KDD Cup 2013

Solecki, Benjamin; Silva, Lucas F. M. da; Efimov, Dmitry

doi:10.1145/2517288.2517297

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…There are several approaches for the detection of anomalies in traditional networks using machine learning. The most widely used datasets are the KDD99 [10] and NSL-KDD Dataset [11] (an improved version of KDD'99). These datasets contain traffic captured on the TCP protocol and collect different types of attacks.…”

Section: Related Workmentioning

confidence: 99%

Multiclass Classification Procedure for Detecting Attacks on MQTT‐IoT Protocol

et al. 2019

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The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems (IDS) are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection Systems (IDS) can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results.

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Section: Related Workmentioning

confidence: 99%

Multiclass Classification Procedure for Detecting Attacks on MQTT‐IoT Protocol

et al. 2019

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“…Here, BMS1 records clickstreams of customers of the web retailer Gazelle. com (Brodley and Kohavi 2000). Similarly, FIFA captures clickstreams of users of the '98 FIFA World Cup website (Arlitt and Jin 2000), and MSNBC contains clickstreams of page categories on the MSNBC news website (Cadez et al 2000).…”

Section: Data Setsmentioning

confidence: 99%

“…As such, path-centric models constitute an interesting class of higher-order networks that model both the temporal and topological dimension of complex systems (Lambiotte et al 2019). Moreover, the ability to accurately predict paths in networks such as travel itineraries in transportation systems (Hackl et al 2018;RITA 2014;Transport for London 2014) or user navigation on eCommerce websites (Montgomery et al 2004;Bollen et al 2009;Hui et al 2009;Brodley and Kohavi 2000) can help us to more effectively manage travel disruptions, break infection paths, recommend related products, or predict online purchases.…”

Section: Introductionmentioning

confidence: 99%

Predicting variable-length paths in networked systems using multi-order generative models

Gote,

Casiraghi,

Schweitzer

et al. 2023

Appl Netw Sci

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Apart from nodes and links, for many networked systems, we have access to data on paths, i.e., collections of temporally ordered variable-length node sequences that are constrained by the system’s topology. Understanding the patterns in such data is key to advancing our understanding of the structure and dynamics of complex systems. Moreover, the ability to accurately model and predict paths is important for engineered systems, e.g., to optimise supply chains or provide smart mobility services. Here, we introduce MOGen, a generative modelling framework that enables both next-element and out-of-sample prediction in paths with high accuracy and consistency. It features a model selection approach that automatically determines the optimal model directly from data, effectively making MOGen parameter-free. Using empirical data, we show that our method outperforms state-of-the-art sequence modelling techniques. We further introduce a mathematical formalism that links higher-order models of paths to transition matrices of random walks in multi-layer networks.

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