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This paper explores behavioral biometrics, an emerging authentication method leveraging unique user behavior patterns for continuous security. This dynamic approach offers enhanced protection compared to traditional methods, yet significant challenges must be addressed. A key concern, examined herein, is accuracy; false positives and false negatives can undermine system effectiveness. User frustration arises from false positives, while false negatives create security vulnerabilities. The work emphasizes the need for careful system tuning and advanced machine learning to mitigate these errors. Data privacy and security are also paramount, given the sensitive, non-replaceable nature of the collected information. The paper highlights the importance of robust security measures, user transparency, and informed consent. Furthermore, it acknowledges that natural human behavioral variability, influenced by physical and environmental factors, can impact authentication accuracy, necessitating adaptive systems. In conclusion, addressing these technical and ethical challenges is crucial for realizing the full potential of behavioral biometrics. 

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. 

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. 

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. 

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. 

As mobile devices become increasingly integral to daily life, the need for robust security measures has intensified. Continuous user authentication (CUA) is an emerging paradigm designed to enhance security by verifying user identity throughout device usage, rather than solely at login. This study aims to explore user perceptions, experiences, and preferences concerning CUA methods, such as biometric scans (e.g., fingerprints, facial recognition) and behavioral analytics (e.g., typing patterns, swipe gestures). We will investigate the importance users place on continuous authentication for safeguarding personal data, as well as the usability challenges they encounter. Specifically, we will delve into how users perceive the reliability and accuracy of biometric and behavioral authentication methods, considering factors such as the perceived invasiveness of biometric scans and concerns about data privacy. Additionally, we will examine how perceptions and preferences for CUA vary across different age groups, as younger generations may be more accustomed to biometric authentication and less concerned about privacy implications, while older generations may have different preferences and concerns. The findings of this study will provide insights into user trust, privacy concerns, and the overall effectiveness of CUA in improving mobile security. By understanding user attitudes, this research seeks to inform the development of more intuitive and secure authentication solutions that align with user needs and expectations across various demographics. 

Deepfake technology presents a significant challenge to cybersecurity. These highly sophisticated AI-generated manipulations can compromise sensitive information and erode public trust, privacy, and security. This has led to broader societal impacts, including decreased trust and confidence in digital communications. This paper will discuss public knowledge, understanding, and perception of AI-generated deepfakes, which was obtained through an online survey that measured people's ability to identify video, audio, and images of deepfakes. The findings will highlight the public's knowledge and perception of deepfakes, the risks that deepfake media presents, and the vulnerabilities to detection and prevention. This awareness will lead to stronger defense strategies and enhanced cybersecurity measures that will ultimately enhance deepfake detection technology and strengthen overall cybersecurity measures that will effectively mitigate exploitation risks and safeguard personal and organizational interests. 

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. 

Anti-Virus Software is designed to keep your computer and other devices safe from viruses and other malware. Some popular free softwares that many people use are Norton, Kaspersky, Avira, and even more. If your device does get a virus, the software is supposed to isolate that file to prevent the file from infecting the rest of your device. To ensure that you are protected to the fullest extent, you must keep the anti-virus software up to date. But during that time that the anti-virus is getting updated, is it still keeping you safe? Many viruses get created every day, let alone every hour, so after your anti-virus is updated it’s already putting you at risk because those new viruses may not be in the update. All anti-virus software companies all claim to be the best frontline protectors, but this paper will see what anti-virus software will give you the best protection from viruses. 

This report will discuss and explore the concept of reflexive control theory (RCT) in the context of day-to-day cybersecurity operations. Specifically, this study aims to investigate and emphasize the influential role of this trend in cyberspace while simultaneously examining the manipulative tactics employed by adversaries and RCT’s effect on the public. This report will further explain the concept of RCT and aims to promote awareness to educate the public about this topic. In the realm of cybersecurity, reflexive control serves as a potent weapon, used to allow adversaries to be able to exploit vulnerabilities, influence decision-making and essentially predict their target’s actions. This research will focus on three key concepts within reflexive control theory: Behavioral Analysis, Threat Detection and Perception Management. Furthermore, this report examines the ethical dimensions and potential risks associated with the reflexive control theory technique. These findings are intended to raise awareness and propose strategies to enhance resilience against these manipulative tactics. By meticulously synthesizing existing literature, research studies, and user surveys, this report provides a comprehensive analysis of the reflexive control theory in cyber operations.