Over the past decade, webcriminality has become a real issue. Because they allow the botmasters to control hundreds to millions of machines, botnets became the first-choice attack platform for the network attackers, to launch distributed denial of service attacks, steal sensitive information and spend spam emails. This work aims at designing and implementing a honeynet, specific to IRC bots. Our system works in 3 phasis: (1) binaries collection, (2) simulation, and (3) activity capturing and monitoring. Our phase 2 simulation uses an IRC redirection to extract the connection information thanks to a IRC redirection (using a DNS redirection and a "fakeserver"). In phase 3, we use the information previously extracted to launch our honeyclient, which will capture and monitor the traffic on the C&C channel. Thanks to our honeynet, we create a database of the activity of IRC botnets (their connection characteristics, commands on the C&C ), and hope to learn more about their behavior and the underground market they create.

In modern computer video games, the quality of artificial characters plays a prominent role in the success of the game in the market. The aim of intelligent techniques, termed game AI, used in these games is to provide an interesting and challenging game play to a game player. Being highly sophisticated, these games present game developers with similar kind of requirements and challenges as faced by academic AI community. The game companies claim to use sophisticated game AI to model artificial characters such as computer game bots, intelligent realistic AI agents. However, these bots work via simple routines pre-programmed to suit the game map, game rules, game type, and other parameters unique to each game. Mostly, illusive intelligent behaviors are programmed using simple conditional statements and are hard-coded in the bots' logic. Moreover, a game programmer has to spend considerable time configuring crisp inputs for these conditional statements. Therefore, we realize a need for machine learning techniques to dynamically improve bots' behavior and save precious computer programmers' man-hours. So, we selected Q-learning, a reinforcement learning technique, to evolve dynamic intelligent bots, as it is a simple, efficient, and online learning algorithm. Machine learning techniques such as reinforcement learning are know to be intractable if they use a detailed model of the world, and also requires tuning of various parameters to give satisfactory performance. Therefore, for this research we opt to examine Q-learning for evolving a few basic behaviors viz. learning to fight, and planting the bomb for computer game bots. Furthermore, we experimented on how bots would use knowledge learned from abstract models to evolve its behavior in more detailed model of the world. Bots evolved using these techniques would become more pragmatic, believable and capable of showing human-like behavior. This will provide more realistic feel to the game and provide game programmers with an efficient learning technique for programming these bots.

With more political and social discourse taking place online, particularly on social media, theorists have started labeling digital communicative realms as “online public spheres.” However, with the modern public sphere comes modern challenges to political communication; a core antagonist of which is political bots. Political bots are automated accounts that produce content and interact with individuals on political topics on social networks. In this thesis, I analyzed the presence of political bots on Twitter during the 2022 Swedish general election, and by examining the content posted by the bots, I investigated whether they jeopardized democratic functions of the online public sphere by publishing misrepresentation (i.e., artificially increasing the popularity of political actors and political ideas). The analysis uncovered significant bot presence during the 2022 Swedish general election; more than one-fifth of all election-related content was produced by bots, ~90% of which produced misrepresentation. I concluded that political bots jeopardized democratic functions of the online public sphere during the 2022 Swedish general election.

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Social media platforms have revolutionized how people interact with each other and how people gain information. However, social media platforms such as Twitter quickly became a platform for public manipulation and spreading or amplifying political or ideological misinformation. Although malicious content can be shared by individuals, today millions of coordinated automated clients disguised as individuals exist, also called social bots which have become a significant contributor of the malicious content spread on social media platforms. Therefore, this study aims to investigate in closer look what the requirements are to create a basic social bot from resources available from the internet. A proof-ofconcept prototype is implemented in the form of a social bot on Twitter. The experiences from the work indicate thatthe skills required to create a basic social botare well within reach ofa third-year Bachelor student in the field of computer science. / Sociala medier har revolutionerat människors sätt att integrera med varandra samt hur de delar och hämtar information. Däremot har sociala medier såsom Twitter snabbt blivit en plattform för offentlig manipulation och spridning eller förstärkning av politisk eller ideologisk desinformation. Även om skadligt innehåll kan delas av individer finns det idag miljontals koordinerade automatiserade klienter förklädda som individer, även kallade sociala bottar som har blivit en betydande bidragsgivare till det skadliga innehållet som sprids på sociala medieplattformar. Därför syftar detta arbete på att närmare undersöka vad som krävs för att skapa en grundläggande social bot på en social mediaplattform från resurser tillgängliga från internet. En bevis-på-koncept-prototyp implementeras i form av en social bot på Twitter. Erfarenheterna från arbetet tyder på att de färdigheter som krävs för att skapa en grundläggande social bot ligger väl inom räckhåll för en tredjeårs kandidatstudent inom området datavetenskap.

