The security defenses that are not comparable to sophisticated adversary tools, let the cloud as an open environment for attacks and intrusions. In this paper, an intelligent protection framework for intrusion detection in a cloud computing environment based on a covariance matrix self-adaptation evolution strategy (CMSA-ES) and multi-criteria decision-making (MCDM) is proposed. The proposed framework constructs an optimal intrusion detector by using CMSA-ES algorithm which adjusts the best parameter set for the attack detector. Moreover, the proposed framework uses a MEREC-VIKOR, a hybrid standardized evaluation technique. MEREC-VIKOR generates the own performance metrics (S, R, and Q) of the proposed framework which is a combination of multi-conflicting criteria. The proposed framework is evaluated for attack detection by using CICIDS 2017 dataset. The experiments show that the proposed framework can detect cloud attacks accurately with low S (utility), R (regret), and Q (integration between S and R). The proposed framework is analyzed with respect to several evolutionary algorithms such as GA, IGASAA, and CMA-ES. The performance analysis demonstrates that the proposed framework that depends on CMSA-ES converges faster than the other evolutionary algorithms such as GA, IGASAA, and CMA-ES. The outcomes also demonstrate that the proposed model is comparable to the state-of-the-art techniques.
Parallel computing of hash functions along with the security requirements have great advantage in order to reduce the time consumption and overhead of the CPU. In this article, a keyed hash function based on farfalle construction and chaotic neural networks (CNNs) is proposed, which generates a hash value with arbitrary (defined by user) length (eg, 256 and 512 bits). The proposed hash function has parallelism merit because it is built over farfalle construction which avoids the dependency between the blocks of a given message. Moreover, the proposed hash function is chaos based (ie, it relies on chaotic maps and CNNs which have non‐periodic behavior). The security analysis shows that the proposed hash function is robust and satisfies the properties of hash algorithms, such as random‐like (non‐periodic) behavior, ideal sensitivity to original message and secret key, one‐way property and optimal diffusion effect. The speed performance of the hash function is also analyzed and compared with a hash function which was built based on sponge construction and CNN, and compared with secure hash algorithm (SHA) variants like SHA‐2 and SHA‐3. The results have shown that the proposed hash function has lower time complexity and higher throughput especially with large size messages. Additionally, the proposed hash function has enough resistance to multiple attacks, such as collision attack, birthday attack, exhaustive key search attack, preimage and second preimage attacks, and meet‐in‐the‐middle attack. These advantages make it ideal to be used as a good collision‐resistant hash function.
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