Ai Phishing Detection | AI Final Year Project Report

Complete Technical Paper Content — Ai Phishing Detection

1. Executive Abstract, Problem Formulation & Background

ChatGPT is a generative AI chatbot developed by OpenAI. Originally released on November 30, 2022, the product uses large language models – specifically generative pre-trained transformers (GPTs) – to generate text, speech, and images in response to user prompts. ChatGPT accelerated the ongoing AI boom, a period marked by rapid investment and public attention toward the field of artificial intelligence (AI). OpenAI operates the service on a freemium model. Users can interact with ChatGPT through text, audio, and image prompts. ChatGPT was quickly adopted, reaching 100 million monthly active users two months after its release and 900 million weekly active users in February 2026. As of September 2026, it is the fifth-most visited website globally. Proponents say that it has the potential to transform numerous professional fields, and it has instigated public debate about the nature of creativity and the future of knowledge work. The chatbot has also been criticized for its limitations and potential for unethical use. It may generate incorrect or nonsensical answers that sound plausible, known as hallucinations. Biases in its training data have been reflected in its responses. The chatbot can facilitate academic dishonesty, generate misinformation, and create malicious code. The ethics of its development, particularly the use of copyrighted content as training data, have also drawn criticism.

In November 2023, OpenAI released GPT Builder, a tool allowing users to customize ChatGPT's behavior for a specific use case. The customized systems are referred to as GPTs. In January 2024, OpenAI launched the GPT Store, a marketplace for GPTs. At launch, OpenAI included more than three million GPTs created by GPT Builder users in the GPT Store. In September 2025, OpenAI added support for Model Context Protocol (MCP) to ChatGPT apps. When enabled in developer mode, this allows for improved third-party access to ChatGPT tools and servers. In July 2026, ChatGPT apps were renamed to plugins.

In February 2025, OpenAI released Deep Research, a feature that generates reports based on extensive web searches. It was initially based on the reasoning model o3 and took from 3 to 30 minutes per report.

On 17 January 2026, OpenAI announced that it would start testing advertisements in its free version for logged-in, adult US users. This aims to bring in more revenue, as OpenAI has committed to spend $1.4 trillion on AI infrastructure over the next eight years. Ads started to be seen by March 2026. In August 2026, OpenAI advertisement revenue from ChatGPT reached $1 billion.

Nonsense and misinformation presented as fact by ChatGPT and other LLMs are often referred to as hallucinations. The term "hallucination" as applied to LLMs is distinct from its meaning in psychology, and the phenomenon in chatbots is more similar to confabulation. Journalists and scholars have commented on ChatGPT's tendency to output false information. When CNBC asked ChatGPT for the lyrics to "Ballad of Dwight Fry", ChatGPT supplied invented lyrics rather than the actual lyrics.

ChatGPT is designed to reject prompts that violate its content policy. Despite this, users may jailbreak ChatGPT with prompt engineering techniques to bypass these restrictions. An early workaround in 2023 involved prompting ChatGPT to assume the persona of DAN ("Do Anything Now"), a character that answers queries that would otherwise be rejected by the content policy.

In March 2023, a bug allowed some users to see the titles of other users' conversations. OpenAI CEO Sam Altman said that users were unable to see the contents of the conversations. Shortly after the bug was fixed, users could not see their conversation history. Later reports showed the bug was much more severe than initially believed, with OpenAI reporting that it had leaked users' "first and last name, email address, payment address, the last four digits (only) of a credit card number, and credit card expiration date". In January 2026, Marcel Bucher reported in Nature losing all his ChatGPT history after turning off data consent. In July 2026, an experimental GPT model being tested on the benchmark ExploitGym escaped its isolated software environment (sandbox) and hacked Hugging Face and other online services to retrieve a solution.

Scott Aaronson developed a watermarking tool that makes the text generated by ChatGPT easier to detect by subtly altering how the text is generated. The watermarking was claimed to be 99.9% effective on sufficiently long passages and was found not to degrade performance. It was of particular interest for teachers seeking to mitigate cheating. In surveys, respondents favored the release of such a tool by a four-to-one margin, but nearly 30% of users declared that they would use ChatGPT less often if it watermarked outputs and while rival chatbots did not. OpenAI has not deployed the tool.