This work presents our efforts on preventing the web bots to illegitimately access web resources. As the first technique, we present SEMAGE (SEmantically MAtching imaGEs), a new image-based CAPTCHA that capitalizes on the human ability to define and comprehend image content and to establish semantic relationships between them. As the second technique, we present NOID - a "NOn-Intrusive Web Bot Defense system" that aims at creating a three tiered defence system against web automation programs or web bots. NOID is a server side technique and prevents the web bots from accessing web resources by inherently hiding the HTML elements of interest by randomization and obfuscation in the HTML responses. A SEMAGE challenge asks a user to select semantically related images from a given image set. SEMAGE has a two-factor design where in order to pass a challenge the user is required to figure out the content of each image and then understand and identify semantic relationship between a subset of them. Most of the current state-of-the-art image-based systems like Assira only require the user to solve the first level, i.e., image recognition. Utilizing the semantic correlation between images to create more secure and user-friendly challenges makes SEMAGE novel. SEMAGE does not suffer from limitations of traditional image-based approaches such as lacking customization and adaptability. SEMAGE unlike the current Text based systems is also very user friendly with a high fun factor. We conduct a first of its kind large-scale user study involving 174 users to gauge and compare accuracy and usability of SEMAGE with existing state-of-the-art CAPTCHA systems like reCAPTCHA (text-based) and Asirra (image-based). The user study further reinstates our points and shows that users achieve high accuracy using our system and consider our system to be fun and easy. We also design a novel server-side and non-intrusive web bot defense system, NOID, to prevent web bots from accessing web resources by inherently hiding and randomizing HTML elements. Specifically, to prevent web bots uniquely identifying HTML elements for later automation, NOID randomizes name/id parameter values of essential HTML elements such as "input textbox", "textarea" and "submit button" in each HTTP form page. In addition, to prevent powerful web bots from identifying special user-action HTML elements by analyzing the content of their accompanied "label text" HTML tags, we enhance NOID by adding a component, Label Concealer, which hides label indicators by replacing "label text" HTML tags with randomized images. To further prevent more powerful web bots identifying HTML elements by recognizing their relative positions or surrounding elements in the web pages, we enhance NOID by adding another component, Element Trapper, which obfuscates important HTML elements' surroundings by adding decoy elements without compromising usability. We evaluate NOID against five powerful state-of-the-art web bots including XRumer, SENuke, Magic Submitter, Comment Blaster, and UWCS on several popular open source web platforms including phpBB, Simple Machine Forums (SMF), and Wordpress. According to our evaluation, NOID can prevent all these web bots automatically sending spam on these web platforms with reasonable overhead.

There are many games that have been a challenge to Research in Artificial Intelligence. One such game is Settlers of Catan (SoC). The purpose of this thesis is to develop a Multi-agent player for SoC. Although it is difficult to focus on all the dimensions of the game during implementation, therefore a good enough solution is proposed. An emphasis is placed on building a good trader for the player. Once a working solution had been built, the player was tested against other players which included human players as well as bots.

The use of computers has grown immensely in the last decades and the possibility to teach through them has grown as well. Various computer applications, games and forums have been created as tools of teaching and learning. This research project contains a summary of various works created within the E-learning area and their results. The virtual world Second Life is tested and some of the research in the literature review will be visited and examined. The research focuses on the feasibility to create scenarios for nursing students to practice patient care and handling, in addition to collaborative qualities that online virtual worlds offer. The conclusion taken from this research is that the possibilities far outweigh the limitations and that learning through virtual worlds should be further explored and developed.

Multiplayer online battle arena (MOBA) games and games in general offer developers opportunities to design and test AI to further push research within the area. MOBA is a genre that has seen an increase in popularity during recent years alongside a rapidly growing esports scene.  The game's main purpose is ultimately to be enjoyed and played by human players, who often come up with and are encouraged to create their own strategies in order to beat the game or win out over their opponents.  This thesis will explore how mimicking human behavior in AI and using statistics from human players can be beneficial to AI design. The AI will be developed in a framework similar to that used by the Conference of Games for their Dota 2 5v5 AI competition. Three different strategies will be created, each of which will mimic some aspect of human behaviour, and then tested against the built-in AI in Dota 2. All the developed AI’s managed to win with varying results. The results showed some support for the notion that human gameplay and statistics can be beneficial to AI design. There are, however, difficulties with the complexity of the game that is Dota 2.

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