{'fraction_non_alphanumeric': 0.014608233731739707, 'fraction_numerical': 0.0, 'mean_word_length': 5.8545454545454545, '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}}
Tracking User Mobility to Detect Suspicious Behavior

Popularity of mobile devices is accompanied by widespread security problems, such as MAC address spoofing in wireless networks. We propose a probabilistic approach to temporal anomaly detection using smoothing technique for sparse data. Our technique builds up on the Markov chain, and clustering is presented for reduced storage requirements. Wireless networks suffer from oscillations between locations, which result in weaker statistical models. Our technique identifies such oscillations, resulting in higher accuracy. Experimental results on publicly available wireless network data sets indicate that our technique is more effective than Markov chain to detect anomalies for location, time, or