Abstract
Network reconnaissance is a very core part of cybersecurity, with Nmap serving as the industry standard tool for network discovery and security auditing. However, with Nmap relying on a rigid command line interface (CLI) and an extensive system of configuration flags it creates a steep learning curve for beginner users. It also complicates the rapid analysis of large scale scan results as raw terminal output can be difficult to parse visually. The technical portion of this portfolio addresses this usability gap by developing an interactive, locally hosted web application that serves as a modern graphical user interface for Nmap. Built using Python, the application simplifies network scanning by eliminating the need for users to memorize complex command line arguments. Users can configure their network scans through an intuitive dashboard that automatically constructs and executes the underlying Nmap commands in the background, while also offering the ability to save custom scan profiles for future use and view all past scans. This prevents repetitive manual entry and standardizes auditing procedures across teams. Once a scan is complete, the application parses the data and transforms the raw output into structured and easy to read tables. This feature allows for rapid vulnerability assessment, port analysis, and host discovery. Also, the application includes a visualization engine that generates dynamic, topological network maps. These maps provide users with a clear, visual representation of network architecture and device relationships, allowing them to instantly identify potential bottlenecks or rogue devices. By lowering the barrier to entry and enhancing data visualization, this installable local tool streamlines the workflow for security professionals, network admins, and students. Ultimately, the project demonstrates how modern web development frameworks can help to revitalize legacy command line tools by bridging the gap between powerful backend capabilities and accessible frontend user experiences.
The STS research paper examines the ethical, legal, and operational boundaries of Autonomous Weapons Systems (AWS) in modern warfare. Sparked by a 2021 United Nations report detailing a fully autonomous drone strike in Libya, the global community faces a critical dilemma regarding the delegation of lethal decision making to artificial intelligence. This paper investigates how much control AWS should possess and how ethical tolerances shift between offensive and defensive applications. Using Actor Network Theory (ANT) and the Social Construction of Technology (SCOT), the research analyzes the flow of accountability among engineers, military commanders, and the algorithms themselves. They also help show how competing social groups including humanitarian organizations, defense contractors, and geopolitical rivals actively shape international policy. The historical analysis reveals that early defensive autonomy, such as the Phalanx Close-In Weapon Systems (CIWS), was ethically accepted because it intercepted inhuman threats at speeds far exceeding human reaction times. However, the modern transition to offensive autonomy in complex environments introduces severe moral hazards. The black box nature of machine learning creates a massive responsibility gap, making it nearly impossible to hold specific individuals legally accountable for unintended civilian casualties or algorithmic errors. Furthermore, the phenomenon of automation bias often reduces human operators to mere legal scapegoats who overly trust AI targeting recommendations without exercising true moral judgement. The paper concludes that while defensive autonomy remains a technological necessity to counter rapid threats, offensive AWS strip away the essential moral conscience from warfare. To ensure accountability international regulations must enforce advance control directives, restrict autonomous systems from directly targeting humans, and maintain meaningful human command over all lethal decisions. Without these strict frameworks, the rapid acceleration of artificial intelligence in military applications risks creating a future where efficiency is prioritized entirely over human life and established international humanitarian laws.
At first glance, building a graphical interface for a network scanner and analyzing the ethics of autonomous weapons appear entirely unrelated. However, completing both projects revealed a shared underlying theme regarding the critical importance of the human-machine interface and the dangers of technological opacity. The technical project aims to help better visualize and utilize a black box command line tool. By using visual design, structured data, and topological mapping, the software provides human understanding and ensures that the user remains fully aware of what the system is executing. Conversely, the STS paper explores the consequences of removing human understanding and control from a complex system. When autonomous weapons operate as black boxes without human oversight, accountability vanishes and ethical boundaries collapse. Together, these projects highlight a universal engineering principle. Whether you are mapping a local area network or regulating advanced military artificial intelligence, technology must be designed to enhance human oversight rather than obscure it.
