Yi Xie

TL;DR: A novel anomaly detector based on hidden semi-Markov model is proposed to describe the dynamics of Access Matrix and to detect the attacks of new application-layer DDoS attacks.

TL;DR: An extended hidden semi-Markov model is proposed to describe the browsing behaviors of web surfers and a novel forward algorithm is derived for the online implementation of the model based on the M-algorithm to reduce the computational amount introduced by the model's large state space.

TL;DR: A metric using a fuzzy logic system based on the Sugeno fuzzy inference model for evaluating the quality of the realism of existing intrusion detection system datasets is proposed and a synthetically realistic next generation intrusion detection systems dataset is designed and generated and a preliminary analysis conducted.

TL;DR: The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model, and a performance evaluation of existing machine learning-based streaming spam detection methods is needed.

TL;DR: In this article, two open data sets generated by the cyber security department of the Australian Defence Force Academy (ADFA) are introduced, namely: Australian Defense Force Academy Windows Data Set (AD FA-WD) and Australian Defence Academy Windows data set with a Stealth Attacks Addendum (ADDA-WD: SAA), and statistical analysis results based on these data sets show that, due to the low foot prints of modern attacks and high similarity of normal and attacked data, highly intelligent host based anomaly detection systems (HADS) design will be required.