A Web Attack Detection Technology Based on Bag of Words and Hidden Markov Model

An effective web attack detection method appears as a natural solution to protect web security, as they help to protect web applications. The traditional method of detecting web attacks is to encode the attack features manually into corresponding rules for detection. With the diversification of web attack methods, the demerits of the traditional methods have become increasingly noticeable. With the rapid development of high-performance computing and expansion of data volume, machine learning methods can obtain more efficient and accurate web attacks detection. In this paper, we exploit a bag of words based (BOW) model to extract features and further efficiently detect web attacks with hidden Markov algorithms. The experimental results show that, compared with the previous experiments of N-gram extraction feature algorithm, BOW has higher detection rate and lower false alarm rate with a lower cost. Finally, satisfactory results in the real environment are also achieved.