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@article{175706,
        author = {Swetha S and Sowbaraniga S R and Vanithadevi A and Shiva Shree E and Mrs.C.Agjelia Lydia},
        title = {AI Powered Defence: Detecting Spyware and Stalkerware with Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3579-3586},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175706},
        abstract = {Because digital technology is developing so quickly, there is a greater chance of cyberthreats like spyware and stalkerware. These malicious apps work in secret, gathering private information from users' devices; without their knowledge or permission. Conventional detection techniques frequently fall behind the quickly changing spyware threat scenario. This study introduces a machine learning algorithm-based AI-driven method for spyware and stalkerware identification. AI-driven solutions strengthen cybersecurity defences against these threats by utilising behavioural analysis, anomaly identification, and pattern recognition. To increase detection accuracy, the study assesses different feature extraction methods, classification algorithms, and real-world datasets. By successfully differentiating between harmful and authorised applications, the suggested framework enhances device security and privacy protection.},
        keywords = {malware detection, machine learning, artificial intelligence, spyware, stalkerware, and data privacy.},
        month = {April},
        }