0000000000968532
AUTHOR
Ari Viinikainen
showing 8 related works from this author
Bandwidth allocation and pricing in multimode network
2006
This paper presents adaptive resource sharing model that uses a revenue criterion to allocate network resources in an optimal way. The model ensures QoS requirements of data flows and, at the same time, maximizes the total revenue by adjusting parameters of the underlying scheduler. Besides, the adaptive model eliminates the need to find the optimal static weight values because they are calculated dynamically. The simulation consists of several cases that analyse the model and the way it provides the required QoS guarantees. The simulation reveals that the installation of the adaptive model increases the total revenue and ensures the QoS requirements for all service classes.
PACKET SCHEDULING AND PRICING BASED ON INFLICTED DELAY
2006
An adaptive packet scheduling method is presented in this paper. The adaptive weights of a scheduler are chosen based on maximizing the revenue of the network service provider. The pricing scenario is based on the delay that a connection will inflict to other connections. The features of the adaptive weight updating algorithm are simulated, analyzed and compared to a constant weight algorithm. peerReviewed
Critical Exploration of Flexible Delivery
2020
This work-in-progress research article presents an introductory qualitative study on students' perceptions of a flexibly delivered, modular computer science course. Many contemporary approaches to education rely in various ways on flexible delivery of course content. This is often done to capitalize on modern technology and the web, and to put the student ‘in the center.' However, it is becoming manifest that these approaches may challenge both the students and the equity between them, making it important to understand the effects of flexible delivery in terms of the students. In the voice of our students, flexible delivery was seen as a largely positive approach, reducing stress, promoting…
A Fast Handover Method for Real-Time Multimedia Services
2006
Mobile IPv6 (MIPv6) has been standardized for mobility management in the IPv6 network. When a mobile node changes its point of attachment in the IPv6 network, it experiences a time due MIPv6 procedures when it cannot receive or send any packets. This time called the handover delay might also cause packet loss resulting undesired quality-of-service degradation for various types of applications. The minimization of this delay is especially important for real-time applications. In this chapter we present a fast handover method called the flow-based fast handover for Mobile IPv6 (FFHMIPv6) to speed up the MIPv6 handover processes. FFHMIPv6 employs flow information and IPv6-in-IPv6 tunneling for…
A Network-Based Framework for Mobile Threat Detection
2018
Mobile malware attacks increased three folds in the past few years and continued to expand with the growing number of mobile users. Adversary uses a variety of evasion techniques to avoid detection by traditional systems, which increase the diversity of malicious applications. Thus, there is a need for an intelligent system that copes with this issue. This paper proposes a machine learning (ML) based framework to counter rapid evolution of mobile threats. This model is based on flow-based features, that will work on the network side. This model is designed with adversarial input in mind. The model uses 40 timebased network flow features, extracted from the real-time traffic of malicious and…
An Analysis of the Flow-Based Fast Handover Method For Mobile IPv6 Network
2006
Mobile IPv6 has been proposed by the IETF (Internet Engineering Task Force) to be the solution to mobility management in IPv6 network. The work is now culminating to a standard status. But, one problem still remaining is the length of the handover time, which might cause packet loss. Thus the handover time should be as short as possible. Especially the real-time traffic suffers from packet loss. Earlier we have introduced a new method for faster handover process in Mobile IPv6 network called the Flow-based Fast Handover Method for Mobile IPv6 (FFHMIPv6). FFHMIPv6 uses the flow state information stored in the routers for the fast redirection of the flow. In this paper we compare the proposed…
Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection
2017
The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing be…
Quality of service and pricing in future multiple service class networks
2006
Ari Viinikainen tutki väitöskirjassaan tietoliikenneverkkojen resurssien jakoa hinnoittelun ja palvelun laadun avulla sekä kiinteissä että langattomissa tietoliikenneverkoissa. Aihe on ajankohtainen, sillä vaikka verkkojen tiedonsiirtokapasiteetti kasvaa, ei kaikille sovelluksille voida taata niiden vaatimaa tiedonsiirtopalvelun laatua. Esimerkiksi videoneuvottelujen laadun kannalta olennaisia ovat mahdollisimman pieni viive, vähäinen viiveen vaihtelu ja suuri tiedonsiirtokapasiteetti. Viinikainen esittelee työssään useita viiveen tai tiedonsiirtonopeuden takaavia algoritmeja, joilla reitittimiin saapuvaa dataa voidaan priorisoida. Algoritmien toiminta perustuu siirrettävän tiedon erilaisee…