The internet is ever expanding and online information is booming, making identification and detection of different web information vitally important, particularly those of dark web or Cyber extremists. Webpages with extremist and terrorist content are believed to be main factors in the radicalization and recruitment of disaffected individuals who might be involved in terrorist activities at home or those who fight alongside terrorist groups abroad. In fact, the sheer volume of online data makes it practically impossible for authorities to carry out the individual examination for every webpage, post or conversational thread that might or might not be relevant to terrorism or contain terrorist sympathies. As terrorists exist within every nation and every religion, hence this paper presents a review and systematic analysis of existing webpages on Cyber Terrorists. This include of existing database of Cyber extremists words and existing techniques of web classifier for keywords. Based on this paper systematic analysis, it will be the input for the formation of a new Cyber extremists WorldNet. � 2019 IEEE.

This paper presents a technique to detect cyber terrorists suspected activities over the net by integrating the Krill Herd and Simulated Annealing algorithms. Three new level of categorizations, including low, high, and interleave have been introduced in this paper to optimize the accuracy rate. Two thousand datasets had been used for training and testing with 10-fold cross validation for this research and the simulations were performed using Matlab'. Based on the conducted experiment, this technique produced 73.01% accuracy rate for the interleave level; thus, outperforming the benchmark work. The findings can be used as a guidance and baseline work for other researchers with the same interest in this area. � 2018 The Science and Information (SAI) Organization Limited.