Search results for "Louhi"

Incentive Mechanism for Edge Computing-Based Blockchain: A Sequential Game Approach

2022

The development of the blockchain framework is able to provide feasible solutions for a wide range of Industrial Internet of Things (IIoT) applications. While the IIoT devices are usually resource-limited, how to make sure the acquisition of computational resources and participation of the devices will be the driving force to realize blockchain. In this work, an edge computing-based blockchain framework is considered, where multiple edge service providers (ESPs) can provide computational resources to the IIoT devices. We focus on investigating the trading between the devices and ESPs, where ESPs are the sellers and devices are the buyers. A sequential game is formulated and by exploring the…

Dimensionality reduction framework for detecting anomalies from network logs

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 security of the services. In this research features are extracted from HTTP query parameters using 2-grams. We propose a framework that uses dimensionality reduction and clustering to identify anomalous behavior. The framework detects intrusions from log data gathered from a real network service. This approach is adaptive, works on the application layer and reduces the number of log lines that needs to be inspected. Furthermore, the traffic can be visualized. peerReviewed

Mining Maximal Frequent Patterns in Transactional Databases and Dynamic Data Streams: A Spark-based Approach

2018

Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic data streams (DDSs) is substantially important for business intelligence. MFPs, as the smallest set of patterns, help to reveal customers’ purchase rules and market basket analysis (MBA). Although, numerous studies have been carried out in this area, most of them extend the main-memory based Apriori or FP-growth algorithms. Therefore, these approaches are not only unscalable but also lack parallelism. Consequently, ever increasing big data sources requirements cannot be met. In addition, mining performance in some existing approaches degrade drastically due to the presence of null transactions. We, therefo…

Anomaly-based online intrusion detection system as a sensor for cyber security situational awareness system

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 time it offers many possible attack vectors that can be abused for cyber vandalism, cyber crime, cyber espionage or cyber terrorism. Those threats produce requirements for cyber security situational awareness and intrusion detection capability. This dissertation concentrates on research and development of anomaly-based network intrusion detection system as a sensor for a situational awareness system. In this dissertation, several models of intrusion detection systems are devel…

''Mä oon kerran kännipäissäni pätkässyt llonaa turpaan" : sukupuolistunut väkivalta Aku Louhimiehen ja Antti J. Jokisen elokuvissa

2021

Artikkelissa tarkastellaan sukupuolistunutta väkivaltaa Antti J. Jokisen ja Aku Louhimiehen ohjaamissa pitkissä kotimaisissa elokuvissa. Tarkastelun kohteena on, miten ja millaista väkivaltaa elokuvissa esitetään ja millaisia väkivaltaa koskevia tulkintoja elokuvat avaavat myös suhteessa kulttuurisiin viitekehyksiin. Keskeisenä kysymyksenä on se, miten väkivalta liittyy sukupuoleen ja seksuaalisuuteen elokuvien representaatioissa sekä minkälaisia käsityksiä sukupuolesta ja seksuaalisuudesta nämä yhteenliittymät mahdollistavat. Artikkelini näkökulma on sukupuolitietoinen kulttuurintutkimus, ja kontekstualisoin elokuvien käsittelemiä aiheita ja teemoja suhteessa feministiseen kulttuurin ja vä…

Propaganda Barometer : A Supportive Tool to Improve Media Literacy Towards Building a Critically Thinking Society

2018

To smartly consume a huge and constantly growing volume of information, to identify fake news and resist propaganda in the context of Information Warfare, to improve personal critical thinking capabilities and increase media literacy, people require supportive environment with sophisticated technology facilitated tools. With rapid development of media, widespread popularity of social networks and fast growing amount of information distribution channels, propaganda and information warfare enter an absolutely new digital technology supported cyber era. Propaganda mining is not a trivial and very time consuming process for human. And, as with any new technology, human need certain time to unde…

On data mining applications in mobile networking and network security

2014

Taming big knowledge evolution

2016

Information and its derived knowledge are not static. Instead, information is changing over time and our understanding of it evolves with our ability and willingness to consume the information. When compared to humans, current computer systems seem very limited in their ability to really understand the meaning of things. On the other hand, they are very powerful when it comes down to performing exact computations. One aspect which sets humans apart from machines when trying to understand the world is that we will often make mistakes, forget information, or choose what to focus on. To put this in another perspective, it seems like humans can behave somehow more randomly and still outperform …

Biased GraphWalks for RDF Graph Embeddings

2017

Knowledge Graphs have been recognized as a valuable source for background information in many data mining, information retrieval, natural language processing, and knowledge extraction tasks. However, obtaining a suitable feature vector representation from RDF graphs is a challenging task. In this paper, we extend the RDF2Vec approach, which leverages language modeling techniques for unsupervised feature extraction from sequences of entities. We generate sequences by exploiting local information from graph substructures, harvested by graph walks, and learn latent numerical representations of entities in RDF graphs. We extend the way we compute feature vector representations by comparing twel…

Global RDF Vector Space Embeddings

2017

Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec. peerReviewed