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On the Variety and Veracity of Cyber Intrusion Alerts Synthesized by Generative Adversarial Networks
ACM Transactions on Management Information Systems, Volume 11, Issue 4•December 2020, Article No.: 22, pp 1-21 • https://doi.org/10.1145/3394503Many cyber attack actions can be observed, but the observables often exhibit intricate feature dependencies, non-homogeneity, and potentially rare yet critical samples. This work tests the ability to learn, model, and synthesize cyber intrusion alerts ...
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research-articleNovember 2019
Synthetic Intrusion Alert Generation through Generative Adversarial Networks
MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), pp 1-6• https://doi.org/10.1109/MILCOM47813.2019.9020850Cyber Intrusion alerts are commonly collected by corporations to analyze network traffic and glean information about attacks perpetrated against the network. However, datasets of true malignant alerts are rare and generally only show one potential attack ...
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research-article
Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks
Cyber attacks infiltrating enterprise computer networks continue to grow in number, severity, and complexity as our reliance on such networks grows. Despite this, proactive cyber security remains an open challenge as cyber alert data is often not available for study. Furthermore, the data that is available is stochastically distributed, imbalanced, lacks homogeneity, and ...
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research-article
Differentiating and Predicting Cyberattack Behaviors Using LSTM
Ian
Perry, - Lutzu
Li,
- Christopher
Sweet,
- Shao-Hsuan
Su,
- Fu-Yuan
Cheng,
- Shanchieh
Jay Yang,
- Ahmet
Okutan
Classifying and predicting cyberattack behaviors are outstanding challenges due to the changing and broad attack surfaces as attackers penetrate into enterprise networks. The rise of Recurrent Neural Networks (RNNs) for temporally structured data in machine learning presents an opportunity to address these challenges ...
