<p>The maritime industry is experiencing one of its longest and fastest periods of growth. Hence, the global maritime surveillance capacity is in a great need of growth as well. The detection of vessel activity is an important objective of the civil security domain. Detecting vessel activity may become problematic if audit data is uncertain. This thesis aims to investigate if Bayesian networks acquired from expert knowledge can detect activities with a signature-based detection approach. For this, a maritime pilot-boat scenario has been identified with a domain expert. Each of the scenario’s activities has been divided up into signatures where each signature relates to a specific Bayesian network information node. The signatures were implemented to find evidences for the Bayesian network information nodes. AIS-data with real world observations have been used for testing, which have shown that it is possible to detect the maritime pilot-boat scenario based on the taken approach.</p>

The maritime industry is experiencing one of its longest and fastest periods of growth. Hence, the global maritime surveillance capacity is in a great need of growth as well. The detection of vessel activity is an important objective of the civil security domain. Detecting vessel activity may become problematic if audit data is uncertain. This thesis aims to investigate if Bayesian networks acquired from expert knowledge can detect activities with a signature-based detection approach. For this, a maritime pilot-boat scenario has been identified with a domain expert. Each of the scenario’s activities has been divided up into signatures where each signature relates to a specific Bayesian network information node. The signatures were implemented to find evidences for the Bayesian network information nodes. AIS-data with real world observations have been used for testing, which have shown that it is possible to detect the maritime pilot-boat scenario based on the taken approach.

The internet has become an essential part of most people's daily lives in recent years, and as more devices connect to the internet, the risk of cyber threats increases dramatically. As malware becomes more sophisticated, traditional security prevention measures are becoming less effective at defending from cyber attacks. As a result, Signature Based Detection and Anomaly Detection are two of many advanced techniques that have become crucial to defend against cyber threats such as malware, but even these are sometimes not enough to stop modern cyberattacks. In this literature review the goal is to discuss how trustworthy each of the mentioned malware detection techniques are at detecting malware in wireless networks. The study will measure trustworthiness by looking further into scalability, adaptability and robustness and resource consumption. This study concludes that both anomaly and signature-based malware detection methods exhibit strengths and weaknesses in scalability, robustness, adaptability, and resource consumption. Furthermore, more research is needed and as malware becomes more sophisticated and an increased threat to the world it is an area that is highly relevant.

In this time of increasing cyber risks, robust intrusion detection systems (IDS) arefundamentally necessary for protecting network systems. This master thesis compares twoprimary network intrusion detection resources to clarify their effectiveness, advantages, andboundaries. The investigation follows a thorough approach, including reviewing existingliterature, practical experimentation, and assessing their performance. The primary goal revolves around a deeper comprehension of the operational procedures, threatdetection capacity, and scalability of the chosen IDS solutions. Through carefulexperimentation and scrutiny, this study investigates various elements such as detection accuracy, false favorable rates, the usage of resources, and resilience in varied networksituations. Real-life data sets and contrived attack situations are harnessed to measure the proficiency of these tools in identifying both identified and fresh intrusion efforts. Finally, our experimentation did not identify a single optimal tool due to certain imperfections in both evaluated tools. However, these findings were instrumental in concluding the properties that would constitute an ideal tool. In the end, this study propels the forward arena of networksecurity, offering a detailed insight into the capabilities and limitations of day-to-day intrusion detection tools. This study aims to strengthen cybersecurity defenses and nurture improved decision-making capabilities. These efforts mitigate the constantly changing threats caused byharmful entities in our digital world.