Machine Learning

The security of systems with limited resources is essential for deployment and cannot be compromised by other performance metrics such as throughput. Physically Unclonable Functions (PUFs) present a promising, cost-effective solution for various security applications, including IC counterfeiting and lightweight authentication. PUFs, as security blocks, exploit physical variations to extract intrinsic responses based on applied challenges, with Challenge-Response Pairs (CRPs) uniquely defining each device.

In deep learning, images are utilized due to their rich information content, spatial hierarchies, and translation invariance, rendering them ideal for tasks such as object recognition and classification. The classification of malware using images is an important field for deep learning, especially in cybersecurity. Within this context, the Classified Advanced Persistent Threat Dataset is a thorough collection that has been carefully selected to further this field's study and innovation.

This data repository contains test data and corresponding test code for evaluating the performance of a machine learning model. The dataset includes 950 labeled samples across 7 different classes. The test code provides implementations of several common evaluation metrics, including accuracy, precision, recall, and F1-score. This resource is intended to facilitate the benchmarking and comparison of different machine learning algorithms on a standardized task.

A PCB 130D20 microphone is kept in a stand and facing towards the tool tip to capture the emitted sound. The microphone is connected to the computer through a specially designed signal conditioner. GoldWave software is used to record the captured sound with sampling frequency set to 44100Hz. A series of machining experiments were conducted on a Turning machine(XLTURN from MTAB) with Carbide Insert and aluminium work piece of 38mm diameter. Throughout the experiment a constant feed rate set at 0.5 mm/rotation was uniformly maintained.