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Intrusion Prevention Systems (IPS), have long been the first layer of defenseagainst malicious attacks. Most sensitive systems employ instances of them(e.g. Firewalls) to secure the network perimeter and filter out attacks orunwanted traffic. A firewall, similar to classifiers, has a boundary to decidewhich traffic sample is normal and which one is not. This boundary is definedby configuration and is managed by a set of rules which occasionally might alsofilter normal traffic by mistake. However, for some applications, anyinterruption of the normal operation is not tolerable e.g. in power plants,water distribution systems, gas or oil pipelines, etc. In this paper, we designa learning firewall that receives labelled samples and configures itselfautomatically by writing preventive rules in a conservative way that avoidsfalse alarms. We design a new family of classifiers, called$\mathfrak{z}$-classifiers, that unlike the traditional ones which merelytarget accuracy, rely on zero false-positive as the metric for decision making.First, we analytically show why naive modification of current classifiers likeSVM does not yield acceptable results and then, propose a generic iterativealgorithm to accomplish this goal. We use the proposed classifier with CART atits heart to build a firewall for a Power Grid Monitoring System. To furtherevaluate the algorithm, we additionally test it on KDD CUP'99 dataset. Theresults confirm the effectiveness of our approach.

A Machine Learning-based Approach to Build Zero False-Positive IPSs for Industrial IoT and CPS with a Case Study on Power Grids Security