Kishore Kumar

CryptoKhan

Technology

AI-Driven Security Analysis

Purpose

Personal Project

Tech Stack

Next.js, Node.js, Python, Tailwind CSS, Framer Motion

CryptoKhan

CryptoKhan is an AI-powered platform designed to analyze encrypted data and predict the encryption algorithm used. Users can simply upload encrypted text, and CryptoKhan will determine the most likely encryption method.

Next.js enhances user experience with smooth routing and performance, optimizing for faster predictions and responsive design.

The backend leverages a combination of AI algorithms to predict encryption types, including Transformers, FFNN, and LSTM, ensuring accurate analysis across various encryption formats.

Additional technologies used include Node.js, Tailwind CSS for responsive design, and Framer Motion for animations.

If you have any questions feel free to ask me or my Chatbot

CryptoKhan

CryptoKhan processes encrypted input data using AI to predict the underlying algorithm, offering insights into the encryption method for enhanced security analysis.

The platform uses multiple algorithms to ensure accuracy in predictions, including Transformer models, Feed-Forward Neural Networks (FFNN), LSTM, Random Forest, and Naive Bayes (NB) for comprehensive analysis across encryption types.

AI Algorithms in CryptoKhan

CryptoKhan leverages a suite of advanced AI-driven models to detect and identify encryption algorithms from complex, encoded data. Each model brings a unique strength to the decryption prediction process, enhancing CryptoKhan's accuracy and versatility:
• Transformer: Processes intricate data sequences with exceptional contextual awareness, enabling deep pattern analysis and robust predictions even in challenging data structures.
• FFNN (Feed-Forward Neural Network): Ideal for straightforward pattern detection, providing quick categorization and contributing to foundational data classification within the platform.
• LSTM (Long Short-Term Memory): Captures sequence dependencies, essential for analyzing encrypted data with time-based or ordered elements, improving accuracy in sequence-sensitive predictions.
• Random Forest (RF): Utilizes an ensemble approach to enhance prediction reliability and reduce errors by considering multiple decision paths, ideal for complex, varied encryption types.
• Naive Bayes (NB): Employs probabilistic methods to handle simpler, rule-based encryption types, delivering efficient classification with speed and accuracy in less complex cases.

BringIt

• CryptoKhan •