Towards Botnet hazard analysis with generative adversarial networks for threat detection

Acklyn Murray; Danda B. Rawat

doi:10.1117/12.2584153

12 April 2021 Towards Botnet hazard analysis with generative adversarial networks for threat detection

Acklyn Murray, Danda B. Rawat

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117460Y (2021) https://doi.org/10.1117/12.2584153
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Generative Adversarial Network (GAN) is regarded as a class of generative models that relies on unsupervised machine learning. In this paper work, we design and evaluate a novel optimized GAN model with anomaly detection algorithm and compare it with a base signature anomaly GAN detection model. Typical anomaly detection algorithms that depend on the generative hostile network that are expected to produce robust and accurate detection of anomalies in a scalable number of data points, result in decrease in accuracy because of lack of enough data or unfiltered resampling data techniques. Thus, unlike in traditional neural networks for feature identification, our proposed approach leverages anomaly and mining prediction GAN ingestion with unique monitoring engine features for enhancing the identification, system resiliency, and overall performance with weight updates for the discriminator and generator models. Performance evaluation results show that the proposed approach results in better detection over the state-of-the-art predictive botnet detection.

Conference Presentation

Citation Download Citation

Acklyn Murray and Danda B. Rawat "Towards Botnet hazard analysis with generative adversarial networks for threat detection", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117460Y (12 April 2021); https://doi.org/10.1117/12.2584153

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