Showing items with similar title or keywords.

• Adaptive framework for network traffic classification using dimensionality reduction and clustering 

  Juvonen, Antti; Sipola, Tuomo (IEEE, 2012)

  Information security has become a very important topic especially during the last years. Web services are becoming more complex and dynamic. This offers new possibilities for attackers to exploit vulnerabilities by inputting ...

• Gear classification and fault detection using a diffusion map framework 

  Sipola, Tuomo; Ristaniemi, Tapani; Averbuch, Amir (Elsevier BV * North-Holland; International Association for Pattern Recognition, 2015)

  This article proposes a system health monitoring approach that detects abnormal behavior of machines. Diffusion map is used to reduce the dimensionality of training data, which facilitates the classification of newly ...

• Dimensionality reduction framework for detecting anomalies from network logs 

  Sipola, Tuomo; Juvonen, Antti; Lehtonen, Joel (CRL Publishing, 2012)

  Dynamic web services are vulnerable to multitude of intrusions that could be previously unknown. Server logs contain vast amounts of information about network traffic, and finding attacks from these logs improves the ...

• Research literature clustering using diffusion maps 

  Nieminen, Paavo; Pölönen, Ilkka; Sipola, Tuomo (Elsevier, 2013)

  We apply the knowledge discovery process to the mapping of current topics in a particular field of science. We are interested in how articles form clusters and what are the contents of the found clusters. A framework ...

• Using affinity perturbations to detect web traffic anomalies 

  Shmueli, Yaniv; Sipola, Tuomo; Shabat, Gil; Averbuch, Amir (EURASIP, 2013)

  The initial training phase of machine learning algorithms is usually computationally expensive as it involves the processing of huge matrices. Evolving datasets are challenging from this point of view because changing ...