An Intelligent Cyber Defense Architecture: Integrating Real-Time Vulnerability Scanning, Asymmetric Encryption, and Network Reconnaissance in Modern Attack Surfaces

Abstract

Cybersecurity professionals who work in ethical hacking and incident response and digital forensics need whole self-operating systems that examine data to develop their threat detection potential. The contemporary digital infrastructure  which relies on self-operating opinion systems and integrated systems but the security systems that shield these networks design additional security dangers. The main difficulty with contemporary forensic tools is the need for security professionals to transmit private information which contains image metadata, passwords and plaintext binary files and network design details to cloud-based storage services. The conventional procedures cause large-scale data leakage threats which break fundamental principles of privacy and security and data accessibility because it detaches control over data from organizations.

The research found that the Cyber Suite which functions as an united Cyber Security Dashboard results in an edge-computing security system for native use. The platform found a new system because it shifts in-depth cryptographic functions and password validation procedures and digital proof analysis duty to the user interface of client devices. The system produces a shielded environment for private operations through the evolution of a multi-vector toolkit which works within a whole React.js platform.

The platform has four active modules which reveal whole integration with native security characteristics through their active security modules. The Cryptography & Hashes engine utilises browser-based systems to manage  2048-bit Asymmetric RSA key sets and Symmetric AES-GCM encryption. The system utilises all of its parts which involves Hash Generator (MD5, SHA1, SHA256) and Data Converters (Base64, URL, Hex encoding/decoding) and  Steganography tool which allows users to conceal and exhibit secret messages through PNG image layers utilising Least Significant Bit (LSB) procedures.

The Password Toolkit merges three components which work together to shield digital identities while defending user privacy. The system utilises a k-anonymity model to achieve its rigorous data breach detection system which uses the Have I Been Pwned API. The system shields user credentials by using native SHA-1 hash creation which carries only a 5-character prefix through the network to confirm user identity against billions of compromised data.

The Network & Web Utilities authorise teams to achieve whole external tracking activities. The system utilises CIDR Subnet computations for network mapping and a URL Analyser to recognise possible phishing links and a DNS Record Fetcher which utilises Google's public DNS-over-HTTPS (DoH) API to get A records and AAAA records and MX records and TXT records without revealing the user's DNS queries to native ISPs.