In the dynamic landscape of cyber threats, multistage malware botnets have surfaced as significant threats of concern. These sophisticated threats can exploit Internet of Things (IoT) devices to undertake an array of cyberattacks, ranging from basic infections to complex operations, such as phishing, cryptojacking, and Distributed Denial-of-Service (DDoS) attacks. Existing machine learning solutions are often constrained by their limited generalizability across various data sets and their inability to adapt to the mutable patterns of malware attacks in real world environments, a challenge known as model drift. This limitation highlights the pressing need for adaptive intrusion detection systems (IDSs), capable of adjusting to evolving threat patterns and new or unseen attacks. This article introduces MalBoT-DRL, a robust malware botnet detector using deep reinforcement learning (RL). Designed to detect botnets throughout their entire lifecycle, MalBoT-DRL has better generalizability and offers a resilient solution to model drift. This model integrates damped incremental statistics with an attention reward mechanism, a combination that has not been extensively explored in the literature. This integration enables MalBoT-DRL to dynamically adapt to the ever-changing malware patterns within IoT environments. The performance of MalBoT-DRL has been validated via trace-driven experiments using two representative data sets: 1) MedBIoT and 2) N-BaIoT, resulting in exceptional average detection rates of 99.80% and 99.40% in the early and late detection phases, respectively. To the best of our knowledge, this work introduces one of the first studies to investigate the efficacy of RL in enhancing the generalizability of IDS.

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device’s battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency.