Evaluation of feature learning for anomaly detection in network traffic

• Ahmed M, Mahmood AN, Hu J (2016) A survey of network anomaly detection techniques. J Netw Comput Appl 60:19–31

  Article  Google Scholar 

• Ambusaidi MA, He X, Nanda P, Tan Z (2016) Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans Comput 65(10):2986–2998

  Article  MathSciNet  Google Scholar 

• Angelov P (2014) Anomaly detection based on eccentricity analysis. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp 1–8, https://doi.org/10.1109/EALS.2014.7009497

• Angelov PP, Gu X (2019) Anomaly detection-empirical approach. Springer International Publishing, Cham, pp 157–173

  Google Scholar 

• Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

  Article  Google Scholar 

• Bhuyan MH, Bhattacharyya DK, Kalita JK (2014) Network anomaly detection: methods, systems and tools. IEEE Commun Surv Tutor 16(1):303–336

  Article  Google Scholar 

• Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. ACM Sigmod Record ACM 29:93–104

  Article  Google Scholar 

• Buczak AL, Guven E (2015) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176

  Article  Google Scholar 

• Chandola V, Banerjee A, Kumar V (2009) Anomaly detection—a survey. ACM Comput Surv 41(3):15:1–15:44. https://doi.org/10.1145/1541880.1541882

  Article  Google Scholar 

• Chen Y, Li Y, Cheng XQ, Guo L (2006) Survey and taxonomy of feature selection algorithms in intrusion detection system. In: International conference on information security and cryptology, Springer, New York, pp 153–167

• Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit 58:121–134

  Article  Google Scholar 

• Garcia-Teodoro P, Diaz-Verdejo J, Maciá-Fernández G, Vázquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28(1):18–28

  Article  Google Scholar 

• Giap CN, Son LH, Chiclana F (2018) Dynamic structural neural network. J Intell Fuzzy Syst 34(4):2479–2490

  Article  Google Scholar 

• Goldstein M, Dengel A (2012) Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm. In: Wölfl S (ed) Poster and Demo Track of the 35th German Conference on Artificial Intelligence (KI-2012), pp 59–63

• Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT press, Cambridge

  MATH  Google Scholar 

• Gu X, Angelov P (2017) Autonomous anomaly detection. In: 2017 evolving and adaptive intelligent systems (EAIS), pp 1–8, https://doi.org/10.1109/EAIS.2017.7954831

• Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(1):1157–1182

  MATH  Google Scholar 

• Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

  Article  MathSciNet  MATH  Google Scholar 

• Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323

  Article  Google Scholar 

• Javaid A, Niyaz Q, Sun W, Alam M (2016) A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), ICST, pp 21–26

• Jolliffe I (2011) Principal component analysis. Springer, New York

  MATH  Google Scholar 

• Khammassi C, Krichen S (2017) A GA-LR wrapper approach for feature selection in network intrusion detection. Comput Secur 70:255–277

  Article  Google Scholar 

• Khan L, Awad M, Thuraisingham B (2007) A new intrusion detection system using support vector machines and hierarchical clustering. VLDB J 16(4):507–521

  Article  Google Scholar 

• Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. CoRR abs/1412.6980,

• Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:13126114

• Lee JA, Verleysen M (2007) Nonlinear dimensionality reduction. Springer Science & Business Media, New York

  Book  Google Scholar 

• Liu FT, Ting KM, hua Zhou Z (2008) Isolation forest. In: In ICDM ’08: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. IEEE Computer Society, pp 413–422

• Lvd Maaten, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

  MATH  Google Scholar 

• Madhawa S, Balakrishnan P, Arumugam U (2018) Employing invariants for anomaly detection in software defined networking based industrial internet of things. J Intell Fuzzy Syst (Preprint):1–13

• Mahoney MV, Chan PK (2003) An analysis of the 1999 DARPA/Lincoln laboratory evaluation data for network anomaly detection. In: International Workshop on Recent Advances in Intrusion Detection, Springer, New York, pp 220–237

• Marir N, Wang H, Feng G, Li B, Jia M (2018) Distributed abnormal behavior detection approach based on deep belief network and ensemble svm using spark. IEEE Access 6:59657–59671

  Article  Google Scholar 

• Martins RS, Angelov P, Sielly Jales Costa B (2018) Automatic detection of computer network traffic anomalies based on eccentricity analysis. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–8, 10.1109/FUZZ-IEEE.2018.8491507

• McHugh J (2000) Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans Inf Syst Secur (TISSEC) 3(4):262–294

  Article  Google Scholar 

• Mirsky Y, Doitshman T, Elovici Y, Shabtai A (2018) Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:180209089

• Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Military Communications and Information Systems Conference (MilCIS), 2015, IEEE, pp 1–6

• Muda Z, Yassin W, Sulaiman M, Udzir NI et al (2011) A k-means and naive bayes learning approach for better intrusion detection. Inf Technol J 10(3):648–655

  Article  Google Scholar 

• Nguyen MN, Vien NA (2018) Scalable and interpretable one-class SVMs with deep learning and random fourier features. arXiv preprint arXiv:180404888

• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

  MathSciNet  MATH  Google Scholar 

• Pérez D, Alonso S, Morán A, Prada MA, Fuertes JJ, Domínguez M (2019) Comparison of network intrusion detection performance using feature representation. In: Macintyre J, Iliadis L, Maglogiannis I, C J (eds) International Conference on Engineering Applications of Neural Networks. Communications in Computer and Information Science, vol. 1000, Springer, pp 463–475

• Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:14014082

• Ring M, Wunderlich S, Scheuring D, Landes D, Hotho A (2019) A survey of network-based intrusion detection data sets. Comput Secur 86:147–167

  Article  Google Scholar 

• Ringberg H, Soule A, Rexford J, Diot C (2007) Sensitivity of PCA for traffic anomaly detection. ACM SIGMETRICS Perform Eval Rev ACM 35:109–120

  Article  Google Scholar 

• Rousseeuw PJ, Driessen KV (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3):212–223

  Article  Google Scholar 

• Rubio JdJ, Cruz D, Elias Barrón I, Ochoa G, Balcazarand R, Aguilar A (2019) ANFIS system for classification of brain signals. J Intell Fuzzy Syst 37:4033–4041. https://doi.org/10.3233/JIFS-190207

  Article  Google Scholar 

• Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471

  Article  Google Scholar 

• Sharafaldin I, Lashkari AH, Ghorbani AA (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, pp 108–116

• Sommer R, Paxson V (2010) Outside the closed world: On using machine learning for network intrusion detection. In: 2010 IEEE symposium on security and privacy, IEEE, pp 305–316

• Song J, Takakura H, Okabe Y, Eto M, Inoue D, Nakao K (2011) Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation. In: Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, ACM, pp 29–36

• Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications 2009

• Vinayakumar R, Alazab M, Soman K, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525–41550

  Article  Google Scholar 

• Wang K, Stolfo SJ (2004) Anomalous payload-based network intrusion detection. In: International Workshop on Recent Advances in Intrusion Detection, Springer, pp 203–222

• Yousefi-Azar M, Varadharajan V, Hamey L, Tupakula U (2017) Autoencoder-based feature learning for cyber security applications. In: 2017 International joint conference on neural networks (IJCNN), IEEE, pp 3854–3861

• Zhang Z, Li J, Manikopoulos C, Jorgenson J, Ucles J (2001) HIDE: a hierarchical network intrusion detection system using statistical preprocessing and neural network classification. In: Proc. IEEE Workshop on Information Assurance and Security, pp 85–90

Download references