The present invention relates to an aqueous liquid laundry formulation comprising an ester based laundry ingredient; an effective cleaning amount of proteaseenzyme; an effective cleaning amount of lipaseenzyme; and from 5 to 60 wt % surfactant; wherein at least 70 wt % of the effective cleaning amount of lipaseenzyme is encapsulated and separated from the ester based laundry ingredient and the liquid by a coating which is insoluble in the formulation but which dissolves on dilution with the wash; and wherein the laundry formulation comprises at least 20 wt % water.
The invention provides an industrial control system intrusion attack and clue discovery method based on deep learning. Intrusion detection is part of the initial phase of an industrial control systemsecurity system. Due to the importance of an industrial control system, the decision of the professional of the security system is still the most important. Therefore, the role of simple intrusion alarms in the security system is very limited. An intrusion detection model based on deep learning is difficult to provide more information due to unexplained reasons thereof, which limits the application of a deep learning method in the field of industrial control network intrusion detection. Aiming at the limitation, the invention analyzes the distribution of classification related information and irrelevant information in each layer of a deep learning model from the perspective of information, so as to find the possibility that the hidden layer of a deep learning classification model can be analyzed. Finally, a hierarchical propagation method maps relevant information from the hidden layer to an input layer. Difficult-to-understand information is transformed into understandable information, which helps the professional to lock and process intrusion threats faster.