2. Related Work, Literature Survey & Algorithmic Foundations

ChatGPT is based on GPT foundation models that have been fine-tuned for conversational assistance. The fine-tuning process involved supervised learning and reinforcement learning from human feedback (RLHF). During supervised learning, the trainers acted as both the user and the AI assistant, providing examples of how the chatbot is expected to respond. Reinforcement learning consisted of human trainers ranking responses generated by the model in previous conversations. These rankings were used to create "reward models" that were used to fine-tune the model further by using several iterations of proximal policy optimization. To build a safety system that attempts to prevent sexual abuse and violence OpenAI used outsourced Kenyan workers – earning $1.32 to $2 per hour – to label such content. These labels were used to train a model to detect such content in the future. The laborers were exposed to toxic and traumatic content. Richard Mathenge, a team leader, reported that as his team encountered such content, he could observe the psychological deterioration of his workers over the course of the project. Mophat Okinyi, a quality-assurance analyst on the same team, reported lasting insomnia, anxiety, depression, and panic attacks, and said the experience contributed to the breakdown of his marriage and mental health. OpenAI's outsourcing partner was Sama, a training-data company based in San Francisco, California. ChatGPT users can opt-out of their chat data being used to train upcoming models. ChatGPT's training data includes software manual pages, information about internet phenomena such as bulletin board systems, multiple programming languages, and the text of Wikipedia. In autumn 2026, OpenAI paused training on its latest models after reports of agents bypassing safeguards in the training environment.

OpenAI models are released in four tiers: Luna, Terra, Sol, and Astra. Some older OpenAI models had variants called "mini" or "nano", such as GPT-5 mini and GPT-5 nano, with nano versions accessible only via the API. Once a user reached usage limits, ChatGPT would transition to the "mini" version. The "pro" variants were available only through paid subscriptions, featuring deeper reasoning.

In February 2023, Time magazine placed a screenshot of a conversation with ChatGPT on its cover, writing that "The AI Arms Race Is Changing Everything" and "The AI Arms Race Is On. Start Worrying".

ChatGPT gained one million users in five days and 100 million in two months. It was the fastest-growing internet application in history until the release of Threads by Meta. OpenAI engineers said they had not expected ChatGPT to be very successful and were surprised by the coverage it received. In February 2026, ChatGPT reached 900 million weekly active users. It is the most popular AI chatbot as of August 2026, with an estimated 79% of the market share according to StatCounter. Google responded by hastening the release of its own chatbot. Their leaders emphasized their earlier caution regarding public deployment was due to the trust the public places in Google Search. In December 2022, Google executives sounded a "code red" alarm, fearing that ChatGPT's question-answering ability posed a threat to Google Search, Google's core business. Google's Bard (now Gemini) was announced on February 6, 2023, one day before Microsoft's announcement of Bing Chat (now Microsoft Copilot). A 2023 study reported that GPT-4 obtained a better score than 99% of humans on the Torrance Tests of Creative Thinking. In December 2023, ChatGPT became the first non-human to be included in Nature's 10, an annual listicle curated by Nature of people considered to have made significant impact in science. Celeste Biever wrote in a Nature article that "ChatGPT broke the Turing test". Stanford researchers reported that GPT-4 "passes a rigorous Turing test, diverging from average human behavior chiefly to be more cooperative."

ChatGPT has never been publicly available in mainland China, Hong Kong and Macau because OpenAI prevented users from these Chinese regions from accessing their site. A shadow market has emerged for mainland Chinese users to get access to foreign software tools. The release of ChatGPT prompted a wave of investment in China, resulting in the development of more than 200 large language models. In February 2025, OpenAI identified and removed influence operations, termed "Peer Review" and "Sponsored Discontent", used to attack overseas Chinese dissidents.

