Intrusion detection applications using knowledge discovery and data mining
Published inJyväskylä studies in computing
PublisherUniversity of Jyväskylä
- Article I: Tuomo Sipola, Antti Juvonen and Joel Lehtonen. Anomaly detection from network logs using diffusion maps. Engineering Applications of Neural Networks, IFIP Advances in Information and Communication Technology, Vol. 363, pp. 172–181, 2011 .Full text
- Article II: Tuomo Sipola, Antti Juvonen and Joel Lehtonen. Dimensionality reduction framework for detecting anomalies from network logs. Engineering Intelligent Systems, Vol. 20, Iss. 1–2, pp. 87–97, 2012. Full text
- Article III: Antti Juvonen and Tuomo Sipola. Adaptive framework for network traffic classification using dimensionality reduction and clustering. Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2012 4th International Congress on, pp. 274–279, 2012. Full text
- Article IV: Mikhail Zolotukhin, Timo Hämäläinen and Antti Juvonen. Growing hierarchical self-organizing maps and statistical distribution models for online detecion of web attacks. Web Information Systems and Technologies. Lecture Notes in Business Information Processing, Vol. 140, pp. 281–295, 2013. DOI: 10.1007/978-3-642-36608-6_18
- Article V: Antti Juvonen and Tuomo Sipola. Combining conjunctive rule extraction with diffusion maps for network intrusion detection. The Eighteenth IEEE Symposium on Computers and Communications (ISCC 2013), pp. 411–416, 2013. Full text.
- Article VI: Antti Juvonen and Timo Hämäläinen. An efficient network log anomaly detection system using random projection dimensionality reduction. New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on, 2014. Full text
- ArticleVII: Antti Juvonen, Tuomo Sipola and Timo Hämäläinen. Online anomaly detection using dimensionality reduction techniques for http log analysis. Submitted to Computer Networks, Elsevier, 2014.
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Anomaly-based online intrusion detection system as a sensor for cyber security situational awareness system Kokkonen, Tero (University of Jyväskylä, 2016)Almost all the organisations and even individuals rely on complex structures of data networks and networked computer systems. That complex data ensemble, the cyber domain, provides great opportunities, but at the same ...
Sipola, Tuomo (University of Jyväskylä, 2013)
Unsupervised network intrusion detection systems for zero-day fast-spreading network attacks and botnets Vahdani Amoli, Payam (University of Jyväskylä, 2015)Today, the occurrence of zero-day and complex attacks in high-speed networks is increasingly common due to the high number vulnerabilities in the cyber world. As a result, intrusions become more sophisticated and fast ...
Zolotukhin, Mikhail (University of Jyväskylä, 2014)
UInDeSI4.0 : An efficient Unsupervised Intrusion Detection System for network traffic flow in Industry 4.0 ecosystem Shukla, Amit, K.; Srivastav, Shubham; Kumar, Sandeep; Muhuri, Pranab, K. (Elsevier BV, 2023)In an Industry 4.0 ecosystem, all the essential components are digitally interconnected, and automation is integrated for higher productivity. However, it invites the risk of increasing cyber-attacks amid the current cyber ...