The electrocardiogram (ECG) signals are basically the combination of sequential electrical impulses generated by tissues in the heart. In the last decades, by using ECG signals, various studies such as heart beat classification, arrhythmia analysis, anomaly detection, and diagnosis of heart-related diseases have been conducted with various neural network (NN), and machine learning (ML) approaches. Regardless of the approach to obtain more accurate results, various preprocessing methods are applied to data. It is observed that performing preprocessing is crucial for the sake of the related analysis. In this paper, focused on studies in the last decade, ECG analysis steps are briefly explained and mentioned approaches are reviewed on their preprocessing methods in detail.
Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating cyber threats in dynamic network environments. However, maintaining high performance over time is challenging due to factors such as initial model limitations, data poisoning attacks, and the influx of low-quality data. Continual learning offers a potential solution, but the risk of performance degradation remains significant. This work proposes a novel approach to enhance the robustness and adaptability of NIDS through the integration of Model Agnostic Meta-Learning (MAML) and Open-Set Recognition (OSR). OSR allows the system to identify and handle previously unseen attack patterns, while MAML facilitates rapid model adaptation to new tasks with minimal additional data. By detecting performance degradation and employing MAML for model repair, our approach aims to maintain and improve NIDS performance over time …
Microservices architecture has been praised as a lightweight, modular and robust alternative to monolithic software in recent years with software containerization bringing parallel ideas to the table against bare metal and even virtual machine based software deployment solutions. While containers provide support for agile software development in the cloud, they suffer from security issues due to their lightweight structure not providing isolation as strong as that of virtual machines. This calls for the development of robust intrusion detection systems (IDS) for containers, taking into account their specific vulnerabilities. Existing IDS for containerized software deployments have mainly used host-based syscall monitoring, with only a few utilizing network-based monitoring without justification for the particular sensor used. In this paper, we aim to close this research gap by empirically evaluating the performances of system …
Network intrusion detection systems (NIDS) are essential in defending online assets from bad actors. Machine learning (ML) has proven to be an effective tool due to its ability to accurately detect complex patterns in network data. Maintaining high model performance, however, is a constant challenge, as malicious actors are non-static and the distribution, frequency and variety of attacks a network experiences constantly changes. Continual Learning (CL) has emerged as a new paradigm for ML-based NIDS to constantly adapt to changing threats and network environments, but faces many key issues, most of all the catastrophic forgetting problem. This paper provides an empirical analysis of different training strategies of CL and model-agnostic metal learning (MAML) to mitigate the effects of catastrophic forgetting within the network security domain. We show that regularization strategies prove ineffective in network …