EVALUATION OF MALWARE INJECTIONS IN APPLICATIONS
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Abstract
Background and Purpose: The internet has become integral to daily life, exposing individuals and organizations to increasing malware threats. Malware infections, such as viruses and worms, can lead to unauthorized activities, including data theft, illegal access to databases, and application malfunction. This project aims to develop a Random-4 encryption model integrated with DNA sequencing to detect and mitigate malware injections in applications, enhancing security in data-critical environments.
Methods: The proposed system combines cryptographic techniques with DNA sequencing principles to generate highly randomized encryption keys. By leveraging the randomness of DNA encoding, the system increases the complexity of encryption and improves security. It also incorporates mechanisms for detecting malware injections and authenticating users via email.
Results: The model demonstrates improved data security by utilizing DNA sequencing to generate highly random encryption keys. This approach significantly increases cryptanalysis difficulty, making unauthorized access and malware injections extremely challenging. The system offers stronger resistance to security breaches and enhances overall application integrity.
Conclusion: This work addresses data security challenges by integrating DNA sequencing with Random-4 encryption. The system ensures high randomness and strong protection against malware attacks. Future developments may focus on optimizing the application size and enhancing usability while preserving robust security measures.