Abstract
We present an architecture, an Anomaly-based Generative Adversarial Network (IGAN), that detects malicious strings with decent accuracy. The proposed IGAN is comprised of an encoder, a decoder, and a discriminator. The encoder and decoder form the generative unit, trying to reconstruct the input and map that input and output to a latent space variable. IGAN exploits this latent space together with the adversarial training with the discriminator to enhance the learning of the normal distribution. The discriminator used is as a classifier as well as a feature extractor. To identify an anomaly or a normal instance, the anomaly score is calculated after passing it to the trained model. We have performed a detailed analysis of the existing and the proposed architecture using reliable metrics such as AUC score, Precision, Recall, and F_score. The experimental results of the proposed IGAN outperform other existing models in detecting anomalies with high accuracy.
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References
Goodfellow, I., Abadie, J.P., Mirza, M., Xu, B., Farley, D.W., Ozair, S., Courville, A., Bengi, Y.: Generative Adversarial Nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Schlegl, T., Seebock, P., Waldstein, S.M., Langs, G., Erfurth, U.S.: f-anogan: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)
Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.R.: Adversarially learned anomaly detection. In: Proceedings of IEEE International Conference on Data Mining, pp. 727–736 (2018)
Gherbi, E., Hanczar, B., Janodet, J.C., Klaudel, W.: An encoding adversarial network for anomaly detection. In: Proceedings of Asian Conference on Machine Learning, pp. 188–203 (2019)
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: semi-supervised anomaly detection via adversarial training. In: Proceedings of Asian Conference on Computer Vision, pp. 622–637 (2018)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders (2015). arXiv: 1511.05644
Dhanabal, L., Shantharajah, S.P.: A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 4(6), 446–452 (2015)
Kingma, D.P., Adam, J.B.: A method for stochastic optimization (2014). arXiv:1412.6980
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic curve. Radiology 143(1), 29–36 (1982)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A Detailed Analysis of the KDD CUP 99 data set. In: Proceedings of IEEE Symposium on Computational Intelligence for Security and Defense Applications (2009)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN 1701, 07875 (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein GANs. In: Proceedings of Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. Med. Image Anal. 58 (2019)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved Techniques for Training GANs. In: Proceedings of Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)
Bojanowski, P., Joulin, A., Paz, D.L., Szlam, A., Optimizing the latent space of generative networks (2017). arXiv:1707.05776
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. 1511, 05644 (2015)
Davis, J., Goadrich, M.: The Relationship between Precision-Recall and ROC curves. In: Proceedings of the international Conference on Machine learning, pp. 233–240 (2006)
Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In: Proceedings of European Conference on Information Retrieval, pp. 345–359 (2005)
Hanley, J., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). arXiv:1511.06434
LeCun, Y., Cortes, C.: MNIST handwritten digit database. http://yann.lecun.com/exdb/mnist/
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
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Shah, J., Das, M. (2022). IGAN: Intrusion Detection Using Anomaly-Based Generative Adversarial Network. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_36
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DOI: https://doi.org/10.1007/978-981-16-2008-9_36
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