Fishing Detektori: Elektron Pochta va Matnli Xabarlar

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@Softeria

As an AI specialist with a decade of experience in AI software development and research (20+ publications), I am fully equipped to tackle your unique Fishing Detektori project. My proficiencies in Machine Learning, Data Science, and Text Analysis cover the full range of tasks required - from data cleaning, feature engineering, to model training and evaluation. With over 600 completed projects, I bring a proven track record of success backed by robust technological skillsets. What sets me apart is my ability to not only develop AI models, but also ensure their seamless integration into your existing systems. In this case, I'll build an intelligent program that analyzes and automatically identifies phishing attacks in electronic mails and text messages. Just as importantly, this will protect valuable user information from falling into the wrong hands. My history of delivering production-ready solutions underpins my commitment to delivering output that seamlessly integrate and improve your operations.

@OlektraGroup

Hi! I understand you need a reliable Phishing Detection system that identifies malicious patterns in emails and SMS using NLP and machine learning. I can build this pipeline for you immediately. I have extensive experience in building classification models, including a recent **Facial Liveness CNN** and a **Deepfake Detection** project. For your requirements, I will implement a robust text-processing pipeline using **scikit-learn’s TF-IDF vectorization** combined with a **Random Forest or XGBoost classifier** to weigh features like URL entropy and urgency markers. I prioritize model interpretability, ensuring the system provides a clear "confidence score" for every scan. I previously optimized a similar detection model to achieve **94% accuracy** on real-world datasets. What is the primary format of the data (CSV, API, or raw text) that the model will ingest for classification?

@emilesd617

Hi, I am a full-stack AI developer with 8 years of rich experience in software development, with a background in AI, machine learning, natural language processing, data science, and AI model development. I am familiar with Python, TensorFlow, Scikit-learn, NLP, TF-IDF, OpenAI, LLMs, data analysis, and AI chatbot development. I can develop an AI-powered phishing detection system that preprocesses email and SMS content, extracts features such as TF-IDF, URLs, suspicious domains, urgency phrases, and other indicators, then classifies messages with a machine learning model while providing a confidence score for each prediction. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@EightVeer

Hi there, We can help structure the phishing detection workflow around TF-IDF, URL and suspicious-domain signals, urgency phrases, and clear model evaluation checks. Our background in automation advisory, data analytics, and BI helps us map the feature set into a decision-ready machine learning approach with practical reporting. One point to confirm is whether labeled email and SMS examples are already available for validation. Best Regards, 8veer

@sabaehsan

Greetings, I see that you’re looking to develop a program that uses AI to analyze emails and text messages for phishing threats. The goal is to protect users from online scams and prevent identity theft by detecting harmful messages early on. To tackle this, I would employ machine learning techniques to preprocess the text, extracting key features like URLs, suspicious domains, and urgency indicators. This data would then feed into a classification model that can effectively distinguish between phishing and legitimate messages. By leveraging natural language processing, we can enhance the accuracy of the detection system, ensuring users receive reliable alerts regarding potential threats. With my background in machine learning and data analysis, I’m confident in creating a robust solution that meets your needs and enhances online safety. Best regards, Saba Ehsan

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Machine Learning (ML), Data Science, Data Analysis, Natural Language Processing, AI Text-to-text, AI Chatbot, AI Model Development, AI Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@SkillCrafts

I understand you're building a phishing detection system for emails and SMS messages, similar to how advanced spam filters leverage AI to analyze content. My expertise in natural language processing and machine learning aligns perfectly with your goal of automatically identifying malicious messages and protecting users from online fraud. My proposed approach involves a multi-stage NLP pipeline. We'll start with robust text preprocessing, including tokenization, stop-word removal, and stemming/lemmatization, using libraries like spaCy. For feature extraction, TF-IDF and word embeddings (e.g., Word2Vec or FastText) will capture semantic relationships. I'll then train and fine-tune a transformer-based model, such as BERT or RoBERTa, for its superior contextual understanding, and evaluate its performance using metrics like precision, recall, and F1-score. What specific types of phishing indicators are you prioritizing for initial detection (e.g., urgency, suspicious links, mismatched sender info)? Would you prefer to integrate this as a standalone service or a component within an existing platform? I'm available for a quick chat to discuss your technical requirements further.

@C0RETECHIES

Salom, Sizning tizimingiz shubhali xabarni qabul qilib, uni tahlil qiladi. Dastlab matn tozalanadi, so'ng undan xavf signallari (havolalar, shoshilinch so'zlar, shubhali domenlar) ajratib olinadi. Ushbu "raqamli iz" fishingni aniqlashga o'rgatilgan AI modeliga yuboriladi. Yakunda tizim xabarning xavflilik darajasini aniq foiz bilan ko'rsatadi. Texnik yondashuv: - Backend: Python (Flask) asosida REST API. - ML Modeli: Matnni tasniflash uchun Scikit-learn (masalan, Logistic Regression) ishlatiladi. - Xususiyatlar: TF-IDF, URL tahlili, kalit so'zlar lug'ati. - Frontend: Natijalarni ko'rsatish uchun sodda React.js interfeysi. Asosiy modullar: - Matnni Qayta Ishlash: Xabarlarni qabul qilish, tozalash va standartlashtirish. - Xavfni Baholash: Matndan belgilangan xususiyatlarni ajratib olish va vektorlash. - AI Yadrovi: O'qitilgan model orqali "Fishing"/"Xavfsiz" deb tasniflash va ishonch skorini hisoblash. - Foydalanuvchi Interfeysi: Tahlil natijasini taqdim etish. Tegishli tizimlar: Biz yaratgan AI tizimi telefon suhbatlari matnini tahlil qilib, ularni "kelishuv" yoki "rad etish" kabi toifalarga avtomatik ajratadi. Bu sizning loyihangizdagi kabi matnni tasniflash vazifasidir va isbotlangan yondashuvni taqdim etadi. Avval ommaviy ma'lumotlar to'plamida o'qitilgan asosiy model va API yaratamiz. So'ngra, oddiy veb-interfeys orqali testlab, model aniqligini oshirish uchun loyihada keltirilgan maxsus xususiyatlarni qo'shamiz. Asosiy e'tibor ishonchli tasniflashga qaratiladi. Savollar: 1. Modelni o'qitish uchun mavjud ma'lumotlar to'plami (dataset) bormi yoki ommaviy to'plamlardan foydalanishimiz kerakmi? 2. Tizim qanday muhitda ishga tushirilishi rejalashtirilgan (masalan, veb-ilova, brauzer kengaytmasi yoki faqat API xizmati)? 3. Vaqt o'tishi bilan paydo bo'ladigan yangi fishing usullariga moslashish uchun modelni qayta o'qitish jarayoni qanday bo'lishi kerak? Hurmat bilan, Rohit

@cliftonc20

Hello, your project is a straightforward message-classification system, and the important part is not TF-IDF itself but whether the detector stays reliable once real-world message variation and false positives show up. I’ve built production AI systems where the main engineering work is in preprocessing, feature quality, scoring logic, and making outputs usable in an application rather than just training a model once. The closest matches here are Dent-Cloud for ML prediction pipeline design and DocIntel AI for text-processing architecture and backend delivery. Both required separating ingestion, normalization, model logic, and user-facing results. For a phishing detector, I usually structure it as input normalization, feature extraction, classification, and an explanation layer. That separation matters because feature rules like suspicious domains, link counts, and urgency phrases will evolve faster than the classifier itself. I’d also put attention on evaluation logic early: calibrated confidence thresholds, false-positive review, and a clean way to inspect why a message was flagged. That is usually the difference between a demo and a system people trust. If useful, I can sketch the scoring pipeline and review how to structure the feature extraction and confidence output for production use. Clifton

@mobiwebsolutions

✋ Salom!!! ✋ LOYIHA MAQSADI:- ONLAYN FIRBIYLIKDAN HIMOYA QILISH UCHUN ELEKTRON POCHTA VA SMS XABARLARINI TAHLIL QILISH UCHUN AI ASOSLI FISHING DETEKTORINI ISHLAB CHIQISH. Men xabarlarni qayta ishlaydigan, muhim xususiyatlarni ajratib oladigan, mashinani o'rganish modellarini qo'llaydigan va ishonchlilik ballari bilan fishingni aniqlashni ta'minlaydigan AI tizimiga bo'lgan talablaringizni diqqat bilan ko'rib chiqdim. Men eng mos kelaman, chunki men to'liq stack dasturchisi sifatida 9+ yillik tajribaga egaman va shunga o'xshash AI, NLP va ma'lumotlarni tahlil qilish loyihalarini yakunlaganman. Birinchidan, men TF-IDF, URL manzillari, kalit so'zlar va shubhali naqsh tahlili yordamida matnni oldindan qayta ishlash va xususiyatlarni ajratib olishni quraman. Ikkinchidan, men aniq fishing va qonuniy tasniflash uchun mashinani o'rganish modellarini ishlab chiqaman va integratsiya qilaman. Uchinchidan, men sinov, agar kerak bo'lsa, UI integratsiyasi, ma'lumotlar bazasini boshqarish va loyiha tugagandan so'ng to'liq manba kodini yetkazib berishni ta'minlayman. Men NLP, avtomatlashtirilgan tasniflash va ishonchli natijalarga ega xavfsiz kontent tahlil tizimlarini o'z ichiga olgan shunga o'xshash AI yechimlari ustida ishladim. Siz bilan kelishuvga erishish uchun suhbatlashishni intiqlik bilan kutyapman Hurmat bilan Elisha Mariam!

@MZITSERVICES

Hi there, I am A.R.M. MASUD, with a strong Data Science background. As a Python developer, I have extensive experience building robust, scalable, and efficient solutions that address various business needs. I understand the importance of delivering high-quality, well-architected code, and I am committed to working closely with you to ensure the success of this project. I implement core functionality using Python, utilizing relevant libraries and frameworks such as Pandas, NumPy, GUI, SciPy, Matplotlib, Seaborn, Plotly, Scikit-learn, TensorFlow, Keras, PyTorch, spaCy, Flask, Django, FastAPI, OpenCV, and Jupyter. I am a professional responsible for extracting actionable insights and knowledge from large volumes of data through Machine Learning models, including CNNs, RNNs, LSTMs, GANs, Transformers, FNNs, ANNs, and DNNs. I conduct comprehensive unit, integration, and performance testing to ensure the solution is error-free and optimized. https://www.freelancer.com/u/MZITSERVICES I appreciate the opportunity to submit this proposal and am excited about the possibility of working with you to bring your project to life. Thanks A.R.M MASUD

@Web1devloper

Building on my 8+ years of experience in Data Analytics & Science, I believe that I am the perfect candidate for your project. I’ve developed a strong skill-set in critical areas such as **Machine Learning, predictive analytics and** **Python**, which are extremely relevant in tackling your fishing detection challenge. My proficiency in utilizing data visualization tools such as Power BI and Tableau can prove essential in clearly illustrating the insights detected from email and textual data. I’ve crafted compelling dashboards and built end-to-end data solutions for clients across various industries – a testament to my expertise in transforming complex datasets into actionable business strategies. Moreover, my understanding of URL presence, occurrence of certain words or phrases, embedded links, as well as other distinguishing factors associated with phishing emails will contribute significantly to the success of this project. Through my capacity to employ statistical analysis and hypothesis testing, I guarantee consistent and reliable performance from our detection model ensuring every threat is closely scrutinized.

@DeveloperStation

Hi, The two-stage design here matters more than the model choice. TF-IDF plus URL and domain features gets you a fast baseline, but phishing writers adapt their wording, so I'd pair the classic features with the message headers and link redirect chains, which are harder to fake than urgent phrasing. One question: do you have a labeled dataset of phishing and legitimate messages, or do we need to build one first? That decides the whole approach. On a recent contract I built a full product with AI integration and server deployment on a MERN stack with Python backends, so the model plus API plus a clean web front end is familiar ground for me. Good first step: I set up a preprocessing and feature pipeline, then a baseline model we can measure against real messages. Want me to start there? Adil

@dfordesigners

Hello, I can help develop your phishing detection system using AI and machine learning to analyze email and text content for suspicious features. I will focus on data preprocessing, feature extraction like TF-IDF and URL detection, and building a reliable classification model to flag phishing attempts accurately. This will help protect users from fraud by identifying threats early and providing confidence scores for each message. I will ensure the solution is tested and easy to use. Do you have sample data ready for training and testing the model? Best regards, Kausar & GoDesign Team

@mikes126

1. Analyze your requirements and define the phishing detection workflow for email and SMS content. 2. Build a preprocessing pipeline to clean text, normalize case, remove unnecessary symbols, and prepare data for analysis. 3. Extract intelligent features including TF-IDF vectors, URL presence, link count, suspicious domains, urgency keywords, uppercase ratios, and special-character frequency. 4. Train and compare ML models such as Logistic Regression, Random Forest, XGBoost, or LightGBM, selecting the highest-performing model using precision, recall, F1-score, and ROC-AUC. 5. Generate a confidence score (e.g., 97% Phishing) with reasons behind the prediction for better transparency. 6. Optimize inference speed, reduce false positives, validate on unseen datasets, and deliver clean, documented code for future model improvements and deployment.

@adriank06

Hi, Your project is a great fit for my background because it combines NLP, machine learning, and practical cybersecurity in a way that can create real value for users. From your description, the main goal is not just to label messages as phishing or legitimate, but to build a reliable detector that also considers URLs, suspicious domains, urgent wording, unusual capitalization, and other warning signs. That is exactly how I would approach it. I have hands-on experience with Python, machine learning, NLP, text classification, TF-IDF, feature engineering, and model evaluation. I can handle the full process—from data cleaning and model training to confidence scoring, testing, and building a simple user-friendly interface. A couple of important questions before starting: Do you already have a labeled phishing/legitimate dataset, or should I prepare and clean one? Would you prefer a lightweight local application, a web interface, or an API-based solution? I can deliver a clean, tested, and well-documented solution that is easy to use and extend. Would you be open to a quick chat about the dataset and your preferred interface?

@romany55

Salom. Men ushbu turdagi loyihalarda muvaffaqiyatli tajribam bor va sizning loyihaingizni ham shunday muvaffaqiyat bilan amalga oshira olishimni ishonch bilan ayta olaman. Loyihangiz aniq belgilangan, ammo xususiyatlarni ajratish va mashinaviy o'qitish modelini tanlash jarayonida yanada batafsil yondashishni talab etishi mumkin. Men dasturiy ta'minotning barcha bosqichlarini to'g'ri tashkil etish uchun avvalgi ma'lumotlarni tahlil qilish va modelni sinovdan o'tkazish jarayonlariga e'tibor qaratar edim. Shuningdek, foydalanuvchilar uchun intuitiv interfeysni yaratish ham muhimdir. Sizning loyiha maqsadlaringizga erishish uchun qanday qo'shimcha himoya choralari ko'rishni rejalashtiryapsiz? Roman

@devendrathakur12

Hi, I recently built an AI text analysis project where unstructured messages were processed, useful features were extracted, and machine learning was used to classify content with confidence scores. I can build a clean phishing detection system with text preprocessing, TF-IDF, URL and domain analysis, suspicious phrase detection, and a model that is easy to improve as new attack patterns appear. The code will be well documented and simple to maintain. One question: do you already have a labeled phishing dataset for training, or should we start with a public dataset and fine-tune it for your use case? Looking forward to building a system that catches scams before users even think about clicking. Best regards, Dev S.