Abstract: 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.

Hassan Awad Hassan Al-Sukhni, Azuan Bin Ahmad, Madihah Mohd Saudi and Najwa Hayaati Mohd Alwi, “Cyber Terrorist Detection by using Integration of Krill Herd and Simulated Annealing Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 10(7), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100744

@article{Al-Sukhni2019,
title = {Cyber Terrorist Detection by using Integration of Krill Herd and Simulated Annealing Algorithms},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0100744},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100744},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {7},
author = {Hassan Awad Hassan Al-Sukhni and Azuan Bin Ahmad and Madihah Mohd Saudi and Najwa Hayaati Mohd Alwi},
}