Abstract: A method for generating a cross-site scripting attack is provided. An attack string sample is analyzed for obtaining a token sequence. A string word corresponding to each token is used to replace the token for generating a cross-site scripting attack string. Accordingly, a large number of cross-site scripting attacks are generated automatically, so as to execute a penetration test for a website.

Abstract: A method for generating a cross-site scripting attack is provided. An attack string sample is analyzed for obtaining a token sequence. A string word corresponding to each token is used to replace the token for generating a cross-site scripting attack string. Accordingly, a large number of cross-site scripting attacks are generated automatically, so as to execute a penetration test for a website.

Abstract: A botnet detection system is provided. A bursty feature extractor receives an Internet Relay Chat (IRC) packet value from a detection object network, and determines a bursty feature accordingly. A Hybrid Hidden Markov Model (HHMM) parameter estimator determines probability parameters for a Hybrid Hidden Markov Model according to the bursty feature. A traffic profile generator establishes a probability sequential model for the Hybrid Hidden Markov Model according to the probability parameters and pre-defined network traffic categories. A dubious state detector determines a traffic state corresponding to a network relaying the IRC packet in response to reception of a new IRC packet, determines whether the IRC packet flow of the object network is dubious by applying the bursty feature to the probability sequential model for the Hybrid Hidden Markov Model, and generates a warning signal when the IRC packet flow is regarded as having a dubious traffic state.

Abstract: A botnet detection system is provided. A bursty feature extractor receives an Internet Relay Chat (IRC) packet value from a detection object network, and determines a bursty feature accordingly. A Hybrid Hidden Markov Model (HHMM) parameter estimator determines probability parameters for a Hybrid Hidden Markov Model according to the bursty feature. A traffic profile generator establishes a probability sequential model for the Hybrid Hidden Markov Model according to the probability parameters and pre-defined network traffic categories. A dubious state detector determines a traffic state corresponding to a network relaying the IRC packet in response to reception of a new IRC packet, determines whether the IRC packet flow of the object network is dubious by applying the bursty feature to the probability sequential model for the Hybrid Hidden Markov Model, and generates a warning signal when the IRC packet flow is regarded as having a dubious traffic state.