{'fraction_non_alphanumeric': 0.018115942028985508, 'fraction_numerical': 0.0, 'mean_word_length': 5.739837398373984, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Android Malware Detection Using Library API Call Tracing and Semantic-Preserving Signal Processing Techniques

We propose to develop a new malware detection mechanism for Android-based mobile devices based upon library API call tracing and signal processing techniques. By tracing and utilizing library API calls we can capture the intentions/behaviors of an application at a higher level. Also, signal processing techniques, such as a wavelet-based transformation, may have the advantage of enhanced flexibility, effective malware detection, reduced runtime overhead, and capability to detect hidden intrusive patterns compared to the other pattern classification techniques, and may enhance the detection capability even for evolving and varying malwares. A dynamic approach will be developed and investigated in this project.