Amjed Sid Ahmed^1,*, Rosilah Hassan², Faizan Qamar³, Mazhar Malik¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 247-265, 2021, DOI:10.32604/cmc.2021.014233

Abstract In networking, one major difficulty that nodes suffer from is the need for their addresses to be generated and verified without relying on a third party or public authorized servers. To resolve this issue, the use of self-certifying addresses have become a highly popular and standardized method, of which Cryptographically Generated Addresses (CGA) is a prime example. CGA was primarily designed to deter the theft of IPv6 addresses by binding the generated address to a public key to prove address ownership. Even though the CGA technique is highly effective, this method is still subject to several vulnerabilities with respect to… 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 >