The great advance of communication technology comes with a rapid increase of disinformation in many kinds and shapes; manipulated images are one of the primary examples of disinformation that can affect many users. Such activity can severely impact public behavior, attitude, and belief or sway the viewers' perception in any malicious or benign direction. Additionally, adversarial attacks targeting deep learning models pose a severe risk to computer vision applications. This dissertation explores ways of detecting and resisting manipulated or adversarial attack images. The first contribution evaluates perceptual hashing (pHash) algorithms for detecting image manipulation on social media platforms like Facebook and Twitter. The study demonstrates the differences in image processing between the two platforms and proposes a new approach to find the optimal detection threshold for each algorithm. The next contribution develops a new pHash authentication to detect fake imagery on social media networks, using a self-supervised learning framework and contrastive loss. In addition, a fake image sample generator is developed to cover three major image manipulating operations (copy-move, splicing, removal). The proposed authentication technique outperforms the state-of-the-art pHash methods. The third contribution addresses the challenges of adversarial attacks to deep learning models. A new adversarial-aware deep learning system is proposed using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. The proposed approach outperforms current state-of-the-art adversarial defense systems. Finally, the fourth contribution fuses big data from Extra-Military resources to support military decision-making. The study proposes a workflow, reviews data availability, security, privacy, and integrity challenges, and suggests solutions. A demonstration of the proposed image authentication is introduced to prevent wrong decisions and increase integrity. Overall, the dissertation provides practical solutions for detecting manipulated and adversarial attack images and integrates our proposed solutions in supporting military decision-making workflow.

Software Defined Networking (SDN) modernizes network control, offering streamlined management. However, its centralized structure makes it more vulnerable to distributed Denial of Service (DDoS) attacks, posing serious threats to network stability. This thesis explores the development of a DDoS attack prevention technique in SDN environments using the Ryu controller application. The research aims to address the vulnerabilities in SDN, particularly focusing on flooding and Internet Protocol (IP) spoofing attacks, which are a significant threat to network security. The study employs an experimental approach, utilizing tools like Mininet-VM (VirtualMachine), Oracle VM VirtualBox, and hping3 to simulate a virtual SDN environment and conduct DDoS attack scenarios. Key methodologies include packet sniffing and rule-based detection by integrating Snort IDS (Intrusion Detection System), which is critical for identifying and mitigating such attacks. The experiments demonstrate the effectiveness of the proposed prevention technique, highlighting the importance of proper configuration and integration of network security tools in SDN. This work contributes to enhancing the resilience of SDN architectures against DDoS attacks, offering insights into future developments in network security.

In the last 12 years, since the devastating attack on the United States Twin Towers on September 11, 2001, the global community has become increasingly wary. The continuing terrorism on July 7, 2005 on the United Kingdom subway system increased tensions between citizens and immigrants in these countries. I use these two countries to examine the consequences effects that these terrorist attacks have had on, in particular, the Muslim immigrant population. In addition to that, I use Germany as a control, since it has not faced a major terrorist attack, yet has a substantial Muslim immigrant population. In the United States and United Kingdom, I use public opinion data polls and immigration policies before and after the attacks. In Germany's case, I utilize the same data and to assess any correlation to the other two countries data. Using the literature already written, public opinion data polls and policy initiatives enacted before and after these attacks, I examine the overall effect, if any, on the Muslim immigrant population in these countries. The intent of this thesis is to explore if the significant changes in immigration policies after the attacks have occurred due to economic or cultural factors. Because public opinion is central to policy changes, I also consider the implications of public's views on immigration after the attacks, along with the effect all this has on the number of Muslim immigrants entering these countries.

Software Defined Networks (SDN) has created great potential and hope to overcome the need for secure, reliable and well managed next generation networks to drive effective service delivery on the go and meet the demand for high data rate and seamless connectivity expected by users. Thus, it is a network technology that is set to enhance our day-to-day activities. As network usage and reliance on computer technology are increasing and popular, users with bad intentions exploit the inherent weakness of this technology to render targeted services unavailable to legitimate users. Among the security weaknesses of SDN is Distributed Denial of Service (DDoS) attacks. Even though DDoS attack strategy is known, the number of successful DDoS attacks launched has seen an increment at an alarming rate over the last decade. Existing detection mechanisms depend on signatures of known attacks which has not been successful in detecting unknown or different shades of DDoS attacks. Therefore, a novel detection mechanism that relies on deviation from confidence interval obtained from the normal distribution of throughput polled without attack from the server. Furthermore, sensitivity analysis to determine which of the network metrics (jitter, throughput and response time) is more sensitive to attack by introducing white Gaussian noise and evaluating the local sensitivity using feed-forward artificial neural network is evaluated. All metrics are sensitive in detecting DDoS attacks. However, jitter appears to be the most sensitive to attack. As a result, the developed framework provides an avenue to make the SDN technology more robust and secure to DDoS attacks.

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