Abstract
Nowadays computer and mobile devices, such as mobile phones, smartphones, smartwatches, tablets, etc., represent the multimedia diary of each of us. Thanks to technological evolution and the advent of an infinite number of applications, mainly aimed at socialization and entertainment, they have become the containers of an infinite number of personal and professional information. For this reason, optimizing the performance of systems able to detect intrusions (IDS - Intrusion Detection System) is a goal of common interest. This paper presents a methodology to classify hacking attacks taking advantage of the generalization property of neural networks. In particular, in this work we adopt the multilayer perceptron (MLP) model with the back-propagation algorithm and the sigmoidal activation function. We analyse the results obtained using different configurations for the neural network, varying the number of hidden layers and the number of training epochs in order to obtain a low number of false positives. The obtained results will be presented in terms of type of attacks and training epochs and we will show that the best classification is carried out for DOS and Probe attacks.
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References
Przemysław, K., Zbigniew, K.: Adaptation of the neural network-based IDS to new attacks detection, Warsaw University of Technology
Laheeb, M.I., Dujan, T.B.: A comparison study for intrusion database. J. Eng. Sci. Technol. 8(1), 107–119 (2013)
Heba, E.I., Sherif, M.B., Mohamed, A.S.: Adaptive layered approach using machine. Int. J. Comput. Appl. 56(7), 0975–8887 (2012)
Alfantookh, A.A.: DoS Attacks Intelligent Detection using Neural Networks. King Saud University, Arabia Saudita (2005)
Barapatre, P., Tarapore, N.: Training MLP Neural Network to Reduce False Alerts in IDS, Pune, India
Minsky, M.L., Papert, S.A.: Perceptrons: An Introduction to Computational Geometry. The MIT Press, Cambridge (1969)
Intrusion detection system. Wikipedia.it. https://it.wikipedia.org/wiki/Intrusion_detection_system
Network intrusion detection system. Wikipedia.it. https://it.wikipedia.org/wiki/Network_intrusion_detection_system
Grippo, L., Sciandrone, M.: Metodi di ottimizzazione per le reti neurali, Roma, Italia
University Of California, 28 October 1999. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Amato, F., Moscato, F.: A model driven approach to data privacy verification in E-health systems. Trans. Data Priv. 8(3), 273–296 (2015)
Amato, F., Moscato, F.: Pattern-based orchestration and automatic verification of composite cloud services. Comput. Electr. Eng. 56, 842–853 (2016)
Moscato, F.: Model driven engineering and verification of composite cloud services in MetaMORP(h)OSY. In: Proceedings - 2014 International Conference on Intelligent Networking and Collaborative Systems, INCoS 2014, pp. 635–640. IEEE (2014). Article no. 7057162
Aversa, R., Di Martino, B., Moscato, F.: Critical systems verification in MetaMORP(h)OSY. In: Bondavalli, A., Ceccarelli, A., Ortmeier, F. (eds.) SAFECOMP 2014. LNCS, vol. 8696, pp. 119–129. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10557-4_15
Minutolo, A., Esposito, M., De Pietro, G.: Development and customization of individualized mobile healthcare applications. In: 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom), pp. 321–326. IEEE (2012)
Sannino, G., De Pietro, G.: An evolved ehealth monitoring system for a nuclear medicine department. In: Developments in E-systems Engineering (DeSE 2011). IEEE (2011)
Cuomo, S., De Pietro, G., Farina, R., Galletti, A., Sannino, G.: A revised scheme for real time ECG signal denoising based on recursive filtering. Biomed. Signal Process. Control. 27, 134–144 (2016)
Coronato A., De Pietro G., Sannino, G.: Middleware services for pervasive monitoring elderly and ill people in smart environments. In: 2010 Seventh International Conference on Information Technology: New Generations (ITNG). IEEE (2010)
Vivenzio, E.: Reti neurali: Il percettrone multilivello. Thesis. University of Naples “Federico II” (2017)
Colace, F., De Santo, M., Greco, L.: A probabilistic approach to tweets’ sentiment classification. In: Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013, pp. 37–42 (2013). Article no. 6681404
Colace, F., De Santo, M., Greco, L., Amato, F., Moscato, V., Picariello, A.: Terminological ontology learning and population using latent Dirichlet allocation. J. Vis. Lang. Comput. 25(6), 818–826 (2014)
Palmieri, F., Fiore, U., Castiglione, A.: Automatic security assessment for next generation wireless mobile networks. Mob. Inf. Syst. 7(3), 217–239 (2011)
Ficco, M., Palmieri, F., Castiglione, A.: Hybrid indoor and outdoor location services for new generation mobile terminals. Pers. Ubiquitous Comput. 18(2), 271–285 (2014)
Palmieri, F., Ficco, M., Castiglione, A. Adaptive stealth energy-related DoS attacks against cloud data centers. In: Proceedings - 2014 8th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2014, pp. 265–272 (2014). Article no. 6975474
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Amato, F., Cozzolino, G., Mazzeo, A., Moscato, F. (2018). An Advanced Methodology to Analyse Data Stored on Mobile Devices. In: Castiglione, A., Pop, F., Ficco, M., Palmieri, F. (eds) Cyberspace Safety and Security. CSS 2018. Lecture Notes in Computer Science(), vol 11161. Springer, Cham. https://doi.org/10.1007/978-3-030-01689-0_12
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DOI: https://doi.org/10.1007/978-3-030-01689-0_12
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