Distribution channel plan for wound care products: Case 3M Finland Ltd.

The aim of this master’s thesis was to make a qualitative marketing research and on the basis of this to develop a distribution plan for the case company Finnish 3M Ltd.’s wound care products. The literature review includes three important parts: distribution channel planning, the buying behavior of seniors, and special characteristics of health care products’ marketing. The empirical part of this thesis comprises two different parts. The first part is a marketing research, in which the buying behavior of wound care products is studied in Espoo. The research aim was to examine, in which distribution channels the wound care patients under home care would most preferably buy wound care products during the time period, when municipalities will not yet provide the products for free. The data was collected through semi-structured phone interviews and regular interviews, and was treated qualitatively and anonymously. The study revealed that the recommendations of nurses and doctors influenced most the buying behavior of wound care customers. In the second part of the thesis a distribution channel plan for wound care products was made for the case company 3M Finland Ltd. based on the results. 3M Finland Ltd. should focus on pharmacies, online-stores and municipal health centers as their main distributors.

Map of Helsinki-Helsingfors

Kääntöpuolella kannessa: Finland Travel Bureau Ltd Esplanade 19, Branch Office Stockmanns Department Stores

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.

Esimiestoiminnan vaikutukset lyhytaikaisiin sairauspoissaoloihin

Yrityksissä on viimeisten vuosien aikana alettu keskustella enemmän sairauspoissaoloista. Ne muodostavat monissa yrityksissä merkittävän kuluerän. Monissa yrityksissä joudutaan käsittelemään sairauspoissaoloja jollakin tavalla. Jokaisella yrityksellä on olemassa toimintatapoja lyhytaikaisten sairauspoissaolojen vähentämiseksi. Toimintatavoilla on tarkoitus tukea esimiehiä käsittelemään työntekijöiden poissaoloja. Työntekijöiden sairauspoissaolot, työkykyyn ja työstä suoriutumiseen liittyvät asiat ovat helpompia käsitellä toimintamallien avulla. Esimiestoiminnalla on todettu olevan yhteys lyhytaikaisiin sairauspoissaoloihin. On lyhytaikaisia sairauspoissaoloja esimiehistä riippumattomistakin syistä, mutta niiden esiintyessä usein, saattaa taustalla olla jokin muu tekijä kuin sairaus. Tämän tutkimuksen avulla oli selvittää eniten lyhytaikaisia sairauspoissaolojen omaavien myymälöiden työtyytyväisyyttä ja onko esimiestoiminnalla selkeä yhteys poissaoloihin. Yrityksellä on käytössään erilaisia toimintamalleja lyhytaikaisiin poissaoloihin ja niiden vähentämiseen. Tutkimuksen avulla pyrittiin huomaamaan onko jokaisessa myymälässä samat toimintamallit.