{'fraction_non_alphanumeric': 0.0324773413897281, 'fraction_numerical': 0.00906344410876133, 'mean_word_length': 4.915178571428571, '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}}
Using data mining approaches to identify voice over IP spam

With its rapid growth in popularity and attendant personal and commercial use of voice over Internet protocol (VoIP), spam over VoIP has emerged as a significant problem. In this paper, we first applied genetic algorithms to weight predetermined features of VoIP calls. Based on the weighting technique, we then proposed a mechanism for the online prevention of VoIP spam by the k‐nearest neighbor classification. Differing from the traditional k‐nearest neighbor algorithm, this paper used genetic algorithms to weight features to reflect the different degree of importance of each feature. VoIP is a real‐time communication service, as compared to off‐line e‐mail, and VoIP spam is also known as Spam over Internet telephony (SPIT). The system proposed in this paper has been named PSPIT that denotes Prevention of SPIT. Once spam is identified, the proposed PSPIT immediately updates the built‐in blacklist on the Session Initiation Protocol (SIP) server to block that specific user from sending SPIT again. The n‐fold cross‐validation was applied to evaluate PSPIT for unknown SPIT detection; in the case of n = 2, the best overall accuracy was 94.7%, whereas in the case of n = 4, the best overall accuracy was 93.1%. Copyright © 2013 John Wiley & Sons, Ltd.