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@article{149792,
        author = {Asha patel and Almas Safora and Amina Javeriya Sultana and Basharath Sultana and Lokesh Murge},
        title = {MADAM:  EFFECTIVE AND EFFICIENT BEHAVIOR-BASED ANDROID MALWARE DETECTION AND PREVENTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {1},
        pages = {675-678},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=149792},
        abstract = {Android users are constantly threatened by an increasing number of malicious applications (apps), generically called malware. In this paper we present MADAM, a novel host-based malware detection system for Android devices which simultaneously analyses and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviours. MADAM has been designed to take into account those behaviours characteristics of almost every real malware which can be found in the wild. MADAM detects and effectively blocks more than 96% of malicious apps, which come from three large datasets with about 2,800 apps, by exploiting the cooperation of two parallel classifiers and a behavioural signature-based detector.},
        keywords = {},
        month = {},
        }