Dark web Leak and Malware Detector
Keywords:
Cybersecurity, Credential Leakage, Malware Detection, Dark Web Simulation, SHA-256 Hashing, Heuristic Analysis, Keylogger Detection, Spyware Detection, Risk Scoring System, Security Awareness Platform, Digital Safety, Intrusion DetectionAbstract
The rapid expansion of digital platforms has significantly increased the risk of cybersecurity threats, particularly
in the form of credential leakage and malware-based attacks. A major concern among users is the repeated use of
passwords across multiple services, which amplifies vulnerability to large-scale data breaches. Additionally,
stealthy malicious programs such as keyloggers, spyware, and script-based attacks can silently capture sensitive
information, further compromising system integrity. Existing security solutions typically address either credential
breach detection or malware identification independently, resulting in fragmented protection mechanisms that are
less effective in educational and general user environments.To overcome these limitations, this study proposes
DARK WEB LEAK AND MALWARE DETECTOR, an integrated, simulation-driven cybersecurity framework
designed to identify both exposed credentials and potential malware threats within a unified platform. The system
utilizes cryptographic hashing techniques, specifically SHA-256, to securely verify user credentials against a
simulated compromised database without exposing plaintext information. In parallel, it incorporates heuristic
analysis and pattern-based detection methods to identify suspicious files, behaviors, and processes.The proposed
platform supports multiple scanning modes, including Quick Scan, Deep Scan, and Full System Analysis, enabling
flexible threat detection based on user requirements. A risk scoring mechanism is introduced to evaluate overall
cybersecurity exposure by combining credential leak status and malware detection results. Based on this
assessment, the system generates personalized security recommendations to enhance user awareness and promote
safer digital practices.Unlike conventional tools, the proposed framework prioritizes user privacy by avoiding
direct interaction with real dark web sources and ensuring that sensitive data is never stored in an unencrypted
format. By integrating simulation-based learning with practical security analysis, the system serves both as a
protective tool and an educational platform, fostering improved cybersecurity awareness among users.
