The use of future-oriented knowledge in regional innovation processes: Research on knowledge generation, transfer and conversion

The importance of the regional level in research has risen in the last few decades and a vast literature in the fields of, for instance, evolutionary and institutional economics, network theories, innovations and learning systems, as well as sociology, has focused on regional level questions. Recently the policy makers and regional actors have also began to pay increasing attention to the knowledge economy and its needs, in general, and the connectivity and support structures of regional clusters in particular. Nowadays knowledge is generally considered as the most important source of competitive advantage, but even the most specialised forms of knowledge are becoming a short-lived resource for example due to the accelerating pace of technological change. This emphasizes the need of foresight activities in national, regional and organizational levels and the integration of foresight and innovation activities. In regional setting this development sets great challenges especially in those regions having no university and thus usually very limited resources for research activities. Also the research problem of this dissertation is related to the need to better incorporate the information produced by foresight process to facilitate and to be used in regional practice-based innovation processes. This dissertation is a constructive case study the case being Lahti region and a network facilitating innovation policy adopted in that region. Dissertation consists of a summary and five articles and during the research process a construct or a conceptual model for solving this real life problem has been developed. It is also being implemented as part of the network facilitating innovation policy in the Lahti region.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

THE NETWORK EMBEDDEDNESS OF A FOREIGN SALES SUBSIDIARY The subsidiary personnel perspective

This study focuses on the network embeddedness of a subsidiary of a multinational company. Academic research has identified the rising role of geographically dispersed subsidiaries as valuable sources of strategic knowledge and value to the whole multinational company. Moreover, previous research suggests that in order to gather this knowledge and transfer it across the multinational company, a subsidiary needs to be insider, i.e. embedded, in both its local external network as well as in its internal corporate network. The purpose of this study is to describe the network embeddedness of a foreign sales subsidiary from the subsidiary personnel perspective and hence increase understanding on the phenomenon and provide suggestions to enhance overall subsidiary embeddedness. The empirical study was based on a theoretical framework on subsidiary network embeddedness and comprised a qualitative single-case study in a French sales subsidiary of a Nordic multinational company. Data collection included nine semi-structured interviews and six Network Pictures drawing tasks. Altogether eight people out of subsidiary staff of eleven participated in the study providing relatively exhaustive overview of the subsidiary personnel perspective. Based on the collected data, the relationships toward the most relevant network actors, both internal and external were identified and their impact on the subsidiary embeddedness were analyzed separately. Moreover, the subsidiary’s simultaneous embeddedness in both internal and external network, that is, the subsidiary dual embeddedness, was discussed to increase understanding how subsidiary personnel perceive their role between the two networks and its impact on the subsidiary activities. The findings of the study suggest that the subsidiary personnel perceives strong external embeddedness increasing internal and dual-embeddedness since intensive external collaboration requires including and activating other corporate units as well. The role of the local sales subsidiary is to act as the interpreter and connector between the internal and external network actors. Hence, by actively promoting relationship linkages between internal and external actors, the subsidiary may adopt an active role beyond its original corporate mandate. In order to achieve this, both managers on the subsidiary and corporate level need to promote open communication and increase cultural understanding between different corporate units.