{'fraction_non_alphanumeric': 0.019198664440734557, 'fraction_numerical': 0.007512520868113523, 'mean_word_length': 5.277486910994765, '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}}
Deep learning for collective anomaly detection

Deep learning has been performing well in a number of application domains. Inspired by its popularity in domains such as image processing, speech recognition, etc., in this paper we explore the effectiveness of deep learning and other supervised learning algorithms for collective anomaly detection. Recently, collective anomaly has become popular for denial of service (DoS) attack detection, however, all these approaches are unsupervised in nature and often have high false alarm rate due to being unsupervised. Therefore, to reduce the false alarm rates, we have experimented using the deep learning method which is supervised in nature. Our experimental results on UNSW-NB15 and KDD Cup 1999 datasets show that the deep learning implemented using H2O achieves ≈97% recall for collective anomaly detection. Deep learning outperforms a wide range of unsupervised techniques for collective anomaly detection. The key insight of this paper is to report the efficiency of deep learning for collective anomaly detection. To the best of our knowledge, this paper is the first one to address the collective anomaly detection problem using deep learning.