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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. 

Web Browsers have storage components and external software that aid in creating an enjoyable and functioning browser experience. Web browser history, cookies, ActiveX controls, and extensions all have vulnerabilities that are exploited by hackers, websites, and the web browsers themselves. Users are putting themselves at risk for an attack on their browser, possibly even their systems if they do not take the proper actions to secure their browser and keep their information private. This paper will discuss the aspects of the web browser named above, their security issues, and what can be done to stay protected. 

This report was made to develop a deeper understanding of what could be done to help better protect computer systems through ways other than simply creating programs. Human error, negligence, and apathy are also problems when it comes to preventing issues with safely browsing the internet or preventing cyber-attacks. Warnings can become too wordy, not be concise enough, not be present enough to notice, and these basic issues can cause even bigger problems. When the layout and functions of a website or an operating system become unclear, there’s an entire branch of computer science principles that can be utilized to make it manageable for people to use, and that is where Human-Computer Interaction (HCI) comes in. By analyzing what makes specific HCI principles effective, the potential to reduce, or possibly eliminate, flaws within cyber security caused by users or perpetuated by poor design choices by the creator. 

Artificial Intelligence, intelligence demonstrated by machines, has emerged as one of the most convenient and personable applications of everyday life. Specifically, AI powers digital personal assistants to answer user questions and automate everyday tasks. AI Assistants listen continuously to answer the user, even when not in use. Why is this a problem? For a hacker, this makes any digital assistant a potential listening device, a major security and privacy issue. While some companies are handling this situation well, others are falling behind as their AI components are slowly dying in the consumer market. Which digital assistant is best and most secure you may ask? This paper will first detail how each AI assistant works from a technical perspective. Then based on survey results, this paper will detail how AI Assistants rank in terms of overall security and performance 

Overall, this document will serve as an analysis of the combination between machine learning principles and computer network analysis in their ability to detect a network anomaly, such as a network attack. The research provided in this document will highlight the key elements of network analysis and provide an overview of common network analysis techniques. Specifically, this document will highlight a study conducted by the University of Luxembourg and an attempt to recreate the study with a slightly different list of parameters against a different dataset for network anomaly detection using NetFlow data. Alongside network analysis, is the emerging field of machine learning. This document will be investigating common machine learning techniques and implement a support vector machine algorithm to detect anomaly and intrusion within the network. MatLab was an utilized machine learning tool for identifying how to coordinate network analysis data with Support Vector Machines. The resulting graphs represent tests conducted using Support vector machines in a method similar to that of the University of Luxembourg. The difference between the tests is within the metrics used for anomaly detection. The University of Luxembourg utilized the IP addresses and the volume of traffic of a specific NetFlow dataset. The resulting graphs utilize a metric based on the duration of transmitted bytes, and the ratio of the incoming and outgoing bytes during the transmission. The algorithm created and defined metrics proved to not be as efficient as planned against the NetFlow dataset. The use of the conducted tests did not provide a clear classification of an anomaly. However, many other factors contributing to network anomalies were highlighted.