [1] [2] [11] [151], Malware Genome project [155], Virus Share [18], VirusTo-”
A Survey of Deep Learning Methods for Cyber Security (2019, unknown): “2 [71,72].”
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams (2017, methodology): “We use the popular Long Short-Term Memory (LSTM) RNN architecture (Hochreiter and Schmidhuber 1997), in which the hidden state hut at time t is a function of a longterm memory cell, cut .”