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@article{208853,
        author = {Bhoomika Naik and H P Deeksha and H Suvarna and Amulya B S and Chithrakshi},
        title = {A Layered Framework for Ransomware Detection and Automated Response},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {3063-3068},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208853},
        abstract = {Ransomware represents one of the most dangerous phenomena in the space of cyber security where the main idea implies the damage of the normal functioning of files or computer data resources. Given the explosive increase of ransomware families, it is no longer possible to rely only on previously recorded signatures in order to ensure efficient detection and prevention mechanisms. The proposed project deals with the development of a multi-layer ransomware attack detecting and preventing system that is based on the following core principles – static analysis, entropy, PE inspection, YARA scan, machine-learning classification, honeypot or tripwire monitoring, alert design and response. All suspicious files and activities are analyzed using a combination of indicators of compromise. The system is able to detect ransomware using the use of static indicators, entropy analysis in search of high-randomness content, rule-based scanning with YARA patterns in search of known malicious constructs or behaviors, machine-learning classification of suspicious files based on extracted features, behavioral detection with honeypot or tripwire monitoring of sensitive resources. All of this contributes to efficient detection and response to ransomware attacks.},
        keywords = {Ransomware, malware detection, machine learning, entropy, YARA, PE analysis, honeypot, tripwire, cyber security, attack prevention.},
        month = {September},
        }