{'fraction_non_alphanumeric': 0.012021857923497269, 'fraction_numerical': 0.0, 'mean_word_length': 4.4523809523809526, '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}}
Transfer Learning to Adapt One Class SVM Detection to Additional Features

In this paper, we use the multi-task learning idea to solve a problem of detection with one class SVM when new sensors are added to the system. The main idea is to adapt the detection system to the upgraded sensor system. To solve that problem, the kernel matrix of multi-task learning model can be divided into two parts, one part is based on the former features and the other part is based on the new features. Typical estimation methods can be used to fill the corresponding new features in the old detection system, and a variable kernel is used for the new features in order to balance the importance of the new features with the number of observed samples. Experimental results show that it can keep the false alarm rate relatively stable and decrease the miss alarm rate rapidly as the number of samples increases in the target task.