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This article examines recent research in molecular communications from a telecommunications system design perspective. In particular, it focuses on channel models and stateof-the-art physical layer techniques. The goal is to provide a foundation for higher layer research and motivation for research and development of functional prototypes. In the first part of the article, we focus on the channel and noise model, comparing molecular and radio-wave pathloss formulae. In the second part, the article examines, equipped with the appropriate channel knowledge, the design of appropriate modulation and error correction coding schemes. The third reviews transmitter and receiver side signal processing methods that suppress intersymbol-interference. Taken together, the three parts present a series of physical layer techniques that are necessary to producing reliable and practical molecular communications.

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An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS.