This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex graph structures, our approach adeptly handles the temporal dynamics inherent to botnet attacks. Key to our approach is the development of a time-stamped multi-edge graph structure that uncovers subtle temporal patterns and hidden relationships in network flows, critical for recognizing botnet behaviors. Moreover, our sequential graph learning framework incorporates time-sequenced edges and multi-edged structures into a two-layered gated graph model, which is optimized with specialized message-passing layers and aggregation functions to address the challenges of time-series traffic data effectively. Our comparative analysis with the state of the art reveals that our sequential gated graph convolutional neural network achieves substantial improvements in detecting IoT botnets. The proposed GGCN model consistently outperforms the conventional model, achieving improvements in accuracy ranging from marginal to substantial—0.01% for BoT IoT and up to 25% for Mirai. Moreover, our empirical analysis underscores the GGCN’s enhanced capabilities, particularly in binary classification tasks, on imbalanced datasets. These findings highlight the model’s ability to effectively navigate and manage the varying complexity and characteristics of IoT security threats across different datasets.

Wireless sensor network (WSN) is a network of a large number of battery-powered tiny sensor nodes wirelessly connected together to facilitate a wide range of monitoring applications. As WSN nodes are energy-constrained microelectronic devices, the primary design objective of WSNs is to minimise energy consumption to prolong the network lifetime. To achieve this goal, a range of cross-layer techniques, particularly focusing on medium access control (MAC) sublayer, is proposed targeting different WSN applications. This paper aims to survey low-power WSN MAC protocols, proposed from 2000 to the present, emphasising some general aspects including the issues addressed, the solutions proposed, design principles, strengths, drawbacks and target applications. With this aim, we mainly classify the MAC protocols into three categories: contention-based protocols, time division multiple access (TDMA)-based protocols and hybrid protocols, where the first category is further subdivided into subclasses. The development trends and potential research challenges are also discussed.

This paper demonstrates the suitability and the practicality of using the advanced open source tools such as the Raspberry Pi and the Node-RED for teaching and learning in the Internet of Things (IOT) subject within a newly created major of Data Engineering in the Faculty of Engineering and IT at University of Technology, Sydney. Understanding and practicing of the Internet of Things largely depend on the high availability of tools, their low cost, and ease of use that can accelerate learning processes. This paper demonstrates relatively uncomplicated practical lab exercises involving the Raspberry Pi hardware, firmware and the Node-RED programming environment that students can execute to stimulate their learning, understanding of the Internet of Things technology and acquire fundamental data engineering skills.

Due to the inevitable presence of random defects, unpredictable grain boundaries in macroscopic samples, stress concentration at clamping points, and unknown load distribution in the investigation of graphene sheets, uncertainties are crucial and challenging issues that require more exploration. The application of the Kriging surrogate model in vibration analysis of graphene sheets is proposed in this study. The Latin hypercube sampling method effectively propagates the uncertainties in geometrical and material properties of the finite element model. The accuracy and convergence of the Kriging surrogate model are confirmed by a comparison with the reported references. The uncertainty analysis for both Zigzag and Armchair graphene sheets are compared and discussed.