Yue Zhang¹, Dengpan Ye², Junjun Gan¹, Zhenyu Li³, Qingfeng Cheng^1,*

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 151-167, 2018, DOI: 10.3970/cmc.2018.02464

Abstract In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method, this paper proposes an image steganography algorithm based on quantization index modulation resisting both scaling attacks and statistical detection. For the spatial image, this paper uses the watermarking algorithm based on quantization index modulation to extract the embedded domain. Then construct the embedding distortion function of the new embedded domain based on S-UNIWARD steganography, and use the minimum distortion coding to realize the embedding of the secret messages.… More >

Jieren Cheng^1,2, Ruomeng Xu^1,*, Xiangyan Tang¹, Victor S. Sheng³, Canting Cai¹

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 95-119, 2018, DOI:10.3970/cmc.2018.055.095

Abstract Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems. We define… More >