Intrusion detection and security calculation in industrial cloud storage based on an improved dynamic immune algorithm

Abstract Cloud computing is a new storage and calculation mode, which has been widely used as a tool to store and analyze users’ data. With the development of industrial intelligence and big data, it is also prevalent in industrial field to accelerate production or to discover more knowledge to make better decisions. There are three main properties in cloud computing, namely dynamic and time-variation, large-scale, and change-of-ownership. Therefore, the data security problems are different and complicated than those in conventional networks. The existing methods, such as encrypted storage, security audit, and access control, cannot solve these security problems proactively and efficiently due to their own drawbacks. Commonly, self-samples always have similar features, while non-self-samples are abnormal in their own way. This paper mainly focuses on these two sources of data, one is intrusion detection of external non-self-samples and another is security calculation of self-samples. In this case, an improved dynamic immune algorithm (IDIA) is proposed, which is composed of an improved negative selection algorithm (iNSA) with the way of shift mutation and an improved dynamic clone selection algorithm (iDCS) with random grouping strategy. The former algorithm is used to generate detectors, and the latter is used to update them dynamically and adaptively. On this basis, an automated industrial production system is designed to guarantee the data security that not been accessed by external or other users. Datasets of KDD CUP99 and real industrial production data are collected to evaluate the performance of IDIA. The experiment results show the proposed algorithm can detect the intrusive data samples and identify autologous ones efficaciously.