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Although eciency is usually high for signature-based intrusion detection, eciency always suers in the case of anomaly-based intrusion detection. ... The work in  experimentally demonstrates that running a classication algorithm on the smartphone device for anomaly-based intrusion detection can result in signicant overhead on the device resources ...doi:10.1145/3369740.3369796 dblp:conf/icdcn/BarbhuiyaKN20 fatcat:cfymfyquqfdflguday63cohkem
INDEX TERMS Smartphone, intrusion detection, mobile malware, android devices, machine learning. ... APPLICATION BEHAVIOR Understanding the pattern of mobile application is the main key for mobile malware detection. ...  were able to detect app patterns by implementing a multi-level anomaly detector scheme for Android Malware (MADAM) at the kernel, app, user, and package levels. ...doi:10.1109/access.2021.3123187 fatcat:evuzzky5izht3efupwo6ty42ea
The facility for consumers to augment the base functionality of a smartphone has not only acted as acatalyst for the rapid adoption of the smartphone but continues to encourage regular use of the device ... Today's smartphone represents not only a complex device akin to an always-connected Personal Computer (PC), butalso a relatively new mechanism for software dissemination. ... In other words, given the current administrative models, network-based intrusion detection systems appear considerably more useful to mobile devices than their host-based counterparts. ...doi:10.1184/r1/7416413 fatcat:xoey5ufncvc3josug7iqki6l2a