Anomaly-based intrusion detection systems identify the computer network behavior which deviates from the statistical model of typical network behavior. Binary classifiers based on supervised machine learning are very accurate at classifying network data into two categories: normal traffic and anomalous activity. Most problems with supervised learning are related to the large amount of data required to train the classifiers. Feature selection can be used to reduce datasets. The goal of feature selection is to select a subset of relevant input features to optimize the evaluation and improve performance of a given classifier. Feature scaling normalizes all features to the same range, preventing the large size of features from affecting classification models or other features. The most commonly used supervised machine learning models, including decision trees, support vector machine, k-nearest neighbors, weighted k-nearest neighbors and feedforward neural network, can all be improved by using feature selection and feature scaling. This paper introduces a new feature scaling technique based on a hyperbolic tangent function and damping strategy of the Levenberg–Marquardt algorithm.

Unintentional electromagnetic (EM) emissions often include information about the data processed by electronic devices. Intrusion based on an unintentional EM emission leaves no evidence of an attacker’s activity, while the data owner is unaware that it has been lost. EM attacks can be performed without physically damaging a device that operates regularly. The most typical intrusion activities involve sensitive data exfiltration using various methods that do not require the physical connection of devices to the computer network or communication channels. This research examines EM emissions from computer monitors, wireless keyboards and mice, printers, scanners, conductors, piezoelectric sensors (PES), and radio frequency identification (RFID) devices. The telecommunication electronics material protected from emanating spurious transmissions (TEMPEST) profiling as a performance engineering of the EM footprint is discussed. This study also presents different TEMPEST standards and highlights their importance concerning unintentional EM radiation.