The thesis explores the problematics of cyber environment for the ICS/SCADA systems. First, shorter section is mainly focused on general introduction into the ICS/SCADA systems and their inner workings. Communication model of a general SCADA system and its foundational elements are explained. It is mainly theoretical passage and it serves as an introduction. It is necessary for understanding the second part which is mainly practical. The appropriate system is chosen as a first thing in the practical part of the thesis for the implementation of the whole project. There are defined criteria on which the system itself is implemented. Following that the system itself is implemented under a framework called openMUC and it is tested with help of the simulators according to the objective of the thesis.

PURPOSE - The issue of drug markets on social media platforms are under-researched. To help address the research gap, this paper accounts for the emerging drug dealing trends surrounding the community on TikTok. The purpose of this paper is to understand how drug dealers use a trending social media platform, namely TikTok, to advertise and sell illegal drugs and how the environment on TikTok facilitates drug promotions on the platform. METHOD - Data were gathered during seven weeks of netnographic fieldwork conducted among Swedish accounts on TikTok marketing drugs through video- and image posts. All evidence of content in which drug marketing was evident, including drug dealer profiles and open interactions of drug dealers and customers in the comment sections, was saved, coded and analyzed. The concepts of routine activity theory were applied and discussed in relation to the prevalence of the drug markets on TikTok. FINDINGS - In total, 116 posts with content of advertisements of illegal drugs were identified, divided into 43 Sweden-based profiles. Some drug dealer profiles were more professional than others and offered various drug types and services. Drug dealers adopted several marketing strategies, facilitated by the functions and algorithms of TikTok, to promote their drug dealing profiles and promotions. KEYWORDS -  netnography, cyber-environment, drug promotion, sale of drugs, algorithms, social media platforms, drug dealing activity. / TikTok, a social media platform which has grown to be one of the most popular communities globally to create and share short videos and live streams with millions of viewers everyday has recently become a new way of promoting the sale and distribution of illegal drugs. Drug dealing on TikTok has recently caught the attention of the Swedish news media, since Swedish Police Officers have reported observing increasingly more drug advertisements flourish in the comment sections with various code words for drugs, such as snow emojis symbolizing cocaine (Dagens Nyheter, 2021; Aftonbladet, 2021). This trend is an urgent issue; not only because of the increasingly strategic marketing strategies targeting young users on the platform with readily available menus of illegal drugs in supply available 24/7, but considering the thousands of minors who may be influenced by the increasingly more approachable promotions and readily available distribution offers; and the consequences of young people's health and well-being that follows. By exploring the settings of TikTok through a netnographic method, this study aims to identify the marketing and sale of drugs on TikTok and account for characteristics surrounding the drug dealing activity.

In the domain of cyber security, machine learning promises advanced threat detection. However, the volume of available unlabeled data poses challenges for efficient data management. This study investigates the potential for active learning, a subset of interactive machine learning, to reduce the effort required for manual data labelling. Through different query strategies, the most informative unlabeled data points were selected for manual labelling. The performance of different query strategies was assessed by testing a transformer model’s ability to accurately distinguish tweets mentioning names of advanced persistent threats. The findings suggest that the K-means diversity-based query strategy outperformed both the uncertainty-based approach and the random data point selection, when the amount of labelled training data was limited. This study also evaluated the cost-effective active learning approach, which incorporates high-confidence data points into the training dataset. However, this was shown to be the least effective strategy. Lastly, the study acknowledges that the computational time taken for each query strategy varies significantly between strategies. Hence, an optimal query strategy selection requires a balanced consideration of F-score performance taken together with time efficiency. / Maskininlärning skulle kunna användas för avancerad hotdetektion i cyberdomänen. Dock utgör behovet av träningsdata tillsammans med den stora tillgången till oannoterad data en utmaning. Detta arbete undersöker huruvida aktiv inlärning, en delmängd av interaktiv maskininlärning, kan minska behovet av annoterad data. Genom olika frågestrategier valdes de mest informativa datapunkterna ut för mänsklig annotering. Resultaten för de olika frågestrategierna utvärderades sedan genom att testa en maskininlärningsmodells förmåga att korrekt urskilja tweets som innehåller namn på cyberhotsaktörer. Resultaten tyder på att när mängden annoterad data var begränsad, presterade den diversifieringsbaserade strategin K-means bättre än både den osäkerhetsbaserade frågestrategin och strategin som väljer ut datapunkter slumpmässigt. Denna studie utvärderade också kostnadseffektiv aktiv inlärning som lägger till datapunkter som modellen redan är relativt säker på till träningsdatamängden. Denna metod visade sig dock vara den minst effektiva strategin. Slutligen visar arbetet att beräkningstiden som krävs för varje frågestrategi varierar avsevärt. För att utse den mest optimala frågestrategin krävs därför ett övervägande av både prestanda och tidsåtgång.