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As cyber threats grow in both frequency and sophistication, traditional cybersecurity measures struggle to keep pace with evolving attack methods. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing threat detection, prevention, and response. AI-driven security systems offer the ability to analyze vast amounts of data in real-time, recognize subtle patterns indicative of cyber threats, and adapt to new attack strategies more efficiently than conventional approaches. However, despite AI’s potential, challenges remain regarding its effectiveness, ethical implications, and risks of adversarial manipulation. This research investigates the strengths and limitations of AI-driven cybersecurity by comparing AI-based security tools with traditional methods, identifying key advantages and vulnerabilities, and exploring ethical considerations. Additionally, a survey of cybersecurity professionals was conducted to assess expert opinions on AI’s role, effectiveness, and potential risks. By combining these insights with experimental testing and a comprehensive review of existing literature, this study provides a nuanced understanding of AI’s impact on cybersecurity and offers recommendations for optimizing its integration into modern security infrastructures. 

This study examines how artificial intelligence (AI) can help with voice phishing (vishing) attacks, with a particular emphasis on deepfake technologies and AI-driven voice synthesis. It examines the strategies used by cybercriminals, assesses the effectiveness of the present defenses, and identifies difficulties in identifying and preventing such attacks. The results show that to combat the increasing complexity of vishing strategies, there is an urgent need for sophisticated detection systems and preventive actions. Future directions include the creation of cooperative policy frameworks to control the misuse of AI and easily accessible solutions for small enterprises. 

This report will discuss and analyze the risks and challenges associated with smart home devices, focusing on vulnerabilities in commonly used products such as smart speakers, security cameras, thermostats, and lighting systems. As the adoption of smart home security grows globally, it has become clear that many users remain unaware of the associated security risks, leading to data breaches and potential privacy violations. This research evaluates the security features of these devices, the frequency of breaches, and common vulnerabilities. Using a mixed-methods approach—including a user survey, analysis of past cybersecurity incidents, and a detailed review of existing literature—this study assesses the current state of smart home device security. The findings aim to highlight gaps in user awareness, evaluate manufacturers’ protective measures, and provide recommendations for improving cybersecurity practices in smart home environments. 

Computer Vision models has increasingly been embedded into video software to recognize and classify things in the physical world. While this can provide a useful result it also opens the door to vulnerabilities through a physical attack. Using a printed-out generated image, individuals can exploit computer visions models to disguise their true intentions. A possible way to block and mitigate the problems is to detect and blur the entire image to try to allow the AI to inference the said image. 

In this paper, we document our findings from previous research and literature related to adversarial examples and object detection. Artificial Intelligence (AI) is an increasingly powerful tool in various fields, particularly in image classification and object detection. As AI becomes more advanced, new methods to deceive machine learning models, such as adversarial patches, have emerged. These subtle modifications to images can cause AI models to misclassify objects, posing a significant challenge to their reliability. This research builds upon our earlier work by investigating how small patches affect object detection on YOLOv8. Last year, we explored patterns within images and their impact on model accuracy. This study extends that work by testing how adversarial patches, particularly those targeting animal patterns, affect YOLOv8's ability to accurately detect objects. We also explore how untrained patterns influence the model’s performance, aiming to identify weaknesses and improve the robustness of object detection systems. 

In recent years, the number of Internet of Things (IoT) devices has expanded fast, transforming various industries such as healthcare, manufacturing, and transportation, and delivering benefits to both individuals and industries. However, the increased use of IoT devices has exposed IoT ecosystems to a slew of security risks and digital forensic issues. This thesis investigates the most common IoT security dangers and attacks, as well as students' understanding of them and mitigation techniques, as well as the key issues involved with IoT forensic investigations. In this thesis, a mixed-method approach is used, combining a literature review and a survey investigation. The poll measures students' understanding of IoT security threats, mitigation approaches, and perceptions of the most effective ways to improve IoT security. In addition, the survey underlines the importance of user training and awareness in minimizing IoT dangers, highlighting the most effective strategies, such as stronger regulations and increased device security by manufacturers. The literature review provides a complete overview of the most popular IoT security risks and attacks, including malware, malicious code injection, replay attacks, Man in the Middle (MITM), botnets, and Distributed Denial of Service (DDoS). This paper also emphasizes the definition and process of digital and IoT forensics, the significance of IoT forensics, and various data sources in IoT ecosystems. The key issues of IoT forensics and how they affect the efficiency of digital investigations in the IoT ecosystem are thoroughly investigated. Overall, the findings of this study contribute to ongoing research to improve IoT device security, emphasize the necessity of greater awareness and user training, and address the issues of IoT forensic investigations. 

This report will discuss the importance of network security. Network Security is important because it prevents hackers from gaining access to data and personal information. The issue in society is that users get their data stolen every day and are scared that their information is blasted out to the world. Within this paper I will talk to you about the importance of network security and how it can change your day-to-day life using cyber security. In addition, I will create a survey for computer science majors to see if network security is important. Also, I will send a survey to a DISA employee to get his perspective on this topic and his comments as well. The best method to incorporate both user input and research into this paper is to use user input to back up the research. User input will be a great addition because it gives the readers a real-world opinion on if this topic is valid. 

This report will discuss the impact of COVID-19 on cybersecurity. This report also discusses cyber threats in the home office, the healthcare and public health (HPH) sector in attackers’ sight, and the rise of COVID-themed phishing attacks targeted towards remote workers and internet users. Tips on mitigating cyber risk will also be touched upon. This study utilizes surveys conducted to identify the effects this ongoing pandemic has had on businesses and people. 

This report will discuss and analyze the risks and different challenges which are accompanied when completing remote work and learning. Specifically, this paper will focus on this trends’ effect, during the 2019 Coronavirus pandemic (COVID-19). The different applications that will be included in this research are Slack, Zoom, Skype, Microsoft Teams, Google Meets, Trello, Webex, and Troop Messenger. In recent months, there has been a complete increase in the amount of people worldwide that use these platforms. However, the majority of users do not fully understand security and privacy when using these different platforms. Due to this lack of knowledge, this comes with an increase in attacks. This study will further discuss the different pros and cons of each of the different platforms (mentioned above); the applications that have been breached, how often they were breached, different flaws, vulnerabilities of each system and more. This study coherently uses and assesses its credibility with the assistance of research, a user survey, and past research studies on this topic. 

With the increased use of machine learning models, there is a need to understand how machine learning models can be maliciously targeted. Understanding how these attacks are ‘enacted’ helps in being able to ‘harden’ models so that it is harder for attackers to evade detection. We want to better understand object detection, the underlying algorithms, different perturbation approaches that can be utilized to fool these models. To this end, we document our findings as a review of existing literature and open-source repositories related to Computer Vision and Object Detection. We also look at how Adversarial Patches impact object detection algorithms. Our objective was to replicate existing processes in order to reproduce results to further our research on adversarial patches.