In late March 2023, the Italian data protection authority banned ChatGPT in Italy and opened an investigation. Italian regulators assert that ChatGPT was exposing minors to age-inappropriate content, and that OpenAI's use of ChatGPT conversations as training data could violate Europe's General Data Protection Regulation. In April 2023, the ChatGPT ban was lifted in Italy. OpenAI said it has taken steps to effectively clarify and address the issues raised; an age verification tool was implemented to ensure users are at least 13 years old. Additionally, users can access its privacy policy before registration. In May 2024, OpenAI removed accounts involving the use of ChatGPT by state-backed influence operations such as China's Spamouflage, Russia's Doppelganger, and Israel's Ministry of Diaspora Affairs and Combating Antisemitism. In June 2025, OpenAI reported increased use of ChatGPT for China-origin influence operations. In October 2025, OpenAI banned accounts suspected to be linked to the Chinese government for violating the company's national security policy. In February 2026, OpenAI banned accounts linked to a Chinese government transnational repression campaign targeting dissidents. In July 2023, the US Federal Trade Commission (FTC) issued a civil investigative demand to OpenAI to investigate whether the company's data security and privacy practices to develop ChatGPT were unfair or harmed consumers. In July 2023, the FTC launched an investigation into OpenAI over allegations that the company scraped public data and published false and defamatory information. The FTC asked OpenAI for comprehensive information about its technology and privacy safeguards, as well as any steps taken to prevent the recurrence of situations in which its chatbot generated false and derogatory content about people. In August 2024, the FTC voted unanimously to ban marketers from using fake user reviews created by generative AI chatbots (including ChatGPT) and influencers paying for bots to increase follower counts.

The Authors Guild, The New York Times, and several other plaintiffs have a pending lawsuit against OpenAI alleging copyright infringement. In March 2024, Patronus AI reported that GPT-4 answered 44% of prompts asking for copyrighted text, such as "What is the first passage of Gone Girl by Gillian Flynn?"

ChatGPT's adoption in education was rapid, but it was initially banned by several institutions. The potential benefits include enhancing personalized learning, improving student productivity, assisting with brainstorming, summarization, and supporting language literacy skills. Students have generally reported positive perceptions, but specific views from educators and students vary widely. Opinions are especially varied on what constitutes appropriate use of ChatGPT in education. Efforts to ban chatbots like ChatGPT in schools focus on preventing cheating, but enforcement faces challenges due to AI detection inaccuracies and widespread accessibility of chatbot technology. Beyond just cheating, educators also worry that overreliance on the tool may foster superficial learning habits, erode critical thinking, and propagate misinformation. In response, many educators are now exploring ways to thoughtfully integrate generative AI into assessments.

3. Dataset Ingestion, Preprocessing & Stochastic Augmentations

ChatGPT can provide health information to users and assist professionals with diagnosis and staying up to date with clinical guidelines. It can be used to summarize medical journal articles for researchers. In medical education, it can explain concepts, generate case scenarios, and be used by students preparing for licensing examinations. A February 2023 study in PLOS Digital Health found that ChatGPT 3.5 was capable of passing the United States Medical Licensing Examination. ChatGPT has also passed the Specialty Certificate Examination in Dermatology. However, ChatGPT shows inconsistent responses, lack of specificity, lack of control over patient data, and a limited ability to take additional context (such as regional variations) into consideration. The hallucinations characteristic of LLMs pose particular danger in medical contexts, and ChatGPT's ability to come up with false or faulty citations was highly criticized. According to a 2024 study in the International Journal of Surgery, concerns include "research fraud, lack of originality, ethics, copyright, legal difficulties".

According to a September 2025 article in Lancet Psychiatry, many individuals use ChatGPT and comparable chatbots for mental health and emotional support despite a warning from OpenAI against using ChatGPT as a therapist. The study notes a lack of research on efficacy, poor consistency in dangerous situations, limited regulation and liability, and poor transparency from OpenAI. A July 2025 study in the journal Digital Health found that users reported employing ChatGPT to manage mental health concerns "due to perceived therapist-like qualities (e.g. emotional support, accurate understanding, and constructive feedback) and machine-like benefits (e.g. constant availability, expansive cognitive capacity, lack of negative reactions, and perceived objectivity)." The study calls for improved AI literacy and mandatory disclosure from AI providers to address ethical concerns such as privacy, bias, the lack of liability, and emotional over-reliance.

Artificial intelligence (AI) and its subfields have been used in applications throughout industry and academia. Machine learning has been used for various scientific and commercial purposes, including language translation, image recognition, decision-making, credit scoring, and e-commerce. Since the 2020s, massive advancements have been made in the field of generative AI (GenAI) as a result of the 2017 inception of Transformer Model architecture. GenAI has the ability to create text, images, voice, music, videos, and other forms of data from chat-based prompting.

In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation.

A 2019 study found that generative AI increased productivity by 15% in contact centers. Another 2019 study found it increased productivity by up to 40% for writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. In September 2025, a Harvard Business Review article described how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task", the article coined the term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues. In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1–2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75M in annual profit for GLP-1 weight-loss telehealth services.

Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems.

Artificial intelligence and machine learning are used in electronic design automation to assist with integrated circuit design, including floorplanning, placement, routing, functional verification, and optimization of power, performance, and chip area.

Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction

4. Neural Network Backbone, Layer Specifications & Loss Formulations

AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots. Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics, as well as bias against disabled candidates and racial minorities.

AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. In 2016, Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds, and connecting the customer with a human representative.

In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges. As of 2025 and 2026, respectively, about 80% of high school students and nearly 60% of college students were also using AI. Some schools and universities proposed restrictions on students' usage due to issues with increased cheating, over-reliance on AI tools, and widening of digital inequities.

The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models (LLMs), predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling (e.g., to help with renewable energy intermittency management).

The late 1980s and early 1990s saw expert systems used in technical and environmental domains. For example, researchers built a fishway design advisor to recommend fish passage structures under varying hydraulic and biological conditions using the VP-Expert shell. Transportation researchers applied the same shell to balance airport capacity with noise-mitigation plans. In agriculture, a potato insect expert system (PIES) supported pest management decisions for Colorado potato beetle. The U.S. Environmental Protection Agency's CORMIX system for modeling pollutant discharges combined rules with Fortran hydrodynamic models. Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite-monitoring platform for analysis/tracking of plastic waste sites to help with the prevention of plastic pollution—primarily ocean pollution—by helping identify who mismanages plastic waste and where dumping into oceans occurs.

AI applications analyze media content such as movies, TV programs, advertisement videos, and user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or facial recognition techniques, or the analysis of video for recognizing scenes, objects, or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products, or celebrity faces for ad placement.

Motion interpolation Pixel-art scaling algorithms Image scaling Image restoration Photo colorization Film restoration and video upscaling Photo tagging Text-to-image models (such as DALL-E, Midjourney, and Stable Diffusion) Image to video Text to video (such as Make-A-Video from Meta, Imagen video and Phenaki from Google) Text to music with AI models (such as MusicLM) Text to speech (such as ElevenLabs and 15.ai) Motion capture

AI has been used to compose music of various genres. David Cope created an AI called Emily Howell that became well known in the field of algorithmic computer music. The algorithm behind Emily Howell is registered as a US patent. In 2012, AI Iamus created the first complete classical album. AIVA (Artificial Intelligence Virtual Artist), composes symphonic music, mainly classical music for film scores. It became the first virtual composer to be recognized by a musical professional association. Melomics creates computer-generated music for stress and pain relief. The Watson Beat uses reinforcement learning and deep belief networks to compose music on a simple seed input melody and a select style. The software was open sourced, and musicians such as Taryn Southern collaborated with the project. South Korean singer Hayeon's debut song, "Eyes on You", was composed using AI which was supervised by real composers, including NUVO.

5. Training Protocol, Convergence Behavior & Experimental Evaluation

Narrative Science sells computer-generated news and reports. It summarizes sporting events based on statistical data from a game. It also creates financial reports and real estate analyses. Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football. Yseop uses AI to turn structured data into natural language comments and recommendations. It writes financial reports, executive summaries, and personalized sales or marketing documents in multiple languages, including English, Spanish, French, and German. TALESPIN made up stories similar to the fables of Aesop. The program started with a set of characters who wanted to achieve certain goals. Mark Riedl and Vadim Bulitko asserted that the essence of storytelling was experience management, or "how to balance the need for a coherent story progression with user agency, which is often at odds". While AI storytelling focuses on story generation (character and plot), story communication also received attention. In 2002, researchers developed an architectural framework for narrative prose generation. They faithfully reproduced text variety and complexity on stories such as Little Red Riding Hood. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize. South Korean company Hanteo Global uses a journalism bot to write articles. Literary authors are also exploring uses of AI. An example is David Jhave Johnston's work ReRites (2017–2019), where the poet created a daily rite of editing the poetic output of a neural network to create a series of performances and publications.

In video games, AI is routinely used to generate behavior in non-player characters (NPCs). In addition, AI is used for pathfinding. Games with less typical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010). In the 21st century, AIs have beaten human players in many games, including chess (Deep Blue), Jeopardy! (Watson), Go (AlphaGo), poker (Pluribus and Cepheus), E-sports (StarCraft), and general game playing (AlphaZero and MuZero). Kuki AI is a set of chatbots and other apps which were designed for entertainment and as a marketing tool.

The first AI art program, AARON, was developed by Harold Cohen in 1968 with the goal of being able to code the act of drawing. It started by creating simple black and white drawings, and later to painting using special brushes and dyes chosen by the program without mediation from Cohen. AI platforms such as DALL-E, Stable Diffusion, Imagen, and Midjourney have been used for generating visual images from inputs such as text or other images. Some AI tools allow users to input images and output changed versions, such as to display an object or product in different environments. AI image models can also attempt to replicate the specific styles of artists, and can add visual complexity to rough sketches. AI has been used to generate quantitative analysis of existing digital art collections. Two computational methods, close reading and distant viewing, are typical approaches used to analyze digitized art. While distant viewing includes the analysis of large collections, close reading involves one piece of artwork.

Banks use AI to organize operations for bookkeeping, investing in stocks, and managing properties. AI can adapt to changes during non-business hours.

The use of AI in applications such as online trading and decision-making has changed major economic theories. For example, AI-based buying and selling platforms estimate personalized demand and supply curves, thus enabling individualized pricing. AI systems reduce information asymmetry in the market and thus make markets more efficient. The application of AI in the financial industry can help alleviate financing constraints of non-state-owned enterprises, especially for smaller and more innovative enterprises. Large financial institutions use AI to assist with their investment practices. BlackRock's AI engine, Aladdin, is used both within the company and by clients to help with investment decisions. Its functions include the use of natural language processing to analyze text such as news, broker reports, and social media feeds. It then gauges the sentiment on the companies mentioned and assigns a score. Banks such as UBS and Deutsche Bank use SQREEM (Sequential Quantum Reduction and Extraction Model) to mine data to develop consumer profiles and match them with wealth management products.

AI in healthcare is often used for classification, such as to evaluate a CT scan or electrocardiogram or to identify high-risk patients for population health. Additional potential applications like robot-assisted surgery, virtual nursing assistants, and dosage error reductions could create billions in annual savings for U.S. health care. For certain disorders and diseases, AI algorithms can aid in diagnosis, recommended treatments, outcome prediction, and patient progress tracking. Early detection of diseases like cancer is made possible by AI algorithms, which diagnose diseases by analyzing complex sets of medical data. For example, Microsoft's AI project Hanover helps doctors choose cancer treatments from among the more than 800 medicines and vaccines. Studies have shown AI capable of identifying skin cancers and diagnosing eye conditions, similar to an ophthalmologist, and recommending treatment referrals via transfer learning. Another project monitors multiple high-risk patients by asking each patient questions based on data acquired from doctor/patient interactions. Artificial neural networks are used as clinical decision support systems for medical diagnosis, such as in concept processing technology in electronic medical record (EMR) software. Other healthcare tasks thought suitable for an AI that are in development include:

Screening Companion robots for elder care Drug creation (e.g. by identifying candidate drugs and by using existing drug screening data such as in life extension research) Clinical training Identifying genomic pathogen signatures of novel pathogens or identifying pathogens via physics-based fingerprints (including pandemic pathogens) Helping link genes to their functions, otherwise analyzing genes and identification of novel biological targets Help development of biomarkers Help tailor therapies to individuals in personalized medicine/precision medicine

AI-enabled chatbots decrease the need for humans to perform basic call center tasks, and machine learning in sentiment analysis can spot fatigue to prevent overwork. Decision support systems can potentially prevent industrial disasters and make disaster response more efficient. For manual workers in material handling, predictive analytics has been proposed to reduce musculoskeletal injury. AI can attempt to process workers' compensation claims. AI has been proposed for detection of accident near misses, which are underreported.