The escalating threat of ransomware attacks to internet services demands robust detection and mitigation methods. This study introduces an innovative strategy for countering ransomware attacks using the Advanced Encryption Standard (AES) algorithm for real-time traffic classification on kafka stream. Achieving a remarkable 96.7% accuracy, model effectively identifies ransomware attack patterns. Leveraging kafka distributed streaming capabilities, the solution proves its scalability for handling extensive ransomware query flows. The model's efficiency and resilience make it a practical choice for prompt ransomware detection and response. Through rigorous experimentation, demonstrate the efficacy of the AES-based approach in combatting ransomware threats. The solution not only accurately identifies attack patterns but also does so swiftly, aligning with real-world requirements. This research presents a pivotal step in fortifying digital ecosystems against ransomware. Amid the mounting ransomware challenge, this study advances security measures for digital landscapes. Employing AES on kafka stream empowers stakeholders to proactively mitigate ransomware risks, highlighting the significance of robust traffic classification in safeguarding digital domains.


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    Titel :

    Enhancing Cybersecurity Resilience: Real-time Ransomware Detection using AES Algorithm on Kafka Stream


    Beteiligte:


    Erscheinungsdatum :

    22.11.2023


    Format / Umfang :

    430454 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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