Critical infrastructure protection based on memory-augmented meta-learning framework

Abstract

Critical infrastructures are related to systems which are essential for sustaining the important functions of a society. Their potential failures can cause serious problems not only to the population and economy but also to national security as well. The importance of these infrastructures calls for measures toward their security and protection. The aim was the reduction of risk related to natural disasters, terrorist acts, and cyberthreats. Traditional security systems, even those that employ intelligent algorithms, fail to prevent advanced zero-day attacks as they require constant training. This research proposes a novel meta-learning architecture that considers the neural turing machines as the approach upon which the model is founded. The introduced model allows for the memorization of useful data from past processes, by integrating external storage memory. Moreover, it facilitates the rapid integration of new information without the need for retraining. In particular, the proposed novel architecture is called memory-augmented neural network (M-ANN) whose core is a sophisticated, very fast, and highly efficient extreme learning machine. The M-ANN is assisted by a series of original modifications, related to fine-tuning of training, to memory retrieval mechanisms, to addressing techniques, and to ways of attention-weight allocation to memory vectors. The efficiency of the proposed system has been successfully tested using an extremely complex scenario for the protection of critical infrastructures. According to the testing scenario, memory could quickly encode and record information about new types of attacks, while any stored representation from previous experience was easily and consistently accessible, to maximize the detection efficiency.