This work addresses the critical need for the early detection of breast cancer, a significant health concern worldwide. Using a combination of advanced deep learning and machine learning techniques, we offer a comprehensive solution to enhance breast cancer detection accuracy. By leveraging state-of-the-art convolutional neural networks (CNNs) like GoogLeNet, AlexNet, and ResNet18, alongside traditional classifiers such as k-nearest neighbors (KNN) and support vector machine (SVM), we ensure robust prediction capabilities. Our preprocessing methods significantly improve input data quality, leading to promising detection accuracies. For instance, ResNet-18 achieved impressive results, outperforming other models. Furthermore, our integration of these algorithms into a user-friendly MATLAB R2024b application ensures easy access for medical professionals, facilitating timely diagnosis and treatment. This work represents a vital step towards more effective breast cancer diagnosis, underscoring the importance of early intervention for improved patient outcomes.

The proliferation of Internet of Things (IoT) and Edge devices in critical applications has exposed them to sophisticated Hardware Intrinsic Attacks (HIAs), where hidden trojans can be activated to trigger malicious behavior. Existing intrusion detection systems (IDSs) are largely dependent on network traffic analysis, which struggles to detect hardware-level compromises and evolving threats that mimic normal communication patterns. To overcome these limitations, this paper introduces a novel IDS that leverages hardware-based current profiling for robust intrusion detection. The core of our approach is the monitoring of devices’ current consumption patterns to identify anomalies indicative of an attack, a method that is fundamentally different from and complementary to traditional network-based detection. We demonstrate the efficacy of this approach on a real-world testbed comprising ESP32 microcontrollers (IoT nodes) and a Raspberry Pi 5 (Edge device). The system was evaluated against four critical attack types: Covert Channel Attack (CCA), Power Depletion Attack (PDA), Denial-of-Service (DoS), and Man-in-the-Middle Attack (MIMA). By integrating Artificial Intelligence (AI) through customized Machine Learning (ML) and Deep Learning (DL) models that are trained on over 1.2 million records of current/voltage consumption collected dataset, our IDS achieves a superior multi-class classification with Accuracy of 99.78%, Precision of 99.61%, Recall of 99.61%, and F1-score of 99.60%. These results significantly outperform existing state-of-the-art methods. The findings underscore that physical layer current draw analysis provides a highly reliable and adaptive security mechanism, offering a powerful solution for securing IoT ecosystems against embedded hardware threats and sophisticated cyberattacks.

This work addresses the critical need for early detection of breast cancer, a significant health concern worldwide. Through a combination of advanced deep learning and machine learning techniques, we offer a comprehensive solution to enhance breast cancer detection accuracy. By leveraging state-of-the-art convolutional neural networks (CNNs) like GoogLeNet, AlexNet, and ResNet18, alongside traditional classifiers such as k-nearest neighbors (KNN) and support vector machine (SVM), we ensure robust prediction capabilities. Our preprocessing methods significantly improve input data quality, leading to promising detection accuracies. For instance, ResNet-18 achieved impressive results, outperforming other models. Furthermore, our integration of these algorithms into a user-friendly MATLAB application ensures easy access for medical professionals, facilitating timely diagnosis and treatment. This work represents a vital step towards more effective breast cancer diagnosis, underscoring the importance of early intervention for improved patient outcomes.