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Mobile devices are increasingly becoming a greater crutch for all generations. All the while, these users are garnering a greater desire for privacy and style. Apple presents a device that is known for its security, but lacks major user customization. On the other hand, Google has developed a device that is keen to customization with Android, but can be susceptible to security flaws. This thesis strives to discuss the security models, app store protections, and best practices of both mobile operating systems. In addition, multiple experiments were conducted to demonstrate how an Android device could be more easily compromised after altering few settings, as well as to demonstrate the privileges, both good and bad, that could be gained by jailbreaking an iOS device.

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As humanity becomes more reliant on digital storage and communication for every aspect of life, cyber attacks pose a growing threat. However, cyber attacks are generally understood as individual incidents reported in technological circles, sometimes tied to a particular vulnerability. They are not generally understood through the macroscopic lens of statistical analysis spanning years over several countries and sectors, leaving researchers largely ignorant of the larger trends and correlates between attacks. This is large part due to the lack of a coherent and open database of prominent attacks. Most data about cyber attacks has been captured using a repository of common vulnerabilities and exposures (CVE’s), and \honey pots", unsecured internet-connected devices which record attacks as they occur against them. These approaches help in the process of identifying vulnerabilities, but they do not capture the real world impact these attacks achieve. Therefore, in this thesis I create a database of 4,000 cyber attacks using a semi-open data source, and perform analytical queries on it to gather insights into how cyber attack volume varies among countries and sectors, and the correlates of cyber attack victims. From here, it is also possible to relate socio-economic data such as GDP and World Happiness Index to cyber attack volume. The end result is an open database of cyber attacks that allows researchers to understand the larger underlying forces which propel cyber attacks.

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Cybercrime costs a total loss of about $338 billion annually which makes it one of the most profitable criminal activities in the world. Controlled malware (Botnet) is one of the most prominent tools used by cybercriminals to infect, compromise computer networks and steal important information. Infecting a computer is relatively easy nowadays with malware that propagates through social networking in addition to the traditional methods like SPAM messages and email attachments. In fact, more than 1/4 of all computers in the world are infected by malware which makes them viable for botnet use. This thesis proposes, implements and presents the Self-healing framework that takes inspiration from the human immune system. The designed self-healing framework utilises the key characteristics and attributes of the nature’s immune system to reverse botnet infections. It employs its main components to heal the infected nodes. If the healing process was not successful for any reason, it immediately removes the infected node from the Enterprise’s network to a quarantined network to avoid any further botnet propagation and alert the Administrators for human intervention. The designed self-healing framework was tested and validated using different experiments and the results show that it efficiently heals the infected workstations in an Enterprise network.

Minecraft, the world's best-selling video game, boasts a vast and vibrant community of users who actively develop third-party software for the game. However, it has also garnered notoriety as one of the most malware-infested gaming environments. This poses a unique challenge because Minecraft software has many community-specific nuances that make traditional malware analysis less effective. These differences include unique file types, differing code formats, and lack of standardization in user-generated content analysis. This research looks at Minecraft clients in the two most common formats: Portable Executable and Java Archive file formats. Feature correlation matrices showed that malware features are too complicated to analyze without advanced algorithms. The latest machine learning methods for malware analysis were employed to classify samples based on both behavioral features generated from running samples in a sandbox environment and static features through file-based analysis. A total sample set of 92 files was used and found that Portable Executable and Java Archive files have significantly different feature sets that are important for malware identification. This study was able to successfully classify 77.8% of all Portable Executable samples 84.2% of all Java Archive samples while maintaining high recall scores. This research, by shedding light on the intricacies of malware detection in Minecraft clients, provides a framework for a more nuanced and adaptable approach to game-related malware research.

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