Cybersecurity specialist with 3+ years of hands-on experience in penetration testing, system security, and infrastructure hardening. Graduated from the Faculty of Computer Science at Ain Shams with a CGPA of 3.31. Published IEEE ICICIS2023 research on blockchain security. Ranked Top 1% on TryHackMe and Top 3 in international CTFs. Skilled in offensive/defensive security, SIEM, and security automation. Certified security lecturer, mentor, and team leader.
A React Web application to identify fake products using blockchain technology in the supply chain. Utilized using turffle framework with solidity programming language. Allows the company to upload its products and we generate a QR code that corresponds to each of them. The user scans the QR code to indentify if the product is the authentic one or fake.
A Secure Web application that is immune to some of the OWASP Top 10 2021 vulnerabilities. These vulnerabilities include broken access control, cyptographic failures, injection (SQL - XSS), insecure design, security misconfiguration, identification and authenticaiton failure. This web app is implemented in Node.js framework.
A tool used to show the relations and properties of people (nodes) in a social network. This include community detection using Louvain Algorithm with some community detection techniques including conductance, modularity, NMI and community coverage. It also utilized page rank for link analysis technique
A console app contains the implementation of the most common cryptographic algorithms including Ceaser Cipher, Hill Cipher, Monoalpahbetic, Auto/Repeating key Vigenere, Rail Fence, Columnar and Play Fair. In addition to advanced network encryption algorithms including AES, DES, Diffie Hellman, RSA, MD5 and RC4.
This is a machine learning model written in Python that predicts the gradient descent of one variable according to another. The Dataset contains 12 columns and 300152 rows. It describes the departure time, arrival time, source city, destination city, flight number, price, ... of each flight. Data visualization include Horizontal Bar Chart, Pie Chart, Histograms, Box Plot, Scatter Plot.
Implementation of a Deep learning model aimed at classifying a binary or multi-class dataset. Improve the model's layers to fit well in the dataset and get high performance. This Model Includes dataset preparation, Preprocessing , Choosing the most suitable model, Training & Testing the model and Modifying the neural network layers to improve efficiency.